CHEE 434/821 Process Control II Some Review...
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CHEE 434/821CHEE 434/821Process Control IIProcess Control II
Some Review MaterialSome Review Material
Winter 2006Winter 2006
Instructor:Instructor:M.GuayM.Guay
TA:TA:V. V. AdetolaAdetola
IntroductionIntroduction
In the chemical industry,
the design of a control system is essential to ensure:
Good Process OperationGood Process OperationProcess SafetyProcess SafetyProduct QualityProduct QualityMinimization of Environmental ImpactMinimization of Environmental Impact
2
IntroductionIntroduction
What is the purpose of a control system?What is the purpose of a control system?“To maintain important process characteristics at
desired targets despite the effects of external perturbations.”
Control
Plant ProcessingProcessingobjectivesobjectives
SafetyMake $$$
Environment...
PerturbationsPerturbations
MarketEconomyClimateUpsets...
IntroductionIntroduction
Plant
ControlWhat constitutes a control system?
Combination of process sensors, actuators and computer systems designed and tuned to orchestratesafe and profitableoperation.
3
IntroductionIntroduction
Process Dynamics:
Study of the transient behavior of processes
Process Control
the use of process dynamics for the improvement of process operation and performance
or
the use of process dynamics to alleviate the effect of undesirable (unstable) process behaviors
IntroductionIntroduction
What do we mean by process?
A process, P, is an operation that takes an INPUT or a DISTURBANCE and gives an OUTPUT
INPUT: (u) Something that you can manipulateDISTURBANCE: (d) Something that comes as a result of some outside phenomenonOUTPUT: (y) An observable quantity that we want to
regulate
u
d
yP
Information Flow
4
ExamplesExamples
Stirred tank heater
MTin, w
Q
T, w
TinwQ
TProcess
InputsInputs Output
ExamplesExamples
The speed of an automobile
Force ofEngine
Friction
Inputs Output
Friction
EngineSpeedProcess
5
ExamplesExamples
e.g. Landing on Mars
ExamplesExamples
e.g. Millirobotics
LaparoscopicLaparoscopic ManipulatorsManipulators
6
IntroductionIntroduction
Process
A process, P, is an operation that takes an INPUT or a DISTURBANCE and gives an OUTPUT
INPUT: (u) Something that you can manipulateDISTURBANCE: (d) Something that comes as a result of some outside phenomenonOUTPUT: (y) An observable quantity that we want to
regulate
u
d
yP
Information Flow
ControlControl
What is control?
To regulate of a process output despite the effect of disturbances
Driving a carControlling the temperature of a chemical reactorReducing vibrations in a flexible structure
To stabilize unstable processesRiding a bikeFlight of an airplaneOperation of a nuclear plant
7
Benefits of ControlBenefits of Control
Economic BenefitsQuality (waste reduction)Variance reduction (consistency)Savings in energy, materials, manpower
Operability, safety (stability)PerformanceEfficiencyAccuracy
roboticsReliabilityStabilizability
bicycleaircraftnuclear reactor
ControlControl
A controller is a system designed to regulate a given process
Process typically obeys physical and chemical conservation lawsController obeys laws of mathematics and logic (sometimes intelligent)
e.g. - Riding a bike (human controller)- Driving a car- Automatic control (computer programmed to control)
Process
Controller
What is a controller?
8
Block representationsBlock representations
Block diagrams are models of the physical systems
Process
System Physical Boundary Transfer of
fundamental quantities
Mass, Energy and Momentum
Input variables Output variables
Physical
OperationAbstract
ControlControl
A controlled process is a system which is comprised of two interacting systems:
e.g. Most controlled systems are feedback controlled systems
The controller is designed to provide regulation of process outputs in the presence of disturbances
Process
Controller
OutputsDisturbances
Action Observation
monitorintervene
9
IntroductionIntroduction
What is required for the development of a What is required for the development of a control systemcontrol system??
1. The Plant (e.g. SPP of Nylon)1. The Plant (e.g. SPP of Nylon)
Water
Steam
Gas Make-up
Vent
Nylon
Blower
Dehumidifier
Reheater
ReliefPot
Heater
IntroductionIntroduction
What is required?What is required?
1. Process UnderstandingRequired measurementsRequired measurementsRequired actuatorsRequired actuatorsUnderstand design limitationsUnderstand design limitations
2. Process InstrumentationAppropriate sensor and actuator selectionAppropriate sensor and actuator selectionIntegration in control systemIntegration in control systemCommunication and computer architectureCommunication and computer architecture
3. Process ControlAppropriate control strategyAppropriate control strategy
10
ExampleExample
Cruise Control
Controller
FrictionProcess Speed
Engine
Human or Computer
Classical ControlClassical Control
Control is meant to provide regulation of process outputs about a reference, r, despite inherent disturbances
The deviation of the plant output, e=(r-y), from its intended reference is used to make appropriate adjustments in the plant input, u
ProcessController
Classical Feedback Control System
d
yur e+-
11
ControlControl
Process is a combination of sensors and actuators
Controller is a computer (or operator) that performs the required manipulations
e.g. Classical feedback control loop
yr eAC P
M
dComputer Actuator
Process
Sensor
-+
ExamplesExamples
Driving an automobile
yeAC P
M
Driver
Automobile-
+Steering
r
Visual and tactile measurement
Desired trajectoryr
Actual trajectoryy
12
ExamplesExamples
Stirred-Tank Heater
Tin, w
Q T, wHeater
TCThermocouple
yeAC P
M
Controller
Tank-
+Heater
Thermocouple
Tin, w
TR
ExamplesExamples
Measure T, adjust Q
Controller: Q=K(TR-T)+Qnominal
where Qnominal=wC(T-Tin)
Q: Is this positive or negative feedback?
Tin, w
TeAC P
M
Controller
Tank-
+Heater
Thermocouple
Feedback control
TR
13
ExamplesExamples
Measure Ti, adjust Q
A PC
MTi
Qi
ΔQ
Q
+
+
Feedforward Control
Control NomenclatureControl Nomenclature
Identification of all process variables
Inputs (affect process) Outputs (result of process)
Inputs
Disturbance variablesVariables affecting process that are due to external forces
Manipulated variablesThings that we can directly affect
14
Control NomenclatureControl Nomenclature
OutputsMeasured
speed of a carUnmeasured
acceleration of a carControl variables
important observable quantities that we want to regulatecan be measured or unmeasured
Controller
Manipulated
Disturbances
Process Control
Other
ExampleExample
T
L
T
wi, Ti
wc, Tci
wc, Tco
wo, To
h
Variables
• wi, wo: Tank inlet and outlet mass flows• Ti, To: Tank inlet and outlet temperatures• wc: Cooling jacket mass flow• Pc: Position of cooling jacket inlet valve• Po: Position of tank outlet valve• Tci, Tco: Cooling jacket inlet and outlet
temperatures• h: Tank liquid level
Po
Pc
15
ExampleExample
Variables Inputs Outputs
Disturbances Manipulated Measured Unmeasured ControlwiTiTciwchwoToPcPo
Task: Classify the variables
Process Control and ModelingProcess Control and Modeling
In designing a controller, we mustDefine control objectivesDevelop a process modelDesign controller based on modelTest through simulationImplement to real processTune and monitor
Model
Controlleryur e
d
Process
Design
Implementation
16
Control System DevelopmentControl System Development
Define Objectives
Develop a processmodel
Design controller based on model
Test bySimulation
Implement and Tune
MonitorPerformance
Control development is usually carried out following theseimportant steps
Often an iterative process, based on performance we may decide to retune, redesign or remodel a given control system
Control System Development Control System Development
Objectives“What are we trying to control?”
Process modeling“What do we need?”
Mechanistic and/or empirical
Controller design“How do we use the knowledge of process behavior to reach our process control objectives?”What variables should we measure?What variables should we control?What are the best manipulated variables?What is the best controller structure?
17
Control System DevelopmentControl System Development
Implement and tune the controlled processTest by simulationincorporate control strategy to the process hardwaretheory rarely transcends to realitytune and re-tune
Monitor performanceperiodic retuning and redesign is often necessary based on sensitivity of process or market demandsstatistical methods can be used to monitor performance
Process ModelingProcess Modeling
Motivation:
Develop understanding of processa mathematical hypothesis of process mechanisms
Match observed process behavioruseful in design, optimization and control of process
Control:
Interested in description of process dynamicsDynamic model is used to predict how process responds to given inputTells us how to react
18
Process ModelingProcess Modeling
What kind of model do we need?
Dynamic vs. Steady-state
Steady-stateVariables not a function of timeuseful for design calculation
DynamicVariables are a function of timeControl requires dynamic model
Process ModelingProcess Modeling
What kind of model do we need?
Experimental vs Theoretical
ExperimentalDerived from tests performed on actual processSimpler model formsEasier to manipulate
TheoreticalApplication of fundamental laws of physics and chemistrymore complex but provides understandingRequired in design stages
19
Process ModelingProcess Modeling
Dynamic vs. Steady-state
Step change in input to observeStarting at steady-state, we made a step changeThe system oscillates and finds a new steady-stateDynamics describe the transitory behavior
0 50 100 150 200 250 30040
45
50
55
60
65
Out
put
Time
Steady-State 1
Steady-State 2
Process ModelingProcess Modeling
Empirical vs. Mechanistic modelsEmpirical Models
only local representation of the process(no extrapolation)model only as good as the data
Mechanistic ModelsRely on our understanding of a processDerived from first principlesObserving laws of conservation of
MassEnergyMomentum
Useful for simulation and exploration of new operating conditionsMay contain unknown constants that must be estimated
20
Process ModelingProcess Modeling
Empirical vs Mechanistic modelsEmpirical models
do not rely on underlying mechanisms Fit specific function to match processMathematical French curve
0 50 100 150 200 250 3000.4
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
Out
put
Time
Process ModelingProcess Modeling
Linear vs NonlinearLinear
basis for most industrial controlsimpler model form, easy to identifyeasy to design controllerpoor prediction, adequate control
Nonlinearrealitymore complex and difficult to identifyneed state-of-the-art controller design techniques to do the jobbetter prediction and control
In existing processes, we really onDynamic models obtained from experimentsUsually of an empirical natureLinear
In new applications (or difficult problems)Focus on mechanistic modelingDynamic models derived from theoryNonlinear
21
Process ModelingProcess Modeling
General modeling procedure
Identify modeling objectivesend use of model (e.g. control)
Identify fundamental quantities of interestMass, Energy and/or Momentum
Identify boundaries
Apply fundamental physical and chemical lawsMass, Energy and/or Momentum balances
Make appropriate assumptions (Simplify)ideality (e.g. isothermal, adiabatic, ideal gas, no friction, incompressible flow, etc,…)
Write down energy, mass and momentum balances (develop the model equations)
Process ModelingProcess Modeling
Modeling procedure
Check model consistencydo we have more unknowns than equations
Determine unknown constantse.g. friction coefficients, fluid density and viscosity
Solve model equationstypically nonlinear ordinary (or partial) differential equationsinitial value problems
Check the validity of the modelcompare to process behavior
22
Process ModelingProcess Modeling
For control applications:
Modeling objectives is to describe process dynamics based on the laws of conservation of mass, energy and momentum
The balance equation
1. Mass Balance (Stirred tank)2. Energy Balance (Stirred tank heater)3. Momentum Balance (Car speed)
Rate of Accumulationof fundamental quantity
FlowIn
FlowOut
Rate ofProduction
= -
+
Process ModelingProcess Modeling
Application of a mass balanceHolding Tank
Modeling objective: Control of tank level
Fundamental quantity: Mass
Assumptions: Incompressible flow
h
F
Fin
23
Process ModelingProcess Modeling
Total mass in system = Total mass in system = ρV = ρAhFlow in = Flow in = ρFin
Flow out = Flow out = ρF
Total mass at time t = ρAh(t)Total mass at time Total mass at time t+Δt = = ρAh(t+Δt)Accumulation Accumulation
ρAh(t+Δt) − ρAh(t) = = ΔΔt(ρFin-ρF ),
ρ ρA dhdt
F Fin= −( ).
lim ( ) ( ) ( ),Δ
ΔΔt in
Ah t t Ah tt
F F→
+ − = −0
ρ ρ ρ
ρ ρ ρAh t t Ah tt
F Fin( ) ( ) ( ),+ − = −Δ
Δ
Process ModelingProcess Modeling
Model consistencyModel consistency“Can we solve this equation?”
Variables: h, ρ, Fin, F, A 5
Constants: ρ, A 2
Inputs: Fin, F 2
Unknowns: h 1
Equations 1
Degrees of freedom 0
There exists a solution for each value of the inputs Fin, F
24
Process ModelingProcess Modeling
Solve equationSolve equation
Specify initial conditions h(0)=h0 and integrate
h t h F FA
dint( ) ( ) ( ) ( )= +
−⎛⎝⎜
⎞⎠⎟∫0 0
τ τ τ
0 10 20 30 40 50 60 70 80 90 1000
0.5
1
1.5
2flo
w F
Fin
0 10 20 30 40 50 60 70 80 90 1000.9
1
1.1
1.2
1.3
h
Process ModelingProcess Modeling
Energy balance
Objective: Control tank temperatureFundamental quantity: EnergyAssumptions: Incompressible flow
Constant hold-up
MTin, w
Q
T, w
25
Process ModelingProcess Modeling
Under constant hold-up and constant mean pressure (small pressure changes)
Balance equation can be written in terms of the enthalpies of the various streams
Typically work done on system by external forces is negligible
Assume that the heat capacities are constant such that
dHdt
H H Qin out= − +& &
dHdt
H H Q Win out s= − + +& &
H C V T TP ref= −ρ ( )
& ( )H C w T Tout P ref= −ρ
& ( )H C w T Tin P in ref= −ρ
Process ModelingProcess Modeling
After substitution,
Since Tref is fixed and we assume constant ρ,Cp
Divide by ρ CpV
dTdt
wV
T T QC Vin
P
= − +( )ρ
ρ ρ ρC Vd T T
dtC w T T C w T T QP
refP in ref P ref
( )( ) ( )
−= − − − +
d C V T Tdt
C w T T C w T T QP refP in ref P ref
( ( ))( ) ( )
ρρ ρ
−= − − − +
26
Process ModelingProcess Modeling
Resulting equation:
Model ConsistencyModel Consistency
Variables: T, F, V, Tin, Q, Cp, ρ 7
Constants: V, Cp, ρ 3Inputs: F, Tin, Q 3Unknown: T 1
Equations 1
There exists a unique solution
dTdt
FV
T T QVCin
P= − +( )
ρ
Process ModelingProcess Modeling
Assume F is fixed
where τ=V/F is the tank residence time (or time constant)
If F changes with time then the differential equation does not have a closed form solution.
