Post on 16-Dec-2015
Mechatronics at the University of Calgary:
Concepts and Applications
Jeff Pieper
Outline Mechatronics Past and Present Research Results Undergraduate Program Directions for the Future Summary
What is Mechatronics? Synergistic combination of mechanics,
electronics, microprocessors and control engineering Like concurrent engineering Does not take advantage of inherent
uniqueness available Control of complex electro-mechanical
systems Rethinking of machine component design by
use of mechanics, electronics and computation Allows more reconfigurablility
What is Mechatronics Design example: timing belt in
automobile Timing belt mechanically
synchronizes and supervises operation
Mechatronics: replace timing belt with software
Allows reconfigurability online
What is Mechatronics Second example: Active
suspension Design of suspension to achieve
different characteristics under different operating environments
Demonstrates fundamental trade-offs
What is Mechatronics Low tech, low cost, low performance: pure
mechanical elements: spring shock absorber Can use dynamic systems to find coefficients via
time or frequency domain mid tech, cost, performance: electronics: op amps,
RLC circuits, hydraulic actuators Typically achieve two operating regimes, e.g.
highway vs off-road High tech, cost , performance: microprocessor-
based online adjustment of parameters Infinitely adjustable performance
*
Research Projects Discrete Sliding Mode Control with Applications Helicopter Flight Control OHS Aircraft Flight Control Magnetic Bearings in Papermaking Systems Robust Control for Marginally stable systems Process Control and System ID Controller Architecture
*
Theme of research Control of
Uncertain Nonlinear Time-varying Industrially relevant Electro-mechanical systems
This is control applications side of mechatronics
Involves sensor, actuator, controller design
What is control? Nominal performance
Servo tracking Disturbance rejection
Low sensitivity Minimal effects of unknown aspects Non-time-varying
Feedback Control*
Discrete Time Sliding Mode Control
Comprehensive evaluation of theoretical methodology
Optimization, maximum robustness
Applications: Web tension system Gantry crane
Web Tension System
Gantry Crane*
Helicopter Flight Control Experimental Model Validation Model-following control design Experimental Flight Control
Multivariable Start-up and safety
Helicopter Flight Control
Helicopter Flight Control Model-following vs. stability
Conflicting Multi-objective
Controller Optimization Order reduction System validation
Helicopter Flight Control*
OHS Aircraft Flight Control Outboard Horizontal Stabilizer Non-intuitive to fly Developed sensors and actuators Model identification and validation Gain-scheduled adaptive control Reconfigurable controller based
upon feedback available
OHS Aircraft *
0 1 2 3 4 5
-4
-2
0
2
4V
eloc
ity (
m/s
)
0 1 2 3 4 5-25
-20
-15
-10
-5
0
5
10
15
20
25
Ang
le o
f Atta
ck (
deg)
0 1 2 3 4 5-30
-20
-10
0
10
20
30
Pitc
h (d
eg)
0 1 2 3 4 5
-100
-75
-50
-25
0
25
50
75
100
Pitc
h R
ate
(deg
/s)
0 1 2 3 4 5-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Time (s)Time (s)
Alti
tude
(m
)
0 1 2 3 4 5-10
-8
-6
-4
-2
0
2
4
6
8
10E
leva
tor
Def
lect
ion
0 1 2 3 4
-4
-2
0
2
4A
irspe
ed (
m/s
)
0 1 2 3 4-25
-20
-15
-10
-5
0
5
10
15
20
25
Ang
le o
f Atta
ck (
deg)
0 1 2 3 4-30
-20
-10
0
10
20
30
Pitc
h (d
eg)
0 1 2 3 4-100
-75
-50
-25
0
25
50
75
100
Pitc
h R
ate
(deg
/s)
0 1 2 3 4-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Time (s)Time (s)
Alti
tude
(m
)
0 1 2 3 4-10
-8
-6
-4
-2
0
2
4
6
8
10E
leva
tor
Def
lect
ion
(deg
)
15 m/s20 m/s
Magnetic Bearings in Papermaking Systems System identification for magnetic
