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
Objective: Discuss the various formats used for representing system models. Also highlights the pros and cons of each format.
Model representations overview
Objective: Illustrate how to estimate system models based on measured data.
System identification overview
Data importing and preprocessing
Objective: Use measured data to estimate the values of a Simulink model's parameters.
Parameter estimation overview
Parameter estimation tips
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.
Frequency response estimation
PID Control in Simulink
Objective: Use Simulink to model and tune PID controllers.
PID Controller block
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.
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
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MATLAB and Simulink Course Schedule
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