Product F(t)T(t) makes this differential equation nonlinear.
Solution will need numerical integration.
T t T e e T QC V dt t in
p
t( ) ( ) ( ( ) ( ) )/ ( )/= + +∫− −0
0
τ ς τ ζτ
ζρ ζ
dT tdt
F tV
T t T t Q tVCin
P
( ) ( ) ( ( ) ( )) ( )= − +
ρ
27
Process ModelingProcess Modeling
A simple momentum balance
Objective:Objective: Control car speedQuantityQuantity: MomentumAssumptionAssumption: Friction proportional to speed
MomentumMomentumOutOut==
Sum of forces Sum of forces acting on systemacting on system
MomentumMomentumInIn
Rate of Rate of AccumulationAccumulation --
++
Force ofEngine (u)
Friction
Speed (v)
Process ModelingProcess Modeling
Forces are: Force of the engine = uFriction = bv
Balance:
Total momentum = Mv
Model consistencyModel consistency
Variables: M, v, b, u 4Constants: M, b 2Inputs: u 1Unknowns v 1
d Mv tdt
M dv tdt
u t bv t( ( )) ( ) ( ) ( )= = −
28
Process ModelingProcess Modeling
Gravity tank
Objectives:Objectives: height of liquid in tankFundamental quantity:Fundamental quantity: Mass, momentumAssumptions:Assumptions:
Outlet flow is driven by head of liquid in the tankIncompressible flowPlug flow in outlet pipeTurbulent flow
h
L
F
Fo
Process ModelingProcess Modeling
From mass and momentum balances,
A system of simultaneous ordinary differential equations results
Linear or nonlinear?
dhdt
FA
A vA
dvdt
hgL
K vA
o P
F
P
= −
= −2
ρ
29
Process ModelingProcess Modeling
Model consistencyModel consistency
Variables Fo, A, Ap, v, h, g, L, KF, ρ 9
Constants A, Ap, g, L, KF, ρ 6
Inputs Fo 1
Unknowns h, v 2
Equations 2
Model is consistent
Solution of Solution of ODEsODEs
Mechanistic modeling results in nonlinear sets of ordinary differential equations
Solution requires numerical integration
To get solution, we must first:specify all constants (densities, heat capacities, etc, …)specify all initial conditionsspecify types of perturbations of the input variables
For the heated stirred tank,
specify ρ, CP, and Vspecify T(0)specify Q(t) and F(t)
dTdt
FV
T T QVCin
P= − +( )
ρ
30
Input SpecificationsInput Specifications
Study of control system dynamicsObserve the time response of a process output in response to input changes
Focus on specific inputs
1. Step input signals2. Ramp input signals3. Pulse and impulse signals4. Sinusoidal signals5. Random (noisy) signals
Common Input Signals
1. Step Input Signal: a sustained instantaneous change
e.g. Unit step input introduced at time 1
0 1 2 3 4 5 6 7 8 9 100
0.5
1
1.5
Inpu
t
Time
0 1 2 3 4 5 6 7 8 9 100
0.2
0.4
0.6
0.8
1
1.2
1.4
31
Common Input SignalsCommon Input Signals
2. Ramp Input: A sustained constant rate of change
e.g.
0 1 2 3 4 5 6 7 8 9 100
1
2
3
4
5
6
7
8
9
Inpu
t
Time
0 1 2 3 4 5 6 7 8 9 10-1
0
1
2
3
4
5
6
7
8
Out
put
Time
Common Input SignalsCommon Input Signals
3. Pulse: An instantaneous temporary change
e.g. Fast pulse (unit impulse)
0 1 2 3 4 5 6 7 8 9 100
10
20
30
40
50
60
70
80
90
100
Inpu
t
Time
0 1 2 3 4 5 6 7 8 9 10-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
Time
Out
put
32
Common Input SignalsCommon Input Signals
3. Pulses:
e.g. Rectangular Pulse
0 1 2 3 4 5 6 7 8 9 100
0.5
1
1.5
Inpu
t
Time
0 1 2 3 4 5 6 7 8 9 10-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Out
put
Time
Common Input SignalsCommon Input Signals
4. Sinusoidal input
0 5 10 15 20 25 30-1.5
-1
-0.5
0
0.5
1
1.5
Inpu
t
Time
0 5 10 15 20 25 30-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Out
put
Time
33
Common Input SignalsCommon Input Signals
5. Random Input
0 5 10 15 20 25 30-1.5
-1
-0.5
0
0.5
1
1.5
Inpu
t
Time
0 5 10 15 20 25 30-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Out
put
Time
Solution of Solution of ODEsODEs using using LaplaceLaplaceTransformsTransforms
Process Dynamics and ControlProcess Dynamics and Control
34
Linear Linear ODEsODEs
For linear ODEs, we can solve without integrating by using Laplace transforms
Integrate out time and transform to Laplace domain
F s f t f t e dtst
t( ) [ ( )] ( )= ℑ = ∫ −
=
∞
0
MultiplicationMultiplication
Y(s) = G(s)U(s)Y(s) = G(s)U(s)
IntegrationIntegration
dy tdt
ay t bu t
y c
( ) ( ) ( )
( )
= +
=0
Common TransformsCommon Transforms
Useful Useful LaplaceLaplace TransformsTransforms1. Exponential1. Exponential
2. Cosine 2. Cosine
f t e bt( ) = −
ℑ = = ∫∫
ℑ = −+
⎤
⎦⎥⎥
=+
− − − − +∞∞
−− + ∞
[ ]
[ ]
( )
( )
e e e dt e dt
e es b s b
bt bt st s b t
bts b t
00
0
1
f t t e ej t j t( ) cos( )= =
+−ω
ω ω
2
ℑ = ∫ + ∫⎧⎨⎩
⎫⎬⎭
=−
++
⎧⎨⎩
⎫⎬⎭
=+
− −∞
− +∞
[cos( )] ( ) ( )ω
ω ω ω
ω ωt e dt e dt
s j s js
s
s j t s j t12
12
1 10 0
2 2
35
Common TransformsCommon Transforms
Useful Useful LaplaceLaplace TransformsTransforms3. Sine3. Sine
f t t e ej
j t j t( ) sin( )= =
− −ω
ω ω
2
ℑ = ∫ − ∫⎧⎨⎩
⎫⎬⎭
=−
−+
⎧⎨⎩
⎫⎬⎭
=+
− −∞
− +∞
[sin( )] ( ) ( )ω
ω ωω
ω
ω ωtj
e dt e dt
j s j s j s
s j t s j t12
12
1 10 0
2 2
Common TransformsCommon Transforms
Operators1. Derivative of a function f(t)
2. Integral of a function f(t)
ℑ ∫⎡
⎣⎢
⎤
⎦⎥ = ∫∫ =−
∞f d e f d dt F s
s
t st t( ) ( ( ) ) ( )τ τ τ τ
0 00
] ]
du df
v e
dfdt
uv udv f t e sf t e dt
dfdt
s f t e dt f sF s f
st
st st
st
=
=
ℑ = − ∫ = − −∫
ℑ = ∫ − = −
−
∞ ∞− ∞ −
∞
−∞
[ ] ( ) ( ( ) )
[ ] ( ) ( ) ( ) ( )
00 0 0
00 0
df tdt( )
36
Common TransformsCommon Transforms
g tt
f t t( )
( )=
<− ≥
⎧⎨⎩
0 ττ τ
[ ]ℑ = + −∫∫ − −∞
g t e dt e f t dtst st( ) ( ) ( )00
ττ
τ
[ ]ℑ = −g t e F ss( ) ( )τ
OperatorsOperators3. Delayed function f(t-τ)
Common TransformsCommon Transforms
Input Signals1. Constant1. Constant
2. Step 2. Step
3. Ramp function3. Ramp function
f tt
a t( ) =
<≥
⎧⎨⎩
0 00
ℑ = = − =∫ − − ∞∞
[ ( )] ( )f t ae dt aes
as
st st0
0
ℑ = = − =∫ − − ∞∞
[ ] ( )a ae dt aes
as
st st0
0
f t a( ) =
f tt
at t( ) =
<≥
⎧⎨⎩
0 00
[ ]ℑ = = −⎤
⎦⎥ + =−
∞ − ∞ −∞
∫ ∫f t ate dt e ats
aes
dt as
stst st
( )0 0 0
2
37
Common TransformsCommon Transforms
Input SignalsInput Signals4. Rectangular Pulse4. Rectangular Pulse
5. 5. Unit impulse
f tt
a t tt t
w
w
( ) =<
≤ <≥
⎧
⎨⎪
⎩⎪
0 00
0
[ ]ℑ = ∫ = −− −f t ae dt as
estt
t sww( ) ( )
01
[ ]ℑ = −→
−δ ( ) lim ( )tt s
et w
t s
w
w
0
1 1
[ ]ℑ = =→
−δ ( ) limt se
st
t s
w
w
01
Laplace Laplace TransformsTransforms
Final Value Theorem
Limitations:Limitations:
Initial Value Theorem
[ ] [ ]lim ( ) lim ( )t s
y t sY s→∞ →
=0
[ ]y t C
sY s s ss
( ) ,lim ( ) Re( )
∈
∀ ≥→
1
00 exists
[ ]y sY ss
( ) lim ( )0 =→∞
38
Solution of Solution of ODEsODEs
We can continue taking Laplace transforms and generate a catalogue of Laplace domain functions. See SEM Table 3.1
The final aim is the solution of ordinary differential equations.
ExampleExampleUsing Laplace Transform, solve
Result
5 4 2 0 1dydt
y y+ = =, ( )
y t e t( ) . . .= + −05 05 0 8
Solution of Linear Solution of Linear ODEsODEs
Stirred-tank heater (with constant F)
taking Laplace
To get back to time domain, we mustSpecify Laplace domain functions Q(s), Tin(s)Take Inverse Laplace
dTdt
FV
T T QVC
T T
inP
= − +
=
( )
( )ρ
0 0
[ ] [ ] [ ]VF
dTdt
T t T tFC
Q t
T s T T s T s K Q s
T ss
Ts
T s Ks
Q s
inP
in P
inP
ℑ⎡⎣⎢
⎤⎦⎥
= ℑ − ℑ + ℑ
− = − +
=+
++
++
( ) ( ) ( )
( ( ) ( )) ( ) ( ) ( )
( ) ( ) ( ) ( )
1
0
10 1
1 1
ρτ
ττ τ τ
39
Linear Linear ODEsODEs
Notes:Notes:The expression
describes the dynamic behavior of the process explicitly
The Laplace domain functions multiplying T(0),Tin(s) and Q(s) are transfer functions
11
1
1
τ
ττ
τ
sKs
s
P
+
+
+
T ss
Ts
T s Ks
Q sinP( ) ( ) ( ) ( )=
++
++
+τ
τ τ τ10 1
1 1
+
++
Tin(s)
Q(s)
T(0)
T(s)
Laplace Laplace TransformTransform
Assume Tin(t) = sin(ωt) then the transfer function gives directly
Cannot invert explicitly, but if we can find A and B such that
we can invert using tables.
Need Partial Fraction Expansion to deal withsuch functions
11 12 2τ
ωω τs
T ss s
in+=
+ +( )
( )( )
As
Bs s s2 2 2 21 1+
++
=+ +ω τ
ωω τ( )( )
40
Linear Linear ODEsODEs
We deal with rational functions of the form r(s)=p(s)/q(s) where degree of q > degree of p
q(s) is called the characteristic polynomial of the function r(s)
Theorem:Every polynomial q(s) with real coefficients can be factored into the product of only two types of factors
powers of linear terms (x-a)n and/orpowers of irreducible quadratic terms, (x2+bx+c)m
Partial fraction ExpansionsPartial fraction Expansions
1. q(s) has real and distinct factors
expand as
2. q(s) has real but repeated factor
expanded
q s s bii
n( ) ( )= +∏
=1
q s s b n( ) ( )= +
r ss b
i
ii
n( ) =
+∑=
α
1
r ss b s b s b
nn( )
( ) ( )=
++
++ +
+
α α α1 22 Λ
41
Partial Fraction ExpansionPartial Fraction Expansion
Heaviside expansion
For a rational function of the form
Constants are given by
Note: Most applicable to q(s) with real and distinct roots. It can be applied to more specific cases.
r s p sq s
p s
s b s bi
i
ni
ii
n( ) ( )
( )( )
( ) ( )= =
+∏=
+∑
=
=
1
1
α
αi is b
s b p sq s
i
= + ⎤
⎦⎥ =−( ) ( )
( )
Partial Fraction ExpansionsPartial Fraction Expansions
3. Q(s) has irreducible quadratic factors of the form
where
Algorithm for Solution of ODEs
Take Laplace Transform of both sides of ODESolve for Y(s)=p(s)/q(s)Factor the characteristic polynomial q(s)Perform partial fraction expansionInverse Laplace using Tables of Laplace Transforms
q s s d s d n( ) ( )= + +21 0
d d2
04<
42
Transfer Function Modelsof Dynamical Processe
Process Dynamics and ControlProcess Dynamics and Control
Transfer FunctionTransfer Function
Heated stirred tank exampleHeated stirred tank example
e.g. e.g. The block
is called the transfer function relating Q(s)to T(s)
T ss
Ts
T s Ks
Q sinP( ) ( ) ( ) ( )=
++
++
+τ
τ τ τ10 1
1 1
11
1
1
τ
ττ
τ
sKs
s
P
+
+
+
+
++
Tin(s)
Q(s)
T(0)
T(s)
Ks
P
τ + 1
43
Process ControlProcess Control
Time DomainTime Domain
Transfer function Modeling, Controller Design and Analysis
Process Modeling,Experimentation and
Implementation
Laplace Laplace DomainDomain
Ability to understand dynamics in Laplace and time domains is extremely important in thestudy of process control
Transfer functionTransfer function
Order of underlying ODE is given by degree of characteristic polynomial
e.g. First order processes
Second order processes
Steady-state value obtained directlye.g. First order response to unit step function
Final value theorem
Transfer functions are additive and multiplicative
Y s Ks
U sP( ) ( )=+τ 1
Y s Ks s
U sP( ) ( )=+ +τ ξτ2 2 2 1
Y sK
s sp( )
( )=
+τ 1
[ ] [ ]lim ( ) lim ( )s s
PsY s G s K→ →
= =0 0
44
Transfer functionTransfer function
Effect of many transfer functions on a Effect of many transfer functions on a variable is additivevariable is additive
T ss
Ts
T s Ks
Q sinP( ) ( ) ( ) ( )=
++
++
+τ
τ τ τ10 1
1 1
11
1
1
τ
ττ
τ
sKs
s
P
+
+
+
+
++
Tin(s)
Q(s)
T(0)
T(s)
Transfer FunctionTransfer Function
Effect of consecutive processes in series in Effect of consecutive processes in series in multiplicativemultiplicative
Transfer FunctionTransfer Function
Ks
P
τ + 1Ks
P
τ + 1U(s) Y2(s)Y1(s)
Y s Ks
U s
Y s Ks
Y s
Y s Ks
Ks
U s
P
P
P P
1
2 1
1
1
1
1 1
( ) ( )
( ) ( )
( ) ( )
=+
=+
=+
⎛⎝⎜
⎞⎠⎟ +
⎛⎝⎜
⎞⎠⎟
τ
τ
τ τ
45
Deviation VariablesDeviation Variables
To remove dependence on initial conditione.g.