bearings Nonlinear, unstable system Closed loop modeling Control Design using Sliding Mode
methods and state estimators Servo-Control of shaft position
Magnetic Bearings
Magnetic Bearings in Papermaking Systems Papermaking system modeling Use of mag bearings for tension
control actuation and model development
Control Design and implementation issues
Compare performance with standard roller torque control
Papermaking Tension Control
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50.9
0.95
1
1.05
1.1
1.15
1.2
1.25
1.3
1.35
1.4The H-infinity norm of different order apprx system
H-in
finith
nor
m o
f tra
nsfe
r fun
ctio
n
Cross-sectional Area of web material
2nd order approx 4th order approx 6th order approx 10th order approx
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6The H-infinity norm of transfer function of PID controller by changing velocity
The
H-in
finity
nor
m o
f tra
nsfe
r fun
ctio
n
Cross-sectional Area of web material(in.2)
*
Robust Control via Q-parameterization
Ball and beam application Stability margin optimization:
Nevanlinna-Pick interpolation
x
Experimental Results Large lag Non-minimum
phase behaviour Friction, hard
limits
Robust Stability Margin*
Delay Nominal Optimal0 -0.27 -0.300.1 -0.26 -0.280.2 -0.19 -0.220.3 -0.07 -0.110.4 0.06 0.010.5 0.17 0.100.6 0.26 0.180.7 0.34 0.240.8 0.39 0.290.9 0.44 0.331 0.48 0.371.1 0.51 0.401.2 0.54 0.421.3 0.56 0.441.4 0.58 0.461.5 0.60 0.47
Process Control and System ID: Quadruple-tank Process Diagram
PUMP1 PUMP2
Tank1 L1
Tank3 L3
Tank2 L2
Tank4 L4
Out1Out2 Out1Out2
G11
G12
G21
G22
System ID: Model Fitting
Tank2 process model with input u1
Tank4 process modelwith input u1
Control Techniques Classic PI control Internal Model Control (IMC) Model Predictive Control (MPC) Linear Quadratic Regulator
(LQR) Optimal Control
MPC Control Results
Different set points changes to test MPC controller performance
Tank2 response Tank4 response
Performance Comparison*
PI IMC MPC LQR
MP(%) 4 20 5 32
ts (sec) 31 99 50 210
ss e(%) 1.5 0 0 0
||e||2 2.7 0.8 0.45 0.3
||e|| 0.2 0.08 0.05 0.05
trc(sec) 34 102 47 76
ControllersParameters
Controller Architecture Given conflicting and redundant
information Design controller with best
practical behaviour Good nominal performance Robust stability Implementable
*
Undergraduate Program: Mechatronics Lab with Applications Developed lab environment for teaching and
research
Two courses: - linear systems, - prototyping
Hands-on work in: modeling
system identification
Sampled-data systems
Optical encoding
State estimation
Control design
Mechatronics Lab with Applications
Multivariable fluid flow control system Two-input-two-outputs Variable dynamics Model Predictive Control Chemical Process Emulation
Fluid Flow Control
Quanser Product
Mechatronics Lab with Applications
Ball and Beam system Hierarchical control system
Motor position servo Ball position
Solve via Q-parameterization Robust stability and optimal nominal
performance
Ball and Beam System
Quanser Product
Mechatronics Lab with Applications
Heat Flow Apparatus Unique design Varying deadtime for challenging
control Demonstrate industrial control
schemes PID controllers Deadtime compensators
Heat Flow Apparatus *
Quanser product
Directions for the Future Robust Performance
robust stability and nominal performance simultaneously guaranteed
Multiobjective Control Design Systems that meet conflicting, and disparate
objectives Performance evaluation for soft measures
Fuzzy systems
Bottom line: Mechatronics *
Summary Control applications in electro-
mechanical systems Emphasis on usability Complex problems from simple
systems Undergraduate program: hands-on
learning and dealing with systems form user to end-effector