Advances in PID control for processes with constraints · processes with constraints Prof. Julio...

Advances in PID control for processes with constraints

Prof. Julio Elias Normey-Rico

Automation and Systems Department

Federal University of Santa Catarina - Brazil

IFAC WC 2020 – Berlin – July 2018

2020 IFAC WORLD CONGRESS

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Process control

Manipulated variables (MV) are constrained

• Amplitude constraints

• Slew-rate constraints

Process variables (PV) need to be constrained

• Operational limits

• Safety

Most used in industry: PID + Saturation blocks in MV + AWP

Actuator limitations

Process operation

Use new ideas to improve PID AWP performance

/26Advances in PID control for processes with constraints

Agenda

1. Motivating example

2. PID control review and wind-up

3. PID control with AWP – Proposed solution

4. Conclusions

3

CSPS - Constrained SISO Process Simulation tool

http://rodolfoflesch.prof.ufsc.br/cspstool

Simulation

tool

http://rodolfoflesch.prof.ufsc.br/cspstool

Motivating example

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Motivating examplePID tuning

PID control with AWPConclusions

Advances in PID control for processes with constraints 5

Ex1-Simple model with constraints

Simple model with delay

Several closed-loop simulations:

- PID tuning

- No constraints

- Constraints – no AWP

- Constraints – classical AWP

- Constraints – Improved AWP

Umax = 0.2 Umin =-0.2

Dumax=0.2

Ymax =1

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Ex1-Simple model with constraints

PID tuning (without constraints)

PID (with saturations)

Saturation MV limits

PID (with AWP)

PID with improved AWP(includes PVConstraints)

PID tuning

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Unified PID formulation

PID tuning: low frequency approximation of the Filtered Smith Predictor


Process models: FOPDT, IPDT, UFOPDT

PID form: Filtered derivative action

Tuning parameter = closed-loop time constant To (trade-off)

ROBUSTNESSTo PERFORMANCE

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Discrete controller – proper sample-time selection Ts

Discrete controller


Parallel form

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Discrete controller


Compact form

PID control with AWP

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Saturation problems

ui(k) only decrease when e(k)

changes its signal

But, as ui(k) >> ur(k), needs some

time to go out from saturation

Causes WIND UP

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Anti Windup techniques

No tuning parameter

13

Incremental algorithm

code update:

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Tuning parameter

14

Back-calculation

Ud

Ui

Up

AWP action

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Error Recalculation (ER)

Recalculate the error signal at every sample to maintain the consistence between u(k) (computed) and ur(k) (applied)

15

Used in the code to

update the error:

e(k-1)=e*(k)

?


No tuning parameter

Consider:

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Comparing AWP methods

Simple example using the simulation tool

16

Ex2- Anti Windup techniques

• Simulation for a 0,95 step

• PID tuning with To=0,5

• Only MV saturation Umx=1, Umin =0.

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Improved AWP

17

Typical process constraints consider other variables

Classical AWP works with simple saturation

Solution: To map the constraints to the manipulated variable!

How? → Using some prediction ideas (MPC concepts)

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Improved AWP

Mapping constraints (Max case)

Using model

18

Direct

Depends on past values of control action!

u(k) will affect the future values of y(k) Predictions!!!

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Improved AWP

MODEL

Predictions

Using prediction ideas

19

Horizon

We do not know the future and we are going to apply only u(k) at t=kTs

Process delay

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Improved AWP

Problem to be solved

20

A set of inequations to obtain u(k)

Final constraint

PID AWP control is then computed using this Usat (Repeat for min condition)

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Example:

Ex3- Improved AWP


Known past values

Max constraints

Known past values

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Ex3- Improved AWP


Past dataMin constraint

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Ex3- Improved AWP


Implementation equations for max and min values:

Apply the AWP scheme for PID control with

Simulation Tool Example

Conclusions

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Conclusions

• Using MPC ideas PID performance can be improved for process with constraints

• Improved AWP PID algorithms can be the solution in modern real-time distributed control systems for simple constrained systems

• Simulation tools are very important for analysis and design


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Thanks!

UFSC

[email protected]

IFAC 2020 organizers

For your attention


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ReferencesAstrom, K.J. and Hagglund, T. (1995). PID controllers: theory, design, and tuning.

Camacho, E.F. and Bordons, C. (2013). Model Predictive Control. Springer, London.

da Silva, L.R., Flesch, R.C.C., and Normey-Rico, J.E. (2018). Analysis of anti-windup techniques in PID control of

processes with measurement noise. In 3rd IFAC Conference on Advances in Proportional-Integral- Derivative Control,

948-953. Ghent, Belgium.

da Silva, L.R., Flesch, R.C.C., and Normey-Rico, J.E. (2019). Controlling industrial dead-time systems: When to use a

PID or an advanced controller. ISA Transactions. doi:10.1016/j.isatra.2019.09.008.

Fertik, H.A. and Ross, C.W. (1967). Direct digital control algorithm with anti-windup feature. ISA Transactions, 6(4), 317.

Flesch, R.C., Normey-Rico, J.E., and Flesch, C.A. (2017). A unified anti-windup strategy for SISO discrete deadtime

compensators. Control Engineering Practice, 69, 50-60.

Guzman, J.L., Astrom, K.J., Dormido, S., Hagglund, T., and Piguet, Y. (2006). Interactive learning modules for PID

control. IFAC Proceedings Volumes, 39(6), 7-12.

Hippe, P. (2006). Windup in Control: Its Effects and Their Prevention. Springer Science & Business Media.

Normey-Rico, J.E. and Camacho, E.F. (2007). Control of Dead-time Processes. Springer, London.

Normey-Rico, J.E. and Guzman, J.L. (2013). Unified PID tuning approach for stable, integrative, and unstable dead-time

processes. Industrial & Engineering Chemistry Research, 52, 16811-16819.

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