Remove dependency on T(0)
Transfer functions express extent of deviation from a given steady-state
ProcedureFind steady-stateWrite steady-state equationSubtract from linear ODEDefine deviation variables and their derivatives if requiredSubstitute to re-express ODE in terms of deviation variables
′ =+
′ ++
′T ss
T s Ks
Q sinP( ) ( ) ( )1
1 1τ τ
T ss
Ts
T s Ks
Q sinP( ) ( ) ( ) ( )=
++
++
+τ
τ τ τ10 1
1 1
Example
Jacketed heated stirred tankJacketed heated stirred tank
AssumptionsAssumptions: Constant hold-up in tank and jacketConstant heat capacities and densitiesIncompressible flow
ModelModel
F, Tin
Fc, TcinFc, Tc
F, T
h
dTdt
FV
T T h AC V
T T
dTdt
FV
T T h AC V
T T
inc c
Pc
c c
ccin c
c c
c Pc cc
= − + −
= − − −
( ) ( )
( ) ( )
ρ
ρ
46
Nonlinear Nonlinear ODEsODEs
Q: If the model of the process is nonlinear, how do we express it in terms of a transfer function?
A: We have to approximate it by a linear one (i.e.Linearize) in order to take the Laplace.
f(x0)
f(x)
∂∂fx
x( )0
xx0
Nonlinear systemsNonlinear systems
First order Taylor series expansion
1. Function of one variable
2. Function of two variables
3. ODEs
f x u f xs usf x u
x x xsf x u
u u uss s s s( , ) ( , ) ( , ) ( ) ( , ) ( )≈ + − + −∂∂
∂∂
f x f xsf x
x x xss( ) ( ) ( ) ( )≈ + −∂∂
& ( ) ( ) ( ) ( )x f x f xsf xs
xx xs= ≈ + −∂
∂
47
Transfer functionTransfer function
Procedure to obtain transfer function from nonlinear process models
Find steady-state of processLinearize about the steady-stateExpress in terms of deviations variables about the steady-stateTake Laplace transformIsolate outputs in Laplace domainExpress effect of inputs in terms of transfer functions
Y sU s
G s
Y sU s
G s
( )( )
( )
( )( )
( )
11
22
=
=
First order ProcessesFirst order Processes
ExamplesExamples, Liquid storage
h
F
Fi
ρ ρ ρ ρ βA dhdt
F F F hi i= − = −
ρβ
ρβ
τ
τ
A dhdt
F h
dhdt
h K F
dhdt
h K F
i
p i
p i
= −
+ =
′ + ′ = ′
48
First Order ProcessesFirst Order Processes
ExamplesExamples: Speed of a Car
Stirred-tank heater
Note:
ρ ρ
ρ
τ
C V dTdt
C FT Q
VF
dTdt C F
Q T
dTdt
K Q T
p p
p
p
′ = − ′ + ′
′ = ′ − ′
′ = ′ − ′
1 ′′
=+
T sQ s
Ks
p( )( ) τ 1
M dvdt
u bv
Mb
dvdt b
u v
dvdt
K u vp
′ = ′ − ′
′ = ′ − ′
′ = ′ − ′
1
τ
′′
=+
v su s
Ks
p( )( ) τ 1
′ =T tin ( ) 0
First Order ProcessesFirst Order Processes
Liquid Storage Tank
Speed of a car
Stirred-tank heater
Kp τ
ρ/β ρA/β
M/b 1/b
1/ρCpF V/F
First order processes are characterized by:
1. Their capacity to store material, momentum and energy2. The resistance associated with the flow of mass, momentum or energy in reaching theircapacity
49
First order processesFirst order processes
Liquid storage:Capacity to store mass : ρAResistance to flow : 1/β
Car:Capacity to store momentum: MResistance to momentum transfer : 1/b
Stirred-tank heaterCapacity to store energy: ρCpVResistance to energy transfer : 1/ ρCpF
Time Constant = τ = (Storage capacitance)*(Resistance to flow)
First order processFirst order process
Step response of first order process
Step input signal of magnitude M
Y sKs
Ms
p( ) =+τ 1
0 1 2 3 4 5 60
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.632
y(t)/
KpM
t/τ
50
First order processFirst order process
What do we look for?What do we look for?
Process Gain: SteadyProcess Gain: Steady--State ResponseState Response
Process Time Constant:Process Time Constant:
ττ = =
What do we need?What do we need?
Process at steadyProcess at steady--statestateStep input of magnitude MStep input of magnitude MMeasure process gain from new steadyMeasure process gain from new steady--statestateMeasure time constantMeasure time constant
lims
pp
Ks
K yu→ +
⎡
⎣⎢
⎤
⎦⎥ = = =
0 1τOverall Change in yOverall Change in u
ΔΔ
Time Required to Reach 63.2% of final value
First order processFirst order process
Ramp response:Ramp response:
Ramp input of slope a
Y sKs
as
p( ) =+τ 1 2
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
τ
a
t/τ
y(t)/
Kpa
51
First order ProcessFirst order Process
Sinusoidal responseSinusoidal response
Sinusoidal input Asin(ωt)
Y s Ks
As
P( ) =+ +τ
ωω1 2 2
0 2 4 6 8 10 12 14 16 18 20-1.5
-1
-0.5
0
0.5
1
1.5
2
AR
φ
y(t)/
A
t/τ
[ ]{ }lim ( ) sin( )t
PY s K A t→ ∞
−ℑ =+
+12 21 τ ω
ω φ
First order ProcessesFirst order Processes
10-2
10-1
100
101
102
10-2
10-1
100
AR
/Kp
τpω
Bode Plots
10-2
10-1
100
101
102
-100
-80
-60
-40
-20
0
φ
τpω
High Frequency
AsymptoteCorner Frequency
AR K=
+1 2 2τ ωφ ωτ= − −tan ( )1
Amplitude Ratio Phase Shift
52
Integrating ProcessesIntegrating Processes
Example: Liquid storage tankExample: Liquid storage tank
Process acts as a pure integrator
h
F
Fi
ρ ρ ρA dhdt
F F
Adhdt
F F
i
i
= −
= −
′′ =H s
F s
As
i
( )
( )
/1
F F F F F F
A dhdt
F F
i i is s
i
′ = − ′ = −′ = ′ − ′
,
′′ = −H s
F s
As
( )
( )
/1
Process ModelingProcess Modeling
Step input of magnitude MStep input of magnitude M
Y s Ks
Ms
KMs
( ) = = 2
Out
put
Time
Inpu
t
Time
Slope = KM
y tt
KMt t( ) =
<≥
⎧⎨⎩
0 00
53
Integrating processesIntegrating processes
Unit impulse responseUnit impulse response
Y s Ks
M KMs
( ) = =
Out
put
Time
Inpu
t
Time
KM
y tt
KM t( ) =
<≥
⎧⎨⎩
0 00
Integrating ProcessesIntegrating Processes
Rectangular pulse responseRectangular pulse response
Y s Ks
Ms
e KMs
et s t sw w( ) ( ) ( )= − = −− −1 12
Out
put
Time
Inpu
t
Time
y tKMt t t
KMt t tw
w w( ) =
<≥
⎧⎨⎩
54
Second Order ProcessesSecond Order Processes
Three types of second order process:
1. Multicapacity processes: processes that consist of two or more capacities in seriese.g. Two heated stirred-tanks in series
2. Inherently second order processes: Fluid or solid mechanic processes possessing inertia and subjected to some acceleratione.g. A pneumatic valve
3. Processing system with a controller: Presence of a controller induces oscillatory behaviore.g. Feedback control system
Second order ProcessesSecond order Processes
MulticapacityMulticapacity Second Order ProcessesSecond Order ProcessesNaturally arise from two first order processes in Naturally arise from two first order processes in seriesseries
By multiplicative property of transfer functionsBy multiplicative property of transfer functions
Y s K Ks s
U sP P( )( )( )
( )=+ +
1 2
1 21 1τ τ
Ks
P1
1 1τ +
U(s) Y(s)
Ks
P2
2 1τ +Y(s)U(s)
K Ks s
P P1 2
1 21 1( )( )τ τ+ +
55
Second Order ProcessesSecond Order Processes
Inherently second order process:Inherently second order process:e.g. Pneumatic Valve
Momentum Balance
x
p
M ddt
dxdt
pA Kx C dxdt
MK
d xdt
CK
dxdt
x AK
p
x sp s
AK
MK s C
K s
= − −
+ + =
′′
=+ +
2
2 1( )( )
Second order ProcessesSecond order Processes
Second order process:Second order process:Assume the general formAssume the general form
wherewhere ΚΚPP = Process steady= Process steady--state gainstate gainττ = Process time constant= Process time constantξξ = Damping Coefficient= Damping Coefficient
Three families of processesThree families of processes
ξξ<1<1 UnderdampedUnderdampedξξ==11 Critically DampedCritically Dampedξξ>1>1 OverdampedOverdamped
Note: Chemical processes are typically Note: Chemical processes are typically overdamped overdamped or critically dampedor critically damped
Y S Ks s
U sP( ) ( )=+ +τ ξτ2 2 2 1
56
Second Order ProcessesSecond Order Processes
Roots of the characteristic polynomial
Case 1) ξ>1: Two distinct real rootsSystem has an exponential behavior
Case 2) ξ=1: One multiple real rootExponential behavior
Case 3) ξ<1: Two complex rootsSystem has an oscillatory behavior
− ± −
− ± −
2 4 42
1 1
2 2 2
2
2
ξτ ξ τ ττ
ξτ τ
ξ
Second order ProcessesSecond order Processes
Step response of magnitude MStep response of magnitude M
Y S Ks s
Ms
P( ) =+ +τ ξτ2 2 2 1
0 1 2 3 4 5 6 7 8 9 100
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
ξ=2
ξ=0
ξ=0.2
57
Second order processSecond order process
Observations
Responses exhibit overshoot (y(t)/KM >1) when ξ<1
Large ξ yield a slow sluggish response
Systems with ξ=1 yield the fastest response without overshoot
As ξ (with ξ<1) becomes smaller system becomes more oscillatory
If ξ<0, system oscillates without bounds (unstable)
Second order processesSecond order processes
Example Example -- Two Stirred tanks in seriesTwo Stirred tanks in series
MTin, w
Q
T1, wM
Q
T2, w
Response of T2 toTin is an example of anoverdamped second order process
58
Second order ProcessesSecond order Processes
Characteristics of underdamped second order process
1. Rise time, tr
2. Time to first peak, tp
3. Settling time, ts
4. Overshoot:
5. Decay ratio:
OS ab
= = −−
⎛
⎝⎜⎜
⎞
⎠⎟⎟exp ξ
ξπ
1 2
DR cb
= = −−
⎛
⎝⎜⎜
⎞
⎠⎟⎟exp 2
1 2πξ
ξ
Second order ProcessesSecond order Processes
0 5 10 15 20 25 30 35 40 45 500
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
-5%
+5%
a
b
c
trts
P
tp
59
Second Order ProcessSecond Order Process
Sinusoidal ResponseSinusoidal Response
wherewhere
Y sK
s sA
sp( ) =
+ + +τ ξτ
ω
ω2 2 2 22 1
( )[ ] ( )y t
K Atp( ) sin( )=
− +
+
1 22 2 2ωτ ξτω φ
φ ξωτ
ωτ= −
−
⎡
⎣⎢⎢
⎤
⎦⎥⎥
−tan( )
12
21
( )[ ] ( )ARn =
− +
1
1 22 2 2ωτ ξωτ
Second Order ProcessesSecond Order Processes
Bode PlotsBode Plots
10-1 100 10110-1
100
101
10-1 100 101
-150
-100
-50
0
ξ=1
ξ=0.1
ξ=1
ξ=0.1
60
More Complicated processesMore Complicated processes
Transfer function typically written as rational function of polynomials
where r(s) and q(s) can be factored as
s.t.
