Control System Design with Simulink

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 are covered in later chapters.

• Defining a control design workflow
• Linearizing a model
• Finding system characteristics
• Setting controller requirements
• Tuning controllers
• Testing controllers

Model Representations

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

• Model representations overview
• LTI objects
• Simulink models

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 Identification

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

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

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