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7/29/2019 Mecha Intro
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Mechatronics at theUniversity of Calgary:Concepts and Applications
Jeff Pieper
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Outline Mechatronics
Past and Present Research Results
Undergraduate Program
Directions for the Future
Summary
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What is Mechatronics? Synergistic combination of mechanics,
electronics, microprocessors and control
engineering Like concurrent engineering
Does not take advantage of inherent uniquenessavailable
Control of complex electro-mechanicalsystems Rethinking of machine component design by use
of mechanics, electronics and computation
Allows more reconfigurablility
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What is Mechatronics Design example: timing belt in
automobile
Timing belt mechanically synchronizesand supervises operation
Mechatronics: replace timing belt with
software
Allows reconfigurability online
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What is Mechatronics Second example: Active suspension
Design of suspension to achievedifferent characteristics under differentoperating environments
Demonstrates fundamental trade-offs
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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
*
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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
*
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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
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What is control? Nominal performance
Servo tracking
Disturbance rejection
Low sensitivity
Minimal effects of unknown aspects
Non-time-varying
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Feedback Control *
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Discrete Time Sliding Mode
Control Comprehensive evaluation of theoretical
methodology
Optimization, maximum robustness
Applications: Web tension system
Gantry crane
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Web Tension System
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Gantry Crane *
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Helicopter Flight Control Experimental Model Validation
Model-following control design
Experimental Flight Control
Multivariable
Start-up and safety
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Helicopter Flight Control
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Helicopter Flight Control Model-following vs. stability
Conflicting Multi-objective
Controller Optimization
Order reduction
System validation
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Helicopter Flight Control *
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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
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OHS Aircraft *
0 1 2 3 4
-4
-2
0
2
4
Airspeed(m/s)
0 1 2 3 4
-25
-20
-15
-10
-5
0
5
10
15
20
25
AngleofAttack(deg)
0 1 2 3 4
-30
-20
-10
0
10
20
30
Pitch(deg)
0 1 2 3 4
-100
-75
-50
-25
0
25
50
75
100
PitchRate(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)
Altitude(m)
0 1 2 3 4
-10
-8
-6
-4
-2
0
2
4
6
8
10
ElevatorDeflection(deg)
15 m/s20 m/s
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Magnetic Bearings in
Papermaking Systems System identification for magnetic
bearings
Nonlinear, unstable system
Closed loop modeling
Control Design using Sliding Modemethods and state estimators
Servo-Control of shaft position
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Magnetic Bearings
20 50 100 200 500 1000 2500-60
-50
-40
-30
-20
-10
0
10
20
Frequency (Hz)
Magnitude(dB)
Yhl/U hl
20 50 100 200 500 1000 2500
-50
0
50
100
150
200
250
Frequency (Hz)
Phase(deg)
20 50 100 200 500 1000 2500-60
-50
-40
-30
-20
-10
0
10
20
Frequency (Hz)
Magnitude(dB)
Yhr/U hl
20 50 100 200 500 1000 2500
-50
0
50
100
150
200
250
Frequency (Hz)
Phase(deg)
0 5 10 15 20 25-3
-2
-1
0
1
2
Uh
l
0 5 10 15 20 25-0.5
0
0.5
1
1.5
Yhl
0 5 10 15 20 25-3
-2
-1
0
1
2
Uh
r
Time (mS)
0 5 10 15 20 25-1
-0.5
0
0.5
1
Yhr
Time (mS)
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Magnetic Bearings in
Papermaking Systems Papermaking system modeling
Use of mag bearings for tension control
actuation and model development
Control Design and implementationissues
Compare performance with standardroller torque control
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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-infinithnormo
ftransfer
function
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
TheH-infinitynormo
ftransferfunction
Cross-sectional Area of web material(in.2)
*
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Robust Control
via Q-parameterization
Ball and beam application
Stability margin optimization: Nevanlinna-Pick
interpolation
x
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Experimental Results Large lag
Non-minimum phase
behaviour Friction, hard limits
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Robust Stability Margin *Delay Nominal Optimal
0 -0.27 -0.300.1 -0.26 -0.280.2 -0.19 -0.220.3 -0.07 -0.11
0.4 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
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Process Control and System ID:Quadruple-tank Process Diagram
PUMP1 PUMP2
Tank1L1
Tank3L3
Tank2
L2
Tank4
L4
Out1Out2 Out1Out2
G11
G12
G21
G22
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System ID: Model Fitting
Tank2 process model
with input u1
Tank4 process model
with input u1
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Control Techniques Classic PI control
Internal Model Control (IMC) Model Predictive Control (MPC)
Linear Quadratic Regulator (LQR)
Optimal Control
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MPC Control Results
Different set points changes to test MPCcontroller performance
Tank2 response Tank4 response
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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
Controllers
Parameters
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Controller Architecture Given conflicting and redundant
information
Design controller with best practicalbehaviour
Good nominal performance
Robust stability Implementable
*
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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
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Mechatronics Lab with Applications Multivariable fluid flow control system
Two-input-two-outputs
Variable dynamics
Model Predictive Control
Chemical Process Emulation
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Fluid Flow Control
Quanser Product
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Mechatronics Lab with Applications Ball and Beam system
Hierarchical control system
Motor position servo Ball position
Solve via Q-parameterization
Robust stability and optimal nominalperformance
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Ball and Beam System
Quanser Product
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Mechatronics Lab with Applications Heat Flow Apparatus
Unique design
Varying deadtime for challenging control
Demonstrate industrial control schemes
PID controllers
Deadtime compensators
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Heat Flow Apparatus *
Quanser product
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Directions for the Future Robust Performance
robust stability and nominal performancesimultaneously guaranteed
Multiobjective Control Design Systems that meet conflicting, and disparate
objectives
Performance evaluation for soft measures Fuzzy systems
Bottom line: Mechatronics *
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Summary Control applications in electro-
mechanical systems
Emphasis on usability
Complex problems from simple systems
Undergraduate program: hands-on
learning and dealing with systems formuser to end-effector