G s r sq s
a a s a s
b b s b s( ) ( )
( )= =
+ + +
+ + +
0 1
0 1
Κ
Κ
ρρ
θθ
q s b s s sr s a s s sa a a
( ) ( )( ) ( )( ) ( )( ) ( )
= + + +
= + + +0 1 2
0 1 2
1 1 11 1 1
τ τ ττ τ τ
θ
θ
Λ
Λ
G s Ks s
s sa a
( )( ) ( )
( ) ( )=
+ +
+ +
τ τ
τ τρ
θ
1 1 1
1 11
Λ
Λ
Poles and zeroesPoles and zeroes
Definitions:the roots of r(s) are called the zeroszeros of G(s)
the roots of q(s) are called the polespoles of G(s)
Poles: Directly related to the underlying differential equation
If Re(pi)<0, then there are terms of the form e-pit in y(t) - y(t) vanishes to a unique point
If any Re(pi)>0 then there is at least one term of the form epit - y(t) does not vanish
z za a
11 1
1= − = −
τ τρρ
, ,Λ
p p11
1 1= − = −
τ τθθ
, ,Λ
61
PolesPoles
e.g. A transfer function of the form
with can factored to a sum of
A constant term from sA e-t/τ from the term (τ1s+1)A function that includes terms of the form
Poles can help us to describe the qualitative behavior of a complex system (degree>2)
The sign of the poles gives an idea of the stability of the system
e t
e t
t
t
−
−
−
−
ξτ
ξτ
ξ τ
ξ τ
2
2
1
1
22
22
sin( )
cos( )
Ks s s s( )( )τ τ ξτ1
2 221 2 1+ + +
0 1≤ <ξ
Poles
Calculation performed easily in MATLAB
Function ROOTSe.g.
» ROOTS([1 1 1 1])ans =
-1.0000 0.0000 + 1.0000i0.0000 - 1.0000i
»
q s s s s( ) = + + +3 2 1
MATLAB
62
PolesPoles
Plotting poles in the complex plane
Roots: -1.0, 1.0j, -1.0j
-1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Imag
inar
y ax
is
Real axis
q s s s s( ) = + + +3 2 1
PolesPoles
Process Behavior with purely complex poles
0 5 10 15 20 25 30 35 40 45 500
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8Unit Step Response
y(t)
t
63
PolesPoles
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Imag
inar
y ax
is
Real axis
Roots: -0.4368, -0.4066+0.9897j, -0.4066-0.9897j
2 2 5 3 13 2s s s+ + +.
PolesPoles
Process behavior with mixed real and complex poles
0 2 4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1Unit Step Response
y(t)
t
64
PolesPoles
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6-1.5
-1
-0.5
0
0.5
1
1.5
Imag
inar
y ax
is
Real axis
2 2 5 3 054 3 2s s s s+ + + −. .
Roots: -0.7441, -0.3805+1.0830j, -0.3805-1.0830j, 0.2550
PolesPoles
Process behavior with unstable pole
0 2 4 6 8 10 12 14 16 18 20-20
0
20
40
60
80
100
120
140
160Unit Step Response
y(t)
t
65
ZerosZeros
Transfer function:Transfer function:
Let τ1 is the dominant time constant
G sK ss s
p a( )( )
( )( )=
+
+ +
ττ τ
11 11 2
y t K M e epa
ta
t( ) = +
−−
+−−
⎛
⎝⎜⎜
⎞
⎠⎟⎟
− −1 1
1 22
1 21 2τ τ
τ ττ ττ τ
τ τ
τ τ1 2>
0 2 4 6 8 10 12 14 16 18 20-0.5
0
0.5
1
1.5
2
2.5
3
Time
y(t)/
KM
16
8
4
21
0-1 -2
ZerosZeros
Observations:
Adding a zero to an overdamped second order process yields overshoot and inverse response
Inverse response is observed when the zeros lie in right half complex plane, Re(z)>0
Overshoot is observed when the zero is dominant ( )
Pole-zero cancellation yields a first order process behavior
In physical systems, overshoot and inverse response are a result of two process with different time constants, acting in opposite directions
τ τa > 1
66
ZerosZeros
Can result from two processes in parallel
If gains are of opposite signs and time constants are different then a right half plane zero occurs
U(s) Y(s)
Ks2
2 1τ +
Ks
11 1τ +
G s K ss s
a( ) ( )( )( )
= ++ +τ
τ τ1
1 11 2
K K K= +1 2 τ τ τa
K KK K
=++
1 2 2 11 2
Dead TimeDead Time
Time required for the fluid to reach the valve
usually approximated as dead time
h
Fi
Control loop
Manipulation of valve does not lead to immediatechange in level
67
Dead timeDead time
Delayed transfer functions
e.g. First order plus dead-time
Second order plus dead-time
e d s−τ G s( )U(s) Y(s)
Y s e G s U sd s( ) ( ) ( )= −τ
G se K
s
d sp( ) =
+
−τ
τ 1
G s e Ks s
d sP( ) =
+ +
−τ
τ ξτ2 2 2 1
Dead timeDead time
Dead time (delay)
Most processes will display some type of lag time Dead time is the moment that lapses between input changes and process response
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
y/K
M
t/tauτD
Step response of a first order plus dead time process
G s e d s( ) = −τ
68
Dead TimeDead Time
Problemuse of the dead time approximation makes analysis (poles and zeros) more difficult
Approximate dead-time by a rational (polynomial) function
Most common is Pade approximation
G se K
s
d sp( ) =
+
−τ
τ 1
e G ss
s
e G ss s
s s
s
s
−
−
≈ =−
+
≈ =− +
+ +
θ
θ
θ
θ
θ θ
θ θ
1
2
22
22
12
12
12 12
12 12
( )
( )
Pade Pade ApproximationsApproximations
In general Pade approximations do not approximate dead-time very well
Pade approximations are better when one approximates a first order plus dead time process
Pade approximations introduce inverse response (right half plane zeros) in the transfer function
Limited practical use
G se K
s
s
s
Ks
sp p( ) =
+≈
−
+ +
−θ
τ
θ
θ τ1
12
12
1
69
Process ApproximationProcess Approximation
Dead timeDead timeFirst order plus dead time model is often used First order plus dead time model is often used for the approximation of complex processesfor the approximation of complex processes
Step response of an Step response of an overdampedoverdamped second second order processorder process
0 1 2 3 4 5 6 7 80
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
- First Order plus dead timeo Second Order
Process ApproximationProcess Approximation
Second order Second order overdamped overdamped or first order or first order plus dead time?plus dead time?
Second order process model may be more Second order process model may be more difficult to identifydifficult to identify
0 1 2 3 4 5 6 7 8-0.2
0
0.2
0.4
0.6
0.8
1
1.2
-- First order plus dead time- Second order overdampedo Actual process
70
Process ApproximationProcess Approximation
Transfer Function of a delay system
First order processes
Second order processes
Y s K es
U sP
sD
( ) ( )=+
− τ
τ 1
Y S K es s
U sP
sD
( ) ( )=+ +
− τ
τ ξτ2 2 2 1
G(s)e Ds− τ
Y(s)U(s)
Process ApproximationProcess Approximation
More complicated processesHigher order processes (e.g. N tanks in series)
For two dominant time constants τ1 and τ2process well approximated by
For one dominant time constant τ1, process well approximated by
Y(s)K K Ks s s
P P PN
N
1 2
1 21 1 1Λ
Λ( )( ) ( )τ τ τ+ + +U(s)
G se K
s
sp
ii
N( )
( )≈
+=
−
=∑
θ
τθ τ
1 21
G se K
s s
sp
ii
N( )
( )( )≈
+ +=
−
=∑
θ
τ τθ τ
1 2 31 1
71
Process ApproximationProcess Approximation
ExampleExample
G ss s s
( )( )( )( )
=+ + +
110 1 25 1 1 2
0 20 40 60 80 100 120 140 160 180 200-0.2
0
0.2
0.4
0.6
0.8
1
1.2
G s es
s1
12
25 1( ) =
+
−
G s es s
s2
2
10 1 25 1( )
( )( )=
+ +
−
Empirical ModelingEmpirical Modeling
Objective:
To identify low-order process dynamics (i.e.,first and second order transfer function models)Estimate process parameters (i.e., Kp, τ and ξ)
Methodologies:
1. Least Squares Estimationmore systematic statistical approach
2. Process Reaction Curve Methodsquick and easybased on engineering heuristics
72
Empirical ModelingEmpirical Modeling
Least Squares Estimation:Simplest model form
Process Description
where y vector of process measurementx vector of process inputsβ1, β0 process parameters
Problem:Find β1, β0 that minimize the sum of squared
residuals (SSR)
E y x[ ] = +β β0 1
y x= + +β β ε0 1
SSR y xi ii
n= − −
=∑ ( )β β0 1
2
1
Empirical ModelingEmpirical Modeling
SolutionDifferentiate SSR with respect to parameters
These are called the normal equations. Solving for parameters gives:
where
∂∂ β
β β
∂∂ β
β β
SSR y x
SSR x y x
i ii
n
i i ii
n0
0 11
10 1
1
2 0
2 0
= − − − =
= − − − =
=∑
=∑
( ∃ ∃ )
( ∃ ∃ )
∃ ∃
∃
β β
β
0 1
11
2 2
1
= −
=−
−
=∑
=∑
y x
x y nxy
x nx
i ii
n
ii
n
x xn
y yn
ii
n ii
n= =
=∑
=∑,
1 1
73
Empirical ModelingEmpirical Modeling
Compact formDefine
Then
Problemfind value of β that minimize SSR
Y
yy
y
X
xx
xn n
=
⎡
⎣
⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥
=
⎡
⎣
⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥
=⎡
⎣⎢
⎤
⎦⎥
1
2
1
2 0
1
11
1Μ Μ Μ
, , βββ
E
y xy x
y x
Y X
n n
=
− −− −
− −
⎡
⎣
⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥
= −
1 0 1 1
2 0 1 2
0 1
β ββ β
β β
βΜ
SSR E ET=
Empirical ModelingEmpirical Modeling
Solution in Compact FormNormal Equations can be written as
which can be shown to give
or
In practiceManipulations are VERY easy to perform in MATLABExtends to general linear model (GLM)
Polynomial model
∂∂ βE ET
= 0
X X X YT T∃β =
( )∃β =−
X X X YT T1
E y x x[ ] = + +β β β0 1 1 11 12
E y x xp p[ ] = + + +β β β0 1 1 Κ
74
Empirical ModelingEmpirical Modeling
Control Implementation:previous technique applicable to process model that are linear in the parameters (GLM, polynomials in x, etc…)
i.e. such that, for all i, the derivatives are not a function of β
typical process step responsesfirst order
Nonlinear in Kp and τoverdamped second order
Nonlinear in Kp, τ1 and τ2
nonlinear optimization is required to find the optimum parameters
∂∂ β
ei
E y t K M ept[ ( )] ( )/= − −1 τ
E y t K M e ep
t t[ ( )]
/ /= − −
−
⎛
⎝⎜⎜
⎞
⎠⎟⎟
− −1 1 2
1 2
1 2τ ττ τ
τ τ
Empirical ModelingEmpirical Modeling
Nonlinear Least Squares required for control applications
system output is generally discretized
or, simply
First Order process (step response)
Least squares problem becomes the minimization of
This yields an iterative problem solution best handled by software packages: SAS, Splus, MATLAB (function leastsq)
SSR y K M ei ptni
i= − − −∑
=
( ( ))/1 2
1
τ
y t y y yn( ) [ , , , ]→ 1 2 Κ
y t y t y t y tn( ) [ ( ), ( ), , ( )]→ 1 2 Κ
E y K M ei pti[ ] ( )/= − −1 τ
75
Empirical ModelingEmpirical Modeling
Example
Nonlinear Least Squares Fit of a first order process from step response data
Model
Data
E y t K ept[ ( )] . ( )/= − −30 1 τ
0 10 20 30 40 50 60 70 80-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
y(t)
t
Step Response
Empirical ModelingEmpirical Modeling
Results:Using MATLAB function “leastsq” obtained
Resulting Fit
Kp = =13432 118962. , .τ
0 10 20 30 40 50 60 70 80-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
y(t)
t
Step Response
76
Empirical ModelingEmpirical Modeling
Approximation using delayed transfer functions
For first order plus delay processes
DifficultyDiscontinuity at θ makes nonlinear least squares difficult to apply
Solution1. Arbitrarily fix delay or estimate using
alternative methods2. Estimate remaining parameters3. Readjust delay repeat step 2 until best value of
SSR is obtained
E yt
K M e tip
ti[ ]( )( )/=
≤ <− ≥
⎧⎨⎩
− −0 0
1θ
θθ τ
Empirical ModelingEmpirical Modeling
Example 2Underlying “True” Process
Data
G ss s s
( )( )( )( )
=+ + +
110 1 25 1 1 2
0 20 40 60 80 100 120 140-0.5
0
0.5
1
1.5
2
2.5
3
3.5
y(t)
t
77
Empirical ModelingEmpirical Modeling
Fit of a first order plus dead time
Second order plus dead time
G s es s
s2
20 994624 9058 1 101229 1
( ) .( . )( . )
=+ +
−
G s es
s1
111000027 3899 1
( ) .( . )
=+
−
0 20 40 60 80 100 120 140-0.5
0
0.5
1
1.5
2
2.5
3
3.5
t
y(t)
Empirical Modeling
Process reaction curve method:based on approximation of process using first order plus delay model
1. Step in U is introduced2. Observe behavior ym(t)3. Fit a first order plus dead time model
GpGc
Gs
M/s D(s)
Y(s)
Ym(s)
Y*(s)
U(s)
Manual Control
Y s KMes sm
s( )
( )=
+
−θ
τ 1
78
Empirical Modeling
First order plus dead-time approximations
Estimation of steady-state gain is easyEstimation of time constant and dead-time is more difficult
0 1 2 3 4 5 6 7 8-0.2
0
0.2
0.4
0.6
0.8
1
1.2
τ
KM
θ
Empirical ModelingEmpirical Modeling
Estimation of time constant and dead-time from process reaction curves
find times at which process reaches 35.3% and 85.3%
Estimate
0 20 40 60 80 100 120 140 1600
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
y(t)
t
τ
t1 t2
θτ
= −= −
13 0 290 67
1 2
2 1
. .. ( )
t tt t
79
Empirical ProcessEmpirical Process
ExampleFor third order process
Estimates:
Compare:Least Squares Fit Reaction Curve
G ss s s
( )( )( )( )
=+ + +
110 1 25 1 1 2
t t1 223 62 51178 26 46
= =∴ = =
, .. , .θ τ
G s es
s1
117810026 46 1
( ) .( . )
.=
+
−G s e
s
s1
111000027 3899 1
( ) .( . )
=+
−
Empirical ModelingEmpirical Modeling
Process Reaction Curve Method
based on graphical interpretationvery sensitive to process noiseuse of step responses is troublesome in normal plant operations
frequent unmeasurable disturbancesdifficulty to perform instantaneous step changesmaybe impossible for slow processes
