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

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esG 1.0

78.2194.1)(

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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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2

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