- Control system design overview
- System modeling
- System analysis
- Control design
- Controller implementation
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
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
Objective: Use measured data to estimate the values of a Simulink model's parameters.
- Parameter estimation overview
- Model preparation
- Estimation process
- Parameter estimation tips
Objective: Illustrate how to estimate system models based on measured data.
- System identification overview
- Data importing and preprocessing
- Model estimation
- Model validation
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
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
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
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