restricted to first order models due to reliabilityquick and easy
Least Squaressystematic approachcomputationally intensivecan handle any type of dynamics and input signalscan handle nonlinear control processesreliable
80
Feedback ControlFeedback Control
Steam heated stirred tankSteam heated stirred tank
Feedback control system: Valve is manipulated to increase flow of steam to control tank temperature
Closed-loop process: Controller and process are interconnected
TT TC
IP
Ps
Condensate
Steam
Fin,Tin
F,TIP
LT
LC
Feedback ControlFeedback Control
Control Objective:maintain a certain outlet temperature and tank level
Feedback Control:
temperature is measured using a thermocouplelevel is measured using differential pressure probesundesirable temperature triggers a change in supply steam pressurefluctuations in level trigger a change in outlet flow
Note:level and temperature information is measured at outlet of process/ changes result from inlet flow or temperature disturbances inlet flow changes MUST affect process before an adjustment is made
81
ExamplesExamples
Feedback Control:requires sensors and actuators
e.g. Temperature Control Loop
Controller:software component implements math hardware component provides calibrated signal for actuator
Actuator:physical (with dynamics) process triggered by controllerdirectly affects process
Sensor:monitors some property of system and transmits signal back to controller
Tin, F
TeAC P
M
Controller
Tank-
+Valve
Thermocouple
TR
ClosedClosed--loop Processesloop Processes
Study of process dynamics focused on uncontrolled or Open-loop processes
Observe process behavior as a result of specific input signals
In process control, we are concerned with the dynamic behavior of a controlled or Closed-loopprocess
Controller is dynamic system that interacts with the process and the process hardware to yield a specific behaviour
GpY(s)U(s)
Gc
Gm
GpGv+
-+ +
controller actuator process
sensor
R(s) Y(s)
D(s)
82
ClosedClosed--Loop Transfer FunctionLoop Transfer Function
Block Diagram of Closed-Loop Process
Gp(s) - Process Transfer Function
Gc(s) - Controller Transfer Function
Gm(s) - Sensor Transfer Function
Gv(s) - Actuator Transfer Function
Gc
Gm
GpGv+
-+ +
controller actuator process
sensor
R(s) Y(s)
D(s)
ClosedClosed--Loop Transfer FunctionLoop Transfer Function
For control, we need to identify closed-loop dynamics due to:- Setpoint changes Servo- Disturbances Regulatory
1. Closed-Loop Servo Responsetransfer function relating Y(s) and R(s) when D(s)=0
Isolate Y(s)
[ ][ ]
Y s G s V s
Y s G s G s U s
Y s G s G s G s E s
Y s G s G s G R s Y s
Y S G s G s G s R s G s Y s
p
p v
p v c
p v c s m
p v c m
( ) ( ) ( )
( ) ( ) ( ) ( )
( ) ( ) ( ) ( ) ( )
( ) ( ) ( ) ( ) ( )
( ) ( ) ( ) ( ) ( ) ( ) ( )( )
=
=
=
= −
= −
Y sG s G s G s
G s G s G s G sR sp v c
p v c m( )
( ) ( ) ( )( ) ( ) ( ) ( )
( )=+1
83
ClosedClosed--Loop Transfer FunctionLoop Transfer Function
2. Closed-loop Regulatory Response
Transfer Function relating D(s) to Y(s) at R(s)=0
Isolating Y(s)
[ ][ ]
Y s D s G s V s
Y s D s G s G s U s
Y s D s G s G s G s E s
Y s D s G s G s G s Y s
Y s D s G s G s G s G s Y s
p
p v
p v c
p v c m
p v c m
( ) ( ) ( ) ( )
( ) ( ) ( ) ( ) ( )
( ) ( ) ( ) ( ) ( ) ( )
( ) ( ) ( ) ( ) ( ) ( )
( ) ( ) ( ) ( ) ( ) ( ) ( )
= +
= +
= +
= + −
= + −
0
0
Y sG s G s G s G s
D sp v c m
( )( ) ( ) ( ) ( )
( )=+
11
ClosedClosed--loop Transfer Functionloop Transfer Function
2. Regulatory Response with Disturbance Dynamics
Gd(s) Disturbance (or load) transfer function
3. Overall Closed-Loop Transfer Function
Y s G sG s G s G s G s
D sd
p v c m( ) ( )
( ) ( ) ( ) ( )( )=
+1
Y sG s G s G s
G s G s G s G sR s
G sG s G s G s G s
D s
p v c
p v c m
dp v c m
( )( ) ( ) ( )
( ) ( ) ( ) ( )( )
( )( ) ( ) ( ) ( )
( )
=+
+
+
1
1
Regulatory
Servo
84
PID ControllersPID Controllers
The acronym PID stands for:P - ProportionalI - IntegralD - Derivative
PID Controllers: greater than 90% of all control implementationsdates back to the 1930svery well studied and understoodoptimal structure for first and second order processes (given some assumptions)always first choice when designing a control system
PID controller equation:
u t K e t e d dedt
ucI
Dt
R( ) ( ) ( )= + +⎡
⎣⎢
⎤
⎦⎥ +∫
10τ
ζ ζ τ
PID ControlPID Control
PID Control Equation
PID Controller ParametersKc Proportional gainτI Integral Time ConstantτD Derivative Time ConstantuR Controller Bias
u t K e t e d dedt
ucI
Dt
R( ) ( ) ( )= + +⎡
⎣⎢
⎤
⎦⎥ +∫
10τ
ζ ζ τ
Proportional Action
IntegralAction
DerivativeAction
ControllerBias
85
PID ControlPID Control
PID Controller Transfer Function
or:
Note:
numerator of PID transfer function cancels second order dynamicsdenominator provides integration to remove possibility of steady-state errors
[ ]ℑ − = ′ = + +⎛⎝⎜
⎞⎠⎟u t u U s K
ss E sR c
ID( ) ( ) ( )1 1
ττ
′ = + +⎛⎝⎜
⎞⎠⎟
U s P Is
Ds E s( ) ( )
PID ControlPID Control
Controller Transfer Function:
or,
Note:
Many variations of this controller existEasily implemented in SIMULINKeach mode (or action) of controller is better studied individually
G s Ks
sc cI
D( ) = + +⎛⎝⎜
⎞⎠⎟1 1
ττ
G s P Is
Dsc ( ) = + +⎛⎝⎜
⎞⎠⎟
86
Proportional FeedbackProportional Feedback
Form:
Transfer function:
or,
Closed-loop form:
u t u K e tR c( ) ( )− =
U s K E sc' ( ) ( )=
G s Kc c( ) =
Y sG s G s K
G s G s K G sR s
G s G s K G sD s
p v c
p v c m
p v c m
( )( ) ( )
( ) ( ) ( )( )
( ) ( ) ( )( )
=+
+
+
1
11
Proportional FeedbackProportional Feedback
Example:Given first order process:
for P-only feedback closed-loop dynamics:
G sKs
G s G spp
v m( ) , ( ) , ( )=+
= =τ 1
1 1
Y s
KpKcKpKc
KpKcs
R s
KpKcs
KpKc
KpKcs
D s
( ) ( )
( )
=+
+
⎛
⎝⎜⎜
⎞
⎠⎟⎟ +
++
⎛
⎝⎜⎜
⎞
⎠⎟⎟ +
+
+
⎛
⎝⎜⎜
⎞
⎠⎟⎟ +
1
11
11
1
11
τ
τ
τ
Closed-LoopTime Constant
87
Proportional FeedbackProportional Feedback
Final response:
Note:for “zero offset response” we require
Possible to eliminate offset with P-only feedback (requires infinite controller gain)
Need different control action to eliminate offset (integral)
lim ( ) , lim ( )t
yservo tKpKc
KpKc tyreg t
KpKc→∞=
+ →∞=
+11
1
lim ( ) , lim ( )t
servot
regy t y t→∞ →∞
= =1 0
Tracking Error Disturbance rejection
Proportional Feedback
Servo dynamics of a first order process under proportional feedback
- increasing controller gain eliminates off-set
0 1 2 3 4 5 6 7 8 9 100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.01
0.5
1.0
5.0
10.0
Kc
y(t)/
KM
t/τ
88
Proportional Feedback
High-order processe.g. second order underdamped process
increasing controller gain reduces offset, speeds response and increases oscillation
0 5 10 15 20 250
0.5
1
1.5
5.0
2.5
1.0
0.5
0.01
y(t)/
KM
Proportional FeedbackProportional Feedback
Important points:proportional feedback does not change the order of the system
started with a first order processclosed-loop process also first orderorder of characteristic polynomial is invariant under proportional feedback
speed of response of closed-loop process is directly affected by controller gain
increasing controller gain reduces the closed-loop time constant
in general, proportional feedbackreduces (does not eliminate) offsetspeeds up responsefor oscillatory processes, makes closed-loop process more oscillatory
89
Integral ControlIntegral Control
Integrator is included to eliminate offset
provides reset actionusually added to a proportional controller to produce a PI controller
PID controller with derivative action turned offPI is the most widely used controller in industryoptimal structure for first order processes
PI controller form
Transfer function model
u t K e t e d ucI
tR( ) ( ) ( )= +
⎡
⎣⎢
⎤
⎦⎥ +∫
10τ
ζ ζ
′ = +⎛⎝⎜
⎞⎠⎟U s K
sE sc
I( ) ( )1 1
τ
PI FeedbackPI Feedback
Closed-loop response
more complex expressiondegree of denominator is increased by one
Y sG s G s K s
s
G s G s K ss
G sR s
G s G s K ss
G sD s
p v cI
I
p v cI
Im
p v cI
Im
( )( ) ( )
( ) ( ) ( )( )
( ) ( ) ( )( )
=
+⎛⎝⎜
⎞⎠⎟
+ +⎛⎝⎜
⎞⎠⎟
+
+ +⎛⎝⎜
⎞⎠⎟
ττ
ττ
ττ
1
1 1
1
1 1
90
PI FeedbackPI Feedback
ExamplePI control of a first order process
Closed-loop response
Note:offset is removedclosed-loop is second order
G sKs
G s G spp
v m( ) , ( ) , ( )=+
= =τ 1
1 1
Y s s
K Ks
K KK K
sR s
K Ks
K Ks
K Ks
K KK K
sD s
I
Ic p
c p
c pI
Ic p
Ic p
Ic p
c p
c pI
( ) ( )
( )
=+
⎛
⎝⎜
⎞
⎠⎟ +
+⎛
⎝⎜
⎞
⎠⎟ +
+
⎛
⎝⎜
⎞
⎠⎟ +
⎛
⎝⎜
⎞
⎠⎟
⎛
⎝⎜
⎞
⎠⎟ +
+⎛
⎝⎜
⎞
⎠⎟ +
ττ τ τ
τ τ τ
τ τ τ
11
1
11
2
2
2
PI FeedbackPI Feedback
Example (contd)effect of integral time constant and controller gain
on closed-loop dynamics
natural period of oscillation
damping coefficient
integral time constant and controller gain can induce oscillation and change the period of oscillation
τ τ τcl
I
c pK K=
ξτ
ττ=
+⎛
⎝⎜
⎞
⎠⎟1
21K
KK K
K Kp
c Ic p
c p
91
PI Feedback
Effect of integral time constant on servo dynamics
0 1 2 3 4 5 6 7 8 9 100
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
y(t)/
KM
0.01
0.1
0.5
1.0
Kc=1
PI Feedback
Effect of controller gain
affects speed of responseincreasing gain eliminates offset quicker
0 1 2 3 4 5 6 7 8 9 100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
y(t)/
KM 0.1
0.51.0
5.010.0
τI=1
92
PI Feedback
Effect of integral action of regulatory response
reducing integral time constant removes effect of disturbancesmakes behavior more oscillatory
0 1 2 3 4 5 6 7 8 9 10-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
y(t)/
KM
PI FeedbackPI Feedback
Important points:
integral action increases order of the system in closed-loop
PI controller has two tuning parameters that can independently affect
speed of responsefinal response (offset)
integral action eliminates offset
integral actionshould be small compared to proportional actiontuned to slowly eliminate offsetcan increase or cause oscillationcan be de-stabilizing
93
Derivative ActionDerivative Action
Derivative of error signalUsed to compensate for trends in output
measure of speed of error signal changeprovides predictive or anticipatory action
P and I modes only response to past and current errorsDerivative mode has the form
if error is increasing, decrease control actionif error is decreasing, decrease control action
Always implemented in PID form
u t K e t e d dedt
ucI
Dt
R( ) ( ) ( )= + +⎡
⎣⎢
⎤
⎦⎥ +∫
10τ
ζ ζ τ
DK
dedtc
D≡ τ
PID FeedbackPID Feedback
Transfer Function
Closed-loop Transfer Function
Slightly more complicated than PI form
′ = + +⎛⎝⎜
⎞⎠⎟U s K
ss E sc
ID( ) ( )1 1
ττ
Y s
G s G s K s ss
G s G s K s ss
G s
R s
G s G s K s ss
G s
D s
p v cD I I
I
p v cD I I
Im
p v cD I I
Im
( )
( ) ( )
( ) ( ) ( )
( )
( ) ( ) ( )
( )
=
+ +⎛
⎝⎜⎜
⎞
⎠⎟⎟
+ + +⎛
⎝⎜⎜
⎞
⎠⎟⎟
+
+ + +⎛
⎝⎜⎜
⎞
⎠⎟⎟
τ τ ττ
τ τ ττ
τ τ ττ
2
2
2
1
1 1
1
1 1
94
PID FeedbackPID Feedback
Example:PID Control of a first order process
Closed-loop transfer function
G sKs
G s G spp
v m( ) , ( ) , ( )=+
= =τ 1
1 1
Y s s s
K Ks
K KK K
sR s
K Ks
K Ks
K Ks
K KK K
sD s
D I I
Ic p
D Ic p
c pI
Ic p
Ic p
Ic p
D Ic p
c pI
( ) ( )
( )
=+ +
+⎛
⎝⎜⎜
⎞
⎠⎟⎟ +
+⎛
⎝⎜⎜
⎞
⎠⎟⎟ +
+
⎛
⎝⎜⎜
⎞
⎠⎟⎟ +
⎛
⎝⎜⎜
⎞
⎠⎟⎟
+⎛
⎝⎜⎜
⎞
⎠⎟⎟ +
+⎛
⎝⎜⎜
⎞
⎠⎟⎟ +
τ τ τ
τ τ τ τ τ
τ τ τ
τ τ τ τ τ
2
2
2
2
11
1
11
PID Feedback
Effect of derivative action on servo dynamics
0 1 2 3 4 5 6 7 8 9 100
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2.01.00.5
0.1
y(t)/
KM
95
PID Feedback
Effect of derivative action on regulatory response
increasing derivative action reduces impact of disturbances on control variableslows down servo response and affects oscillation of process
0 1 2 3 4 5 6 7 8 9 10-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
2.01.00.50.1
Derivative ActionDerivative Action
Important Points:
Characteristic polynomial is similar to PIderivative action does not increase the order of the systemadding derivative action affects the period of oscillation of the process
good for disturbance rejectionpoor for tracking
the PID controller has three tuning parameters and can independently affect,
speed of responsefinal response (offset)servo and regulatory response
derivative actionshould be small compared to integral actionhas a stabilizing influencedifficult to use for noisy signalsusually modified in practical implementation
96
Closed-loop Stability
Every control problem involves a consideration of closed-loop stability
General concepts:
BIBO Stability:
“ An (unconstrained) linear system is said to be stable if the output response is boundedfor all bounded inputs. Otherwise it is unstable.”
