Applying Control Design with MATLAB and SIMULINK

 

Course Highlights

This is a two day hands-on course designed to provide a general understanding of how to use The MathWorks suite of control system design tools to accelerate the design process for closed loop control systems.

Topics include:

  • Model formats
  • System identification
  • System analysis
  • Linearization
  • Compensator design
  • Controller implementation

Course Objectives

The aim of the course is to provide general knowledge for participants to use MATLAB and SIMULINK control system design tools to accelerate the design process for closed-loop control system.

Who Must 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.

Course Benefits


Upon the completion of the course, the participants will gain a comprehensive understanding on utilizing the Control System Toolbox and SIMULINK Control Design to design and develop control system using a variety of design methodologies.


Prerequisites

Attended "Comprehensive MATLAB" and "Comprehensive SIMULINK" or equivalent experience using MATLAB and SIMULINK, and an understanding of terminology and concepts related to common control systems.


Course Outline

Day 1

Control System Design Overview
Objectives: Provide an overview of the control system design process and introduce how The MathWorks tools fit into that process. The details of each step in the design process will be covered in later chapters.

  • The digital motion control system
  • 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.

  • Model representations overview
  • LTI objects
  • Simulink models

System Identification
Objective: Illustrate how to estimate system models based on measured data.

  • 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.

  • Parameter estimation overview
  • Model preparation
  • Estimation process
  • Parameter estimation tips

System Analysis
Objective: Illustrate how to estimate system models based on measured data.

  • System identification overview
  • Data importing and preprocessing
  • Model estimation
  • Model validation

Day 2

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

  • Linearization workflow
  • Operating points
  • Linearization functions
  • Frequency response estimation

PID Control in Simulink
Objective: Use Simulink to model and tune PID controllers.

  • PID Workflow
  • Model setup
  • PID Controller block
  • Automatic tuning
  • Additional PID features

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

  • PID control
  • Lead/Lag control
  • Parameter tuning in Simulink

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

  • Physical and practical limitations of controllers
  • Discretizing a controller
  • Creating more realistic simulations