Applying Control Systems Design with MATLAB and SIMULINKļƒ¢

Overview

Control system design applies automatic control theory to design systems with desired behaviors in control environment. In practice, sensors or detectors are used to measure output performance of the plant that being controlled. These measurements provide corrective feedback to achieve the desired performance. Automatic control system is a system designed to perform without requiring human input. Controller tuning process requires good control theory knowledge to adjust the controller parameters. Automatic controller tuning is desired to improve the control system design process without working on manual calculation of the controller parameters.

This training addresses the need of working with system modeling, controller auto-tuning and proof-of-concept through simulation.

Course Highlights

This 2-day course provides basic of control system design and a general understanding of how to accelerate the design process for closed-loop control systems using MATLAB and Simulink. It includes PID controller auto-tuning, custom controller design and controller hardware implementation consideration. Topics include:

  • Control system design overview
  • System Modeling
  • System analysis
  • Control 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.

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

 

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 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.
  • 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.

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

  • 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.

  • 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

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.

  • 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.

  • 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

 

 

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