Comments:Stability is much easier to prove than unstabilityThis is just one type of stability
Closed-loop Stability
Closed-loop dynamics
if GOL is a rational function then the closed-loop transfer functions are rational functions and take the form
and factor as
Y sG G GG G G G
Y sG G G G
D sc v p
c v p m c v p m( ) ( ) ( )*=
++
+11
1
GOL
G s r sq s
a a s a s
b b s b s( ) ( )
( )= =
+ + +
+ + +
0 1
0 1
Κ
Κ
ρρ
θθ
G s Ks s
s sa a
( )( ) ( )
( ) ( )=
+ +
+ +
τ τ
τ τρ
θ
1 1 1
1 11
Λ
Λ
97
Closed-loop stability
General Stability criterion:
“ A closed-loop feedback control system is stable if and only if all roots of the characteristic polynomial are negative or have negative real parts. Otherwise, the system is unstable.”
Unstable region is the right half plane of the complex plane.
Valid for any linear systems.
Underlying system is almost always nonlinearso stability holds only locally. Moving away from the point of linearization may cause instability.
Closed-loop Stability
Problem reduces to finding roots of a polynomial
Easy (1990s) way : MATLAB function ROOTS
Traditional:1. Routh array:
Test for positivity of roots of a polynomial
2. Direct substitutionComplex axis separates stable and unstable regionsFind controller gain that yields purely complex roots
3. Root locus diagram Vary location of poles as controller gain is variedOf limited use
98
Closed-loop stability
Routh array for a polynomial equation
is
where
Elements of left column must be positive to have roots with negative real parts
a s a s a s ann
nn+ + + + =−
−1
11 0 0Λ
a a aa a a
b b bc c
z
n n n
n n n
− −
− − −
2 4
1 3 5
1 2 3
1 2
1
ΛΛΛ
ΛΜ
1234
1Μ
n +
b a a a aa
b a a a aa
c b a b ab
c b a b ab
n n n n
n
n n n n
n
n n n n
11 2 3
12
1 4 5
1
11 3 2 1
12
1 5 3 1
1
= − = −
= − = −
− − −
−
− − −
−
− − − −
, ,
, ,
Κ
Κ
Example: Routh Array
Characteristic polynomial
Polynomial Coefficients
Routh Array
Closed-loop system is unstable
2 36 149 058 121 0 42 0 78 05 4 3 2. . . . . .s s s s s+ − + + + =
a a aa a ab b bc cd de
5 3 1
4 2 0
1 2 3
1 2
1 2
1
2 36 0 58 0 42149 121 0 782 50 082 0
0 72 0 78189 00 78
( . ) ( . ) ( . )( . ) ( . ) ( . )
( . ) ( . ) ( )( . ) ( . )( . ) ( )( . )
−
− −
a a a a a a5 4 3 2 1 02 36 149 058 121 0 42 0 78= = = − = = =. , . , . , . , . , .
99
Direct Substitution
Technique to find gain value that de-stabilizes the system.
Observation: Process becomes unstable when poles appear on
right half plane
Find value of Kc that yields purely complex poles
Strategy:Start with characteristic polynomial
Write characteristic equation:
Substitute for complex pole (s=jω)
Solve for Kc and ω
q j K r jc( ) ( )ω ω+ = 0
( )1 1+ = +K G s G s G s K r s
q sc v p m c( ) ( ) ( ) ( )
q s K r sc( ) ( )+ = 0
Example: Direct Substitution
Characteristic equation
Substitution for s=jω
Real Part Complex Part
System is unstable if
1 105 05 0 75
0
05 05 0 75 0
05 05 0 75 0
3 2
3 2
3 2
+ +
+ − −=
+ − − + + =
+ + − + − =
K ss s s
s s s K s K
s s K s K
c
c c
c c
. . .. . .
. ( . ) ( . )
( ) . ( ) ( . ) ( . )
. ( . ) ( . )
j j K j K
j K j Kc c
c c
ω ω ω
ω ω ω
3 2
3 205 05 0 75 0
0 5 05 0 75 0
+ + − + − =
− − + − + − =
− + − =0 5 0 75 02. .ω Kc ( . )Kc − − =05 03ω ω
∴ = + ⇒ + − − =
⇒ − + =
⇒ = ± =
K
K
c
c
0 5 0 75 05 0 75 0 5 0
05 0 25 02 2 1
2 2 3
2. . ( . . . )
. ./ ,
ω ω ω ω
ω
ω
Kc > 1
100
Root Locus Diagram
Old method that consists in plotting poles of characteristic polynomial as controller gain is changed
e.g.
s s K s Kc c3 205 05 0 75 0+ + − + − =. ( . ) ( . )
-1.5 -1 -0.5 0 0.5 1 1.5-1.5
-1
-0.5
0
0.5
1
1.5Im
agin
ary
Axi
s
Real Axis
Kc-0
Kc-0 1
Stability and Performance
Given plant model, we assume a stable closed-loop system can be designed
Once stability is achieved - need to consider performance of closed-loop process - stability is not enough
All poles of closed-loop transfer function have negative real parts - can we place these poles to get a “good” performance
S: Stabilizing Controllers for a given plant
P: Controllers that meet performance
S
P
CSpace of all Controllers
101
Controller Tuning
Can be achieved byDirect synthesis : Specify servo transfer function required and calculate required controller - assume plant = model
Internal Model Control: Morari et al. (86) Similar to direct synthesis except that plant and plant model are concerned
Tuning relations:Cohen-Coon - 1/4 decay ratiodesigns based on ISE, IAE and ITAE
Frequency response techniquesBode criterionNyquist criterion
Field tuning and re-tuning
Direct Synthesis
From closed-loop transfer function
Isolate Gc
For a desired trajectory (C/R)d and plant model Gpm, controller is given by
not necessarily PID forminverse of process model to yield pole-zero cancellation (often inexact because of process approximation)used with care with unstable process or processes with RHP zeroes
CR
G GG Gc p
c p=
+1
GG
CRC
Rc
p=
−
⎛
⎝⎜⎜
⎞
⎠⎟⎟
11
( )( )G
G
CRC
Rc
pmd
d
=−
⎛
⎝
⎜⎜
⎞
⎠
⎟⎟
11
102
Direct Synthesis
1. Perfect Control
cannot be achieved, requires infinite gain
2. Closed-loop process with finite settling time
For 1st order Gp, it leads to PI controlFor 2nd order, get PID control
3. Processes with delay θ
requiresagain, 1st order leads to PI control2nd order leads to PID control
CR d
⎛⎝⎜
⎞⎠⎟
= 1
CR sd c
⎛⎝⎜
⎞⎠⎟
=+
11τ
CR
esd
s
c
c⎛⎝⎜
⎞⎠⎟ =
+
−θ
τ 1
θ θc ≥
IMC Controller Tuning
Gpm
Gp
R
-+
+-
++
D
CGc
*
Closed-loop transfer function
CG G
G G GR
G G
G G GDc p
c p pm
c p
c p pm=
+ −+
−
+ −
*
*
*
*( ) ( )1
1
1
In terms of implemented controller, Gc
G GG G
cc
c pm=
−
*
*1
103
IMC Controller Tuning
1. Process model factored into two parts
where contains dead-time and RHP zeros, steady-state gain scaled to 1.
2. Controller
where f is the IMC filter
based on pole-zero cancellationnot recommended for open-loop unstable processesvery similar to direct synthesis
G G Gpm pm pm= + −
Gpm+
GG
fcpm
* = −1
fsc
r=+
11( )τ
Example
PID Design using IMC and Direct synthesis for the process
Process parameters: K=0.3, τ=30, θ=9
1. IMC Design: Kc=6.97, τI=34.5, τd=3.93Filter
2. Direct Synthesis: Kc=4.76, τI=30Servo Transfer function
G s esps
( ) .=+
−9 0 330 1
fs
=+
112 1
CR
esd
s⎛⎝⎜
⎞⎠⎟
=+
−9
12 1
104
Example
Result: Servo ResponseIMC and direct synthesis give roughly same results
IMC not as good due to Pade approximation
0 50 100 150 200 250 3000
5
10
15
20
25
y(t)
t
IMC
DirectSynthesis
Example
Result: Regulatory response
Direct synthesis rejects disturbance more rapidly (marginally)
0 50 100 150 200 250 30015
20
25
30
35
40
y(t)
t
IMC
Direct Synthesis
105
Tuning Relations
Process reaction curve method:based on approximation of process using first order plus delay model
1. Step in U is introduced2. Observe behavior ym(t)3. Fit a first order plus dead time model
GpGc
Gs
1/s D(s)
Y(s)
Ym(s)
Y*(s)
U(s)
Manuel Control
Y s Kesm
s( ) =
+
−θ
τ 1
Tuning Relations
Process response
4. Obtain tuning from tuning correlationsZiegler-NicholsCohen-CoonISE, IAE or ITAE optimal tuning relations
0 1 2 3 4 5 6 7 8-0.2
0
0.2
0.4
0.6
0.8
1
1.2
τ
KM
θ
106
Ziegler-Nichols Tunings
Controller Kc Ti Td
P-only )/)(/1( θτpKPI )/)(/9.0( θτpK θ3.3PID )/)(/2.1( θτpK θ0.2 θ5.0
- Note presence of inverse of process gain in controllergain- Introduction of integral action requires reduction incontroller gain- Increase gain when derivation action is introduced
Example:
PI: Kc= 10 τI=29.97PID: Kc= 13.33 τI=18
τI=4.5
G s esps
( ) .=
+
−9 0 330 1
Example
Ziegler-Nichols Tunings: Servo response
0 50 100 150 200 250 30020
25
30
35
40
45
50Z-N PI
Z-N PID
Direct Synthesisy(t)
t
107
Example
Regulatory Response
Z-N tuningOscillatory with considerable overshootTends to be conservative
0 50 100 150 200 250 30010
15
20
25
30
35
40
Direct Synthesis
Z-N PID
Z-N PI
Cohen-Coon Tuning Relations
Designed to achieve 1/4 decay ratiofast decrease in amplitude of oscillation
Example:
PI: Kc=10.27 τI=18.54Kc=15.64 τI=19.75τd=3.10
Controller Kc Ti TdP-only ]3/1)[/)(/1( τθθτ +pKPI ]12/9.0)[/)(/1( τθθτ +pK
)/(209)]/(330[
τθτθθ
++
PID]
12163)[/)(/1(τ
τθθτ +pK
)/(813)]/(632[
τθτθθ
++
)/(2114
τθθ
+
108
Tuning relations
Cohen-coon: Servo
More aggressive/ Higher controller gainsUndesirable response for most cases
0 50 100 150 200 250 30020
25
30
35
40
45
50
55
C-C PID
C-C PI
Tuning Relations
Cohen-Coon: Regulatory
Highly oscillatoryVery aggressive
0 50 100 150 200 250 3005
10
15
20
25
30
35
40
C-C PI
C-C PID
y(t)
t
109
Integral Error Relations
1. Integral of absolute error (IAE)
2. Integral of squared error (ISE)
penalizes large errors
3. Integral of time-weighted absolute error (ITAE)
penalizes errors that persist
ITAE is most conservativeITAE is preferred
ISE e t dt= ∫∞
( )2
0
ITAE t e t dt= ∫∞
( )0
IAE e t dt= ∫∞
( )0
ITAE Relations
Choose Kc, τI and τd that minimize the ITAE:
For a first order plus dead time model, solve for:
Design for Load and Setpoint changes yield different ITAE optimum
∂∂
∂∂τ
∂∂τ
ITAEK
ITAE ITAEc I d
= = =0 0 0, ,
Type ofInput
Type ofController
Mode A B
Load PI P 0.859 -0.977I 0.674 -0.680
Load PID P 1.357 -0.947I 0.842 -0.738D 0.381 0.995
Set point PI P 0.586 -0.916I 1.03 -0.165
Set point PID P 0.965 -0.85I 0.796 -0.1465D 0.308 0.929
110
ITAE Relations
From table, we getLoad Settings:
Setpoint Settings:
Example
( ) ( )Y A KK A BB
c Id= = = = +θ
ττ
τθ
ττ
τ ,
( )Y A KKB
c Id= = = =θ
ττ
ττ
τ
G es
Gss
L=+
=−0 3
30 11
9. ,
ITAE Relations
Example (contd)Setpoint Settings
Load Settings:
( )KK
K K
c
c
= =
= = =
−1357 4 2437
4 2437 4 24370 3 1415
930
0 947. .
. .. .
.
