MATLAB & Simulink

CONTROL DESIGN

Control System Design with MATLAB and SIMULINK

​Course Highlight

This two-day course provides a general understanding of how to accelerate the design process for closedloop control systems using MATLAB® and Simulink®
 

Topics include:
• Control system design overview
• System modeling
• System analysis
• Control design
• Controller implementation

Who Should Attend

Engineer, researchers, scientists, and managers who are involved in control engineering design and problem solving. It is also strongly recommended for those who would like to establish and strengthen their foundation in Control Engineering.

Partners 

Upcoming Program

Techsource Systems is
Mathworks Sole and Authorised Distributor and Training Partner

Course Prerequisites

MATLAB Fundamentals, Simulink for System and Algorithm Modeling

Course Benefits

Upon the completion of the course, the participants will gain a comprehensive understanding on the following.

  • Plant modeling

  • Closed loop control system analysis

  • Compensator design

  • Controller implementation

Course Outline

Day 1 of 2

Control System Design Overview

 

Objective: Provide an overview of the control system design process and introduce how MATLAB and SIMULINK fit into that process. The details of each step in the design process will be covered in later chapters.

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  • Defining a control design workflow

  • Linearizing a model

  • Finding system characteristics

  • Setting controller requirements

  • Tuning a controller

  • Testing the controller

 

Model Representations

 

Objective: Discuss the various formats used for representing system models. Also, highlights the pros and cons of each format.

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  • Model representations overview

  • LTI objects

  • Simulink models

 

System Identification

 

Objective: illustrate how to estimate system models based on measured data.

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  • System identification overview

  • Data importing and preprocessing

  • Model estimation

  • Model validation

 

Parameter Estimation

 

Objective: Use measured data to estimate the values of a Simulink model's parameters.

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  • Parameter estimation overview

  • Model preparation

  • Estimation process

  • Parameter estimation tips

 

System Analysis

 

Objective: Outline the different analysis tools and functions available for understanding system behavior - such as system resonances, transient response, etc.

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  • System analysis functions

  • Linear System Analyzer

  • DC motor analysis

  • Automation of analysis tasks

  • Open loop analysis

Day 2 of 2

Linearization

 

Objective: Discuss techniques for linearizing a Simulink model and validating the linearization results.

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  • Linearization workflow

  • Operating points

  • Linearization functions

  • Frequency response estimation

 

 

PID Control in Simulink

 

Objective: Use Simulink to model and tune PID controllers.

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  • PID Workflow

  • Model setup

  • PID Controller block

  • Automatic tuning

  • Additional PID features

 

 

Classical Control Design

 

Objective: Use classical control design techniques to develop system controllers. Common control techniques are covered, such as PID and Lead/Lag controllers.

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  • Open-loop tuning

  • Closed-loop analysis

  • PID control

  • Lead/Lag control

 

Response Optimization

 

Objective: Use optimization techniques to tune model parameters based on design requirements and parameter uncertainty.

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  • Optimizing model response

  • Performing sensitivity analysis

  • Optimizing with parameter uncertainty

 

Controller Implementation

 

Objective: Discuss steps that might be needed to effectively implement a controller on a real system.

  • Identifying physical and practical limitations of controllers

  • Discretizing a controller

  • Preparing a controller for code generation

  • Converting to fixed-point data types