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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
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