( )
( )
ττ
τ τ
ττ
τ τ
I
I
d
d
= =
= = =
= =
= =
−0842 2 0474
2 047430
2 0474 14 65
0 381 930 01150
01150 34497
930
0 738
0 995
. .
. . .
. .
. .
.
.
( )KK
K K
c
c
= =
= = =
−0 965 2 6852
2 6852 2 68520 3 8 95
930
0 85. .
. .. .
.
( )
( )
ττ
τ τ
ττ
τ τ
I
I
d
d
= − =
= = =
= =
= =
0 796 01465 0 7520
0 752030
0 7520 39 89
0 308 930 01006
01006 30194
930
0 929
. . .
. . .
. .
. .
.
111
ITAE Relations
Servo Response
design for load changes yields large overshoots for set-point changes
0 50 100 150 200 250 30020
25
30
35
40
45
50
55
60
ITAE(Load)
ITAE(Setpoint)
ITAE Relations
Regulatory response
Tuning relations are based GL=Gp
Method does not apply to the processSet-point design has a good performance for this case
0 50 100 150 200 250 3000
5
10
15
20
25
30
35
40
ITAE(Setpoint)
ITAE(Load)
112
Tuning Relations
In all correlations, controller gain should be inversely proportional to process gain
Controller gain is reduced when derivative action is introduced
Controller gain is reduced as increases
Integral time constant and derivative constant should increase as increases
In general,
Ziegler-Nichols and Cohen-Coon tuning relations yield aggressive control with oscillatory response (requires detuning)
ITAE provides conservative performance (not aggressive)
θτ
θτ
ττ
dI
= 0 25.
CHE 446Process Dynamics and Control
Frequency Response ofFrequency Response ofLinear Control SystemsLinear Control Systems
113
First order ProcessFirst order Process
Response to a sinusoidal input signal
Recall: Sinusoidal input Asin(ωt) yields sinusoidal output caharacterized by AR and φ
0 2 4 6 8 10 12 14 16 18 20-1.5
-1
-0.5
0
0.5
1
1.5
2
AR
φ
y(t)/
A
t/τ
[ ]{ }lim ( ) sin( )t
PY s K A t→ ∞
−ℑ =+
+12 21 τ ω
ω φ
First order ProcessesFirst order Processes
10-2
10-1
100
101
102
10-2
10-1
100
AR
/Kp
τpω
Bode Plots
10-2
10-1
100
101
102
-100
-80
-60
-40
-20
0
φ
τpω
High Frequency
AsymptoteCorner Frequency
AR K=+1 2 2τ ω
φ ωτ= − −tan ( )1
Amplitude Ratio Phase Shift
114
Second Order ProcessSecond Order Process
Sinusoidal ResponseSinusoidal Response
wherewhere
Y sK
s sA
sp( ) =
+ + +τ ξτ
ω
ω2 2 2 22 1
( )[ ] ( )y t
K Atp( ) sin( )=
− +
+
1 22 2 2ωτ ξτω φ
φ ξωτ
ωτ= −
−
⎡
⎣⎢⎢
⎤
⎦⎥⎥
−tan( )
12
21
( )[ ] ( )ARn =
− +
1
1 22 2 2ωτ ξωτ
Second Order ProcessesSecond Order Processes
Bode Plot
10-1 100 10110-1
100
101
10-1 100 101
-150
-100
-50
0
ξ=1
ξ=0.1
ξ=1
ξ=0.1
Amplitude reachesa maximum at
resonance frequencyARφ
ω
115
Frequency Response
Q: Do we “have to” take the Laplace inverse to compute the AR and phase shift of a 1st or 2nd order process?
No
Q: Does this generalize to all transfer function models?
Yes
Study of transfer function model response to sinusoidal inputs is called “Frequency Domain Response” of linear processes.
Frequency Response
Some facts for complex number theory:
i) For a complex number:
It follows that where
such that
w a bj= +
a w b w= =Re( ), Im( )
Re
Im
w
θa
b
a w b w= =cos( ), sin( )θ θ
w w w= +Re( ) Im( )2 2 θ = = ⎛⎝⎜
⎞⎠⎟
−arg( ) tan Im( )Re( )
w ww
1
w we j= θ
116
Frequency Response
Some facts:ii) Let z=a-bj and w= a+bj then
iii) For a first order process
Let s=jω
such that
w z z w= = − and arg( ) arg( )
G sKs
p( ) =+τ 1
G jKj
jj
K Kjp p p( ) ( )
( )ω
τ ωτ ωτ ω τ ω
ωτ
τ ω=
+−−
=+
−+1
11 1 12 2 2 2
G jK
AR
G j
p( ) ( )
arg( ( )) tan ( )(
ωτ ω
ω ωτ
=+
=
= − =−1 2 2
1 Phase Lag)
Frequency Response
Main Result:
The response of any linear process G(s) to a sinusoidal input is a sinusoidal.
The amplitude ratio of the resulting signal is given by the Modulus of the transfer function model expressed in the frequency domain, G(iω).
The Phase Shift is given by the argument of the transfer function model in the frequency domain.
i.e.
AR G j G j G j
G jG j
= = +
= = ⎛⎝⎜
⎞⎠⎟
−
( ) Re( ( )) Im( ( ))
tan Im( ( ))Re( ( ))
ω ω ω
ϕ ωω
2 2
1Phase Angle
117
Frequency ResponseFrequency Response
For a general transfer function
Frequency Response summarized by
where is the modulus of G(jω) and ϕis the argument of G(jω)
Note: Substitute for s=jω in the transfer function.
G s r sq s
e s z s zs p s p
sm
n( ) ( )
( )( ) ( )
( ) ( )= = − −
− −
−θ1
1
ΛΛ
G j G j e j( ) ( )ω ω ϕ=
G j( )ω
Frequency Response
The facts:
For any linear process we can calculate the amplitude ratio and phase shift by:
i) Letting s=jω in the transfer functionG(s)
ii) G(jω) is a complex number. Its modulus is the amplitude ratio of the process and its argument is the phase shift.
iii) As ω, the frequency, is varied that G(jω) gives a trace (or a curve) in the complex plane.
iv) The effect of the frequency, ω, on the process is the frequency response of the process.
118
Frequency Response
Examples:
1. Pure Capacitive Process G(s)=1/s
2. Dead Time G(s)=e-θs
G j Kj
jj
K j( )ωω
ωω ω
=−−
⎛⎝⎜
⎞⎠⎟
= −
AR K K= =
−⎛⎝⎜
⎞⎠⎟
= −−ω
φ ω π, tan /10 2
G j e j( )ω ωθ= −
AR = = −1, φ ωθ
Frequency Response
Examples:
3. n process in series
Frequency response of G(s)
therefore
G s G s G sn( ) ( ) ( )= 1 Λ
G j G j G j
G j e G j en
jn
j n
( ) ( ) ( )
( ) ( )
ω ω ω
ω ωφ φ
=
=
1
1 1
Λ
Λ
AR G j G j
G j G j
ii
n
ii
ni
i
n
= = ∏
= = =∑ ∑
=
= =
( ) ( )
arg( ( )) arg( ( ))
ω ω
φ ω ω φ
1
1 1
119
Frequency Response
Examples.
4. n first order processes in series
5. First order plus delay
G s Ks
Ks
n
n( ) =
+ +1
1 1 1τ τΛ
( ) ( )
AR K Kn
n
n
=+ +
= − − −− −
1
12 2 2 2
11
1
1 1τ ω τ ω
φ ωτ ωτ
Λ
Λtan tan
G sK e
sp
s( ) =
+
−θ
τ 1
ARKp=+
= − −−( ), tan ( )
1
1 2 21
τ ωφ ωτ θω
Frequency Response
To study frequency response, we use two types of graphical representations
1. The Bode Plot:Plot of AR vs. ω on loglog scalePlot of φ vs. ω on semilog scale
2. The Nyquist Plot:Plot of the trace of G(jω) in the complex plane
Plots lead to effective stability criteria and frequency-based design methods
120
Bode Plot
AR K= = −ω
ϕ π2
10-2 10-1 100100
101
102
AR
10-2 10-1 100-91
-90.5
-90
-89.5
-89
Frequency (rad/sec)
Pha
se A
ngle
Pure Capacitive Process
Bode Plot
G s G s G s G s( ) ( ) ( ) ( )= 1 2 3
G ss
G ss
G ss1 2 3
110 1
15 1
11
( ) , ( ) , ( )=+
=+
=+
G j( )( )( )( )
tan ( ) tan ( ) tan ( )
ωω ω ω
ϕ ω ω ω
=+ + +
= − − −− − −
1
1 10 1 5 1 1
10 5
2 2 2 2 2 2
1 1 1
10-4 10-3 10-2 10-1 100 10110-4
10-2
100
10-4 10-3 10-2 10-1 100 101-300
-200
-100
0
G1
G2
G3
G
121
Bode Plot
Example: Effect of dead-time
G s e s( ) = −θ
G j( ) ,ω ϕ θ ω= = −1
G s e G s G s G sds( ) ( ) ( ) ( )= −2
1 2 3
10-4 10-3 10-2 10-1 100 10110-4
10-2
100
10-4 10-3 10-2 10-1 100 101
-300
-200
-100
0
G=Gd
GGd
Nyquist Plot
Plot of G(jω) in the complex plane as ω is varied
Relation to Bode plot
AR is distance of G(jω) for the originPhase angle, ϕ , is the angle from the Real positive axis
Example First order process (K=1, τ=1)
ϕ
G j( )ω
122
Nyquist Plot
DeadDead--timetime
Second OrderSecond Order
ω
ξ ≥ 1
ξ < 1
Nyquist Plot
Third Order
Effect of dead-time (second order process)
G ss s
Gd s e s( ) , ( )=+ +
= −12 3 1
2
G ss s s
( ) =+ + +
13 3 13 2
123
Che 446: Process Dynamics andControl
Frequency DomainController Design
PI Controller
AR KcI
I
= +
= −−
1 1
1
2 2
1
ω τ
ϕ ωτtan ( / )
10-3 10-2 10-1 100 101100
101
102
103
10-3 10-2 10-1 100 101-100
-80
-60
-40
-20
0
AR
ϕ
ω
124
PID Controller
AR Kc DI
DI
= −⎛⎝⎜
⎞⎠⎟ +
= −⎛⎝⎜
⎞⎠⎟−
ωτωτ
ϕ ωτωτ
1 1
1
2
1tan
10-3 10-2 10-1 100 101100
101
102
103
10-3 10-2 10-1 100 101-100
-50
0
50
100
AR
ϕ
ω
Bode Stability Criterion
Consider open-loop control system
1. Introduce sinusoidal input in setpoint (D(s)=0) and observe sinusoidal output
2. Fix gain such AR=1 and input frequency such that φ=-180
3. At same time, connect close the loop and set R(s)=0
Q: What happens if AR>1?
GpGc
Gs
D(s)
Y(s)
Ym(s)
R(s)
U(s)
Open-loop Response to R(s)
+-++
125
Bode Stability Criterion
“A closed-loop system is unstable if the frequency of the response of the open-loop GOL has an amplitude ratio greater than one at the critical frequency. Otherwise it is stable. “
Strategy:
1. Solve for ω in
2. Calculate AR
arg( ( ))G jOL ω π= −
AR G jOL= ( )ω
Bode Stability Criterion
To check for stability:
1. Compute open-loop transfer function2. Solve for ω in φ=-π3. Evaluate AR at ω4. If AR>1 then process is unstable
Find ultimate gain:
1. Compute open-loop transfer function without controller gain2. Solve for ω in φ=-π3. Evaluate AR at ω4. Let
KARcu =1
126
Bode Criterion
Consider the transfer function and controller
- Open-loop transfer function
- Amplitude ratio and phase shift
- At ω=1.4128, φ=-π, AR=6.746
G s es s
s( )
( )( . )
.=
+ +
−51 05 1
01G s
sc ( ) ..
= +⎛⎝⎜
⎞⎠⎟
0 4 1 101
G s es s sOL
s( )
( )( . ).
.
.=
+ ++⎛
⎝⎜⎞⎠⎟
−51 05 1
0 4 1 101
01
AR =+ +
+
= − − − − ⎛⎝⎜
⎞⎠⎟
− − −
5
1
1
1 0 250 4 1 1
0 01
01 05 101
2 2 2
1 1 1
ω ω ω
φ ω ω ωω
..
.
. tan ( ) tan ( . ) tan.
Ziegler-Nichols Tuning
Closed-loop tuning relation
With P-only, vary controller gain until system (initially stable) starts to oscillate.Frequency of oscillation is ωc,
Ultimate gain, Ku, is 1/M where M is the amplitude of the open-loop systemUltimate Period
Ziegler-Nichols Tunings
P Ku/2PI Ku/2.2 Pu/1.2PID Ku/1.7 Pu/2 Pu/8
Puc
=2πω
127
Nyquist Stability Criterion
“If N is the number of times that the Nyquist plot encircles the point (-1,0) in the complex plane in the clockwise direction, and P is the number of open-loop poles of GOL that lie in the right-half plane, then Z=N+P is the number of unstable roots of the closed-loop characteristic equation.”
Strategy
1. Substitute s=jω in GOL(s)2. Plot GOL(jω) in the complex plane3. Count encirclements of (-1,0) in the clockwise direction
Nyquist Criterion
Consider the transfer function
and the PI controller
G s es s
s( )
( )( . )
.=
+ +
−51 05 1
01
G ssc ( ) .
.= +⎛
⎝⎜⎞⎠⎟
0 4 1 101
128
Stability Considerations
Control is about stability
Considered exponential stability of controlled processes using:
Routh criterionDirect SubstitutionRoot LocusBode Criterion (Restriction on phse angle)Nyquist Criterion
Nyquist is most general but sometimes difficult to interpret
Roots, Bode and Nyquist all in MATLAB
MAPLE is recommended for some applications.
Polynomial (no dead-time)
CHE 446Process Dynamics and
Control
Advanced Control Techniques:Advanced Control Techniques:1. 1. Feedforward Feedforward ControlControl
129
Feedforward Control
Feedback control systems have the general form:
where UR(s) is an input bias term.
