modeling, simulation, and control of a real system robert throne electrical and computer engineering...
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![Page 1: Modeling, Simulation, and Control of a Real System Robert Throne Electrical and Computer Engineering Rose-Hulman Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062714/56649d5e5503460f94a3dd18/html5/thumbnails/1.jpg)
Modeling, Simulation, and Control of a Real System
Robert ThroneElectrical and Computer EngineeringRose-Hulman Institute of Technology
![Page 2: Modeling, Simulation, and Control of a Real System Robert Throne Electrical and Computer Engineering Rose-Hulman Institute of Technology](https://reader035.vdocuments.site/reader035/viewer/2022062714/56649d5e5503460f94a3dd18/html5/thumbnails/2.jpg)
Introduction
• Models of physical systems are widely used in undergraduate science and engineering education.
• Students erroneously believe even simple models are exact.
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Introduction
• Obtained ECP Model 210a rectilinear mass, spring, damper systems for use in both system dynamics and controls systems labs.
• Models for these systems are easy to develop and students have seen these types of models in a variety of courses.
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Introduction(mass, springs, and encoder)
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Introduction(motor, rack and pinion, damper, and spring
connecting to first cart)
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Introduction
We developed four groups of labs for the ECE introductory controls class for a one degree of freedom system:
• Time domain system identification.• Frequency domain system identification.• Closed loop plant gain estimation.• Controller design based on the model.
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Parameters to Identify
In the transfer function model
we need to determine
• the gain
• the damping ratio
• the natural frequency
( )K
( )
( )n
2
2
( )2
1n n
KH s
s s
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Time Domain System Identification
• Log decrement analysis
• Fitting the step response of a second order system to the measured step response
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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
-0.5
0
0.5
1
1.5
Time (sec)D
ispl
acem
ent
(cm
)
1
2
34
5 6 7
0 0.2 0.4 0.6 0.8 1 1.2
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Time (sec)
Dis
plac
emen
t (c
m)
EstimatedMeasured
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0 0.5 1 1.5 2
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Time (sec)
Dis
plac
emen
t (c
m)
EstimatedMeasured
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Frequency Domain System Identification
• Determine steady state frequency response by exciting the system at different frequencies.
• Compare to predicted frequency response.
• Optimize transfer function model to best fit measured frequency response.
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Model/Actual Frequency Response(from log-decrement)
101
48
50
52
54
56
58
60
62
64
66
68
Mag
nitu
de (
dB)
Frequency (rad/sec)
= 0.0877, n = 25.43
ModelActual
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Model/Actual Frequency Response(from fitting step response)
101
50
52
54
56
58
60
62
64
66
68
Mag
nitu
de (
dB)
Frequency (rad/sec)
= 0.1, n = 26.7
ModelActual
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Model From Frequency Response
101
54
56
58
60
62
64
66
Mag
nitu
de (
dB)
Frequency (rad/sec)
= 0.19081, n = 26.1252
ModelActual
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Closed Loop Plant Gain Estimation
• We model the motor as a gain, , and assume it is part of the plant
• We use a proportional controller with gain • The closed loop system is
• The closed loop plant gain is then
motorK
pK
clpg motorK K K
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Closed Loop Plant Gain Estimation
• Input step of amplitude A
• Steady state output
• The closed loop plant gain is given by
ssy
clpg
1ss
p ss
yK
K A y
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Results with Controllers
After identifying the system, I, PI, PD, and PID controllers were designed using Matlab’s sisotool to control the position of the mass (the first cart).
Both predicted (model based) responses and actual (real system) responses are plotted on the same graph.
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Integral (I) Controller
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (sec)
Dis
plac
emen
t (c
m)
ModelActual
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``It doesn’t work!’’
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Proportional+Derivative (PD) Controller
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (sec)
Dis
plac
emen
t (c
m)
ModelActual
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PID Controller(complex conjugate zeros)
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Time (sec)
Dis
plac
emen
t (c
m)
ModelActual
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PID Controller(real zeros)
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Time (sec)
Dis
plac
emen
t (c
m)
ModelActual
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State Variable Feedback
0 0.05 0.1 0.15 0.2 0.25 0.30
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
Time (sec)
Pos
ition
(cm
) k
1 = 0.3, k
2 = 0.02, f = 0.40764
ModelActual
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Conclusions
Students learn:
• Simple, commonly used models are not exact, but still very useful.
• Simple models are a reasonable starting point for design.
• Motors have limitations which must be incorporated into designs.
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Conclusions
We have extended these labs to include
Model matching• ITAE • quadratic optimal• polynomial equation (Diophantine)
2 and 3 DOF state variable models
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Acknowledgement
This material is based upon work supported by the National Science Foundation under Grant No. 0310445