Control Design Made Easy - MathWorks · 3 Agenda (3 demos) PID Control Tuning in MATLAB from...
Transcript of Control Design Made Easy - MathWorks · 3 Agenda (3 demos) PID Control Tuning in MATLAB from...
1© 2014 The MathWorks, Inc.
Control Design Made Easy
By Ryan Gordon
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Key Themes
You can automatically tune PID controllers in MATLAB from acquired data
You can automatically tune PID controllers from dynamic simulations
Complex MIMO control systems can be tuned automatically
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Agenda (3 demos)
PID Control Tuning in MATLAB from Measured Input/Output data
PID Control Tuning in Simulink using a Simscape dynamic model
Automatic Tuning of Multi-input Multi-output (MIMO) control systems in Simulink
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Modeling Dynamic Systems: two approaches
Use system test data to derive a mathematical representation
Data-Driven Modeling
sese
esG 1.0
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Modeling Dynamic Systems: two approaches
Use an understanding of the system’s physics to derive a mathematical representation
First-Principles Modeling
V+
V-
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Modeling Dynamic Systems: Simscape
Use an understanding of the system’s physics to map topography of physical components
First-Principles Modeling
V+
V-
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Both have advantages & disadvantages
Data-Driven Modeling First-Principles ModelingComplete Modeling Environment
Advantages: Insight in behavior Physical parameters
Disadvantages: Time-consuming Requires expertise
Advantages: Fast Accurate
Disadvantages: Requires plant Requires data acquisition system
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Agenda (3 demos)
PID Control Tuning in MATLAB from Measured Input/Output data
PID Control Tuning in Simulink using a Simscape dynamic model
Automatic Tuning of Multi-input Multi-output (MIMO) control systems in Simulink
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Introduction to Control System Toolbox
Use industry-standard tools and algorithms for analysis and design of control systems
Create, manipulate, and analyze linear models
Design SISO and MIMO controllers
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Introduction to System Identification Toolbox
What is it? Modeling tool that can use experimental data to estimate
mathematical models (black box) or tune parameters of predefined models in MATLAB® (grey box)
Why was it developed? To estimate models from data
Who can use it? Controls engineers – Plant and noise models for control system
design Applications requiring prediction – MPC, noise cancellation,
financial analysis Decision making – Virtual sensing, fault diagnosis, modal analysis
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sss
t
y(t)
Data to Model
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Agenda (3 demos)
PID Control Tuning in MATLAB from Measured Input/Output data
PID Control Tuning in Simulink using a Simscape dynamic model
Automatic Tuning of Multi-input Multi-output (MIMO) control systems in Simulink
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Introduction to Simulink®
Block-diagram environment Model, simulate, and analyze
multidomain systems Design, implement, and test:
– Control systems– Signal processing systems– Communications systems– Other dynamic systems
Platform for Model-Based Design
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Introduction to Simulink Control Design
Automatically tune gains of PID controllers
Rapidly perform advanced linear analysis and control design for plants modeled in Simulink
A x + B u = 0
yPlant
uController
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Agenda (3 demos)
PID Control Tuning in MATLAB from Measured Input/Output data
PID Control Tuning in Simulink using a Simscape dynamic model
Automatic Tuning of Multi-input Multi-output (MIMO) control systems in Simulink
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Introduction to Robust Control Toolbox
Analyze and automatically tune control systems for performance and robustness in the presence of uncertainty
Design fixed-structure controllers modeled in Simulink
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Key Themes
You can automatically tune PID controllers in MATLAB from acquired data
You can automatically tune PID controllers from dynamic simulations
Complex MIMO control systems can be tuned automatically
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Next Steps
Check out the other booths at the MATLAB Virtual Conference
Visit the website for even more videos, examples and webinars
Check out the MATLAB community– Answers– Cody– Blogs