Feedback controllersoutput of process must change before any action is takendisturbances only compensated after they affect the process
GpGc
Gs
Y(s)
Ym(s)
R(s)
U(s)+
+ +GD
Gv+ +
D(s)
UR(s)
Feedforward Control
Assume that D(s)can be measured before it affects the processeffect of disturbance on process can be described with a model GD(s)
Feedforward Control is possible.
Feedback/Feedforward ControllerStructure
GpGc
Gs
Y(s)
Ym(s)
R(s)
U(s)+
+ +GD
Gv
Gf
+ +
D(s)
FeedforwardController
130
Feedforward Control
Heated Stirred Tank
Is this control configuration feedback or feedforward?How can we use the inlet stream thermocouple to regulate the inlet folow disturbancesWill this become a feedforward or feedback controller?
TTTC1
Ps
Condensate
Steam
F,Tin
F,T
TT
Feedforward Control
A suggestion:
How do we design TC2?
TT
TC1Ps
Condensate
Steam
F,Tin
F,T
TC2
++TT
131
Feedforward Control
The feedforward controller:
Transfer Function
Tracking of YR requires that
Gp Y(s)U(s) ++
GD
Gv
Gf
+ +
D(s)
UR(s)
Y s G s D s G s G s U sY s G s D s G s G s U s G s D s
Y s G s G s G s G s D s G s G s U s
Y s G s G s G s G s D s Y s
D P v
D P v R f
D p v f p v R
D p v f R
( ) ( ) ( ) ( ) ( ) ( )( ) ( ) ( ) ( ) ( )( ( ) ( ) ( ))
( ) ( ( ) ( ) ( ) ( )) ( ) ( ) ( ) ( )
( ) ( ( ) ( ) ( ) ( )) ( ) ( )
= += + +
= + +
= + +
G s G s G s G s
G s G sG s G s
D p v f
fD
p v
( ) ( ) ( ) ( )
( ) ( )( ) ( )
+ =
⇒ = −
0
Feedforward Control
Ideal feedforward controller:
Exact cancellation requires perfect plant and perfect disturbance models.
Feedforward controllers:very sensitive to modeling errorscannot handle unmeasured disturbancescannot implement setpoint changes
Need feedback control to make control system more robust
G s G sG s G sf
Dp v
( ) ( )( ) ( )
= −
G s G s G s G sD p v f( ) ( ) ( ) ( )+ ≠ 0
132
Feedforward Feedforward ControlControl
GpGc
Gs
Y(s)
Ym(s)
R(s)
U(s)+
+ +GD
Gv
Gf
+ +
D(s)
What is the impact of Gf on the closed-loopperformance of the feedback control system?
Feedback/Feedforward Control
Feedforward Control
Regulatory transfer function of feedforward/feedback loop
Perfect control requires that (as above)
Note:Feedforward controllers do not affect closed-loop stabilityFeedforward controllers based on plant models can be unrealizable (dead-time or RHP zeroes)Can be approximated by a lead-lag unit or pure gain (rare)
C sD s
G s G s G s G sG s G s G s G s
D f v p
c v p m
( )( )
( ) ( ) ( ) ( )( ) ( ) ( ) ( )
=+
+1
G s G sG s G sf
D
v p( ) ( )
( ) ( )= −
G s K ssf f( ) ( )
( )=
++
ττ
1
2
11
G s KK Kf
D
v p( ) = −
133
Feedforward Control
Tuning: In absence of disturbance model lead-lag approximation may be good
Kf obtained from open-loop data
− τ1 and τ2
from open-loop data
from heuristics
Trial-and-error
G s K ssf f( ) ( )
( )=
++
ττ
1
2
11
K KK Kf
D
v p= −
τ τ τ τ1 2= =p D,
ττ
ττ
1
2
1
205 2 0= =. .
τ τ1 2− = c
Feedforward Control
Example:
Plant:
Plant Model:
Feedback Design from plant model: IMC PID tunings
G ss s s
G ss s
p
D
( )( )( )( )
( )( . )( )
=+ + +
=+ +
1010 1 5 1 1
12 5 1 1
G s es
G s espm
sDm
s( ) ( )
.=
+=
+
− −1010 1 2 5 1
6
Kc I D= = =0 26 13 2 31. , , .τ τ
134
Feedforward Control
Possible Feedforward controllers:
1. From plant models:
Not realizable
2. Lead-lag unit
3. Feedforward gain controller:
G s e ssf
s( ) ( )
( . )= − +
+
5
1010 12 5 1
τ τ1 210 2 51
10
= =
= −
, .
K f
K f = − 110
Feedforward Control
For Controller 2 and 3
Some attenuation observed at first peakDifficult problem because disturbance dynamic are much faster
0 20 40 60 80 100 120 140 160 180 200-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2Disturbance Controller with Feedforward
.. - Gain Controller
-- - Lead-Lag Controller
- - No FF Controller
135
Feedforward Feedforward ControlControl
Useful in manufacturing environments if good models are available
outdoor temperature dependencies can be handle by gain feedforward controllersscheduling issues/ supply requirements can be handled
Benefits are directly related to model accuracy
rely mainly on feedback control
Disturbances with different dynamics always difficult to attenuate with PID
may need advanced feedback control approach (MPC, DMC, QDMC, H -controllers, etc…)
Use process knowledge (and intuition)
CHE 446:Process Dynamics and
Control
Advanced Control Techniques2. Cascade Control
136
Cascade Control
Jacketed Reactor:
Conventional Feedback Loop:operate valve to control steam flowsteam flow disturbances must propagate through entire process to affect outputdoes not take into account flow measurement
TTTC1
Ps
Condensate
Steam
F,Tin
F,T
TT
FT
Cascade Control
Consider cascade control structure:
Note:TC1 calculates setpoint cascaded to the flow controllerFlow controller attenuates the effect of steam flow disturbances
TTTC1
Ps
Condensate
Steam
F,Tin
F,T
TT
FTFC
137
Cascade Control
Cascade systems contain two feedback loops:
Primary Loopregulates part of the process having slowerdynamicscalculates setpoint for the secondary loope.g. outlet temperature controller for the jacketed reactor
Secondary Loopregulates part of process having fasterdynamicsmaintain secondary variable at the desired target given by primary controllere.g. steam flow control for the jacketed reactor example
Cascade Control
Blo
ck D
iagr
am G
c2G
p1G
p2G
v2
Gm
2
Gm
1
Gc1
D1
D2
--
++
++
138
Cascade Control
Closed-loop transfer function
1. Inner loop
2. Outer loop
Characteristic equation
CR
G G GG G G G
Gp v c
p v c mcl
2
2
2 2 2
2 2 2 221
=+
=
CR
G G GG G G G
p cl c
p cl c m11
1 2 1
1 2 1 11=
+
1 0
11
0
1 0
1 2 1 1
12 2 2
2 2 2 21 1
2 2 2 2 1 2 2 2 1 1
+ =
++
=
+ + =
G G G G
GG G G
G G G GG G
G G G G G G G G G G
p cl c m
pp v c
p v c mc m
p v c m p p v c c m
Cascade Control
1 02 2 2 2 1 2 2 2 1 1+ + =G G G G G G G G G Gp v c m p p v c c mStability of closed-loop process is governed by
Example
GKs
G K G G
GKs
G K G
pp
c c v m
pp
c c m
11
11 1 1 1
22
22 2 2
11
11
=+
= = =
=+
= =
τ
τ
, ,
, ,
11 1 1
022
21
2
2
1
1+
++
+ +=K
Ks
KKs
Ksc
pc
p pτ τ τ
( )( ) ( )τ τ τ1 2 2 2 1 1 2 11 1 1 0s s K K s K K Kc p c p p+ + + + + =
τ τ τ τ τ1 22
1 2 2 2 1 2 2
1 2 1
1
0
s K K s K K
K K Kc p c p
c p p
+ + + + + +
=
( )
139
Cascade Control
Design a cascade controller for the followingsystem:
1. Primary:
2. Secondary:
G s es s
G
G Kcs
ps
m
cI
101
1
1 1
05 1 11
1 1
( )( . )( )
, ,.
=+ +
=
= +⎛⎝⎜
⎞⎠⎟
−
τ
Gs
G G
G K
p v m
c c
2 2 2
2 2
101 1
1=+
= =
=.
,
Cascade Control
1.1. PI controller only
Critical frequency
Maximum gain
G Ks s
es sOL c
s1 1
011 1 1
01 1 05 1 1= +⎛
⎝⎜⎞⎠⎟ + + +
−
. ( . )( )
.
AR Kc= ++ + +
1 2 2 2 21 1 1
0 01 1
1
0 25 1
1
1ω ω ω ω. .
ϕω
ω
ω ω ω
= − ⎛⎝⎜
⎞⎠⎟
−
− − −
− −
− −
tan tan ( . )
tan ( . ) tan ( ) .
1 1
1 1
1 01
05 01
ωc AR= =2 99 0178. , .
Kc1 561= .
140
AR
ϕ
Bode Plots
Cascade Control
ϕ
ln(ω)
Cascade Control
2. Cascade Control
Secondary loop
no critical frequency gain can be largeLet Kc2=10.
Primary loop
G KsOl c2 21
01 1=
+.
G Ks
s
s
es s
K e
s s s
OL cs
cs
1 101
101
1 110
01 11 10 1
01 105 1 1
1011 05 1 01
111
= +⎛⎝⎜
⎞⎠⎟
+
++
+ +
=+ +
−
−
.
.( . )( )
( . )( . )
.
.
141
Cascade Control
Closed-loop stability:
Bode
Maximum gain Kc1=10.44Secondary loop stabilizes the primary loop.
ωc AR= =413 0 0958. , .
ARKc1 2
22
1 1
1011
1 1
1 0111
1
1 0 25
201 01
1105
=
+ ⎛⎝⎜
⎞⎠⎟
+
= − − − ⎛⎝⎜
⎞⎠⎟
−− −
ωω
ω
φ π ω ω ω
. .
. tan . tan ( . )
Cascade Control
Use cascade when:conventional feedback loop is too slow at rejecting disturbancessecondary measured variable is available which
responds to disturbanceshas dynamics that are much faster than those of the primary variablecan be affected by the manipulated variable
Implementation
tune secondary loop firstoperation of two interacting controllers requires more careful implementation
switching on and off
142
CHE 446Process Dynamics and Control
Advanced Control Techniques3. Dead-time Compensation
Dead-time Compensation
Consider feedback loop:
Dead-time has a de-stabilizing effect on closed-loop systemPresence of dead-time requires detuning of controllerNeed a way to compensate for dead-time explicitly
Gc Gp e-θsR C
D
143
Dead-time Compensation
Motivation
G s es s
G ss
s
c
( ) , . .
( )
=+ +
≤ ≤
= +⎛⎝⎜
⎞⎠⎟
−θθ2 3 2
01 0 75
4 1 1
0.10.75 0.5 0.25
Dead-time Compensation
Use plant model to predict deviation from setpoint
Result:Removes the de-stabilizing effect of dead-time
Problem:Cannot compensate for disturbances with just feedback (possible offset)Need a very good plant model
Gc Gp e-θsR C
D
Gpm
144
Dead-time Compensation
Closed-loop transfer function
Characteristic Equation becomes
Effect of dead-time on closed-loop stability is removedController is tuned to stabilize undelayed process modelNo disturbance rejection
C sD s
C sR s
G G eG G
c ps
c pm
( )( )
, ( )( )
= =+
−1
1
θ
1 0+ =G Gc pm
Dead-time Compensation
0 1 2 3 4 5 6 7 8 9 100
0.5
1
1.5Servo Response
0 1 2 3 4 5 6 7 8 9 100
0.2
0.4
0.6
0.8
1Regulatory Response
e-0.5sR C
4 1 1+⎛
⎝⎜⎞⎠⎟s
13 22s s+ +
13 22s s+ +
D
145
Dead-time Compensation
Include effect of disturbances using model predictions
Adding this to previous loop gives
∃( ) ( ) ∃( )
∃( ) ( ) ( )∃
D s Y s Y s
D s G e U s G e U sps
pms
= −
= −− −θ θ
Gc Gp e-θsR C
D
Gpm Gpm e-θs
+
+
+-
++
+ -
Dead-time Compensation
Closed-loop transfer function
Characteristic Equation
Effect of dead-time on stability is removed Disturbance rejection is achievedController tuned for undelayed dynamics
C sD s
e e G G
G G G G e G e
C sR s
G G e
G G G G e G e
s sc pm
c pm c ps
pms
c ps
c pm c ps
pms
( )( )
( )
( )
( )( ) ( )
∃
∃
∃
=+ −
+ + −
=+ + −
− −
− −
−
− −
1
1
1
θ θ
θ θ
θ
θ θ
1 0+ + − =− −G G G G e G ec pm c ps
pms( )
∃θ θ
Fast Dynamics
SlowDynamics
146
Dead-time Compensation
e-0.5sR C
4 1 1+⎛
⎝⎜⎞⎠⎟s
13 22s s+ +
13 22s s+ +
D
e-0.5s13 22s s+ +
+
+
+-
∃( )D s
+
+
+ -
0 1 2 3 4 5 6 7 8 9 100
0.5
1
1.5Servo Response
0 1 2 3 4 5 6 7 8 9 10-0.5
0
0.5
1Regulatory Response
Dead-time Compensation
Alternative form
Reduces to classical feedback control system with
called a Smith-Predictor
Gc Gp e-θsR C
D
Gpm(1-e-θs)
+
+
+
+
+ -
G s G sG e
cc
pmsm
*( ) ( )( )
=+ − −1 1 θ
147
Dead-time compensation
Smith-Predictor Design
1. Determine delayed process model
2. Tune controller Gc for the undelayed transfer function model Gpm
3. Implement Smith-Predictor as
4. Perform simulation studies to tune controller and estimate closed-loop performance over a range of modeling errors (Gpm and θm)
∃( ) ( )Y s G s epmsm= −θ
G s G sG e
cc
pmsm
*( ) ( )( )
=+ − −1 1 θ
Dead-time Compensation
Effect of dead-time estimation errors:
e-0.5sR C
4 1 1+⎛⎝⎜
⎞⎠⎟s
13 22s s+ +
13 22s s+ +
D
e-τs13 22s s+ +
+
+
+-
∃( )D s
+
+
+ -
τ