Mecha Intro

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

Documents

Mecha Intro