Design and Analysis of Intelligent Control Systems for Real-world Applications

Course Highlights
Intelligent control systems exist in many sectors ranging from manufacturing to healthcare. This course is concerned with the design and analysis of intelligent control systems for real-world applications. In the analysis of the system, given the system, we wish to determine it characteristics or behaviours. In the design aspect, we specify the desired system characteristics or behaviour, and we will configure or synthesize the intelligent system so that it exhibits these desired qualities.

In this advanced control course, fundamental principles and practices pertaining to design and analysis of sampled-data systems will be covered. Specifically, the concept of sampled-data control, modelling techniques, stability, dynamic response and frequency domain analysis and a widely used control strategy such as Proportional-Integral-Derivative (PID) Controller as well as modern control technique such as state-space feedback control will be covered. In many real-world problems, models of the plant/process to be controlled are either difficult to obtain or impossible to obtain. As such, data-driven modelling techniques which enable models to be derived from input and output data. Specifically,  neural-networks-based controllers which have the learning ability and fuzzy-logic-based controllers which can deal with uncertainties will be covered.

Course Objectives
What you will learn:
• Fundamental principle of sampled-data feedback control theory
• Modelling and simulation of sampled-data dynamic systems
• Design and implementation of intelligent controllers
• Concept of stability in the z-domain

METHODOLOGIES
This course is presented an interactive seminar and discussion style with case studies to illustrate the principles and concepts taught. Hands-on sessions will be carried out in Day 3.

Participants will have a good grasp of the fundamental principles of sampled-data feedback control and will be able to design an intelligent controller for complex dynamic systems. 

Who Must Attend
Engineers, 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 Advanced Control.

Course Outline

Day 1
1. Overview of Sampled-data Control

2. Z-transforms and Stability

3. Sampled-data Systems
- Selection of sampling rate; Analysis of sample and hold; Spectrum of a sample and aliasing; Data extrapolation and impostors; Anti-aliasing filters; Tracking effectiveness; Disturbance rejection; Hardware characteristics.

Day 2
1. Design of Digital Controllers Using Transform Techniques

2. Design of Digital Controllers Using State-space Methods
- Control law design; Estimator design; regulator design; Introduction of the reference input; Integral control; pole placement using polynomials; controllability and observability.

3. Quatization Effects
- Analysis of round-off errors; Effects of round-off of parameters; Limit cycles and dither.

4. Neural Networks Fundamentals and its Control Applications
- The fundamental principles of Neural Networks will be presented. The pros and cons of using Neural Networks will be discussed. Controller design using Neural Networks Toolbox will be discussed and demonstrated.

Day 3
1. Fuzzy Logic Fundamentals and its Control Applications
- The fundamental principles, the pros and cons of using Fuzzy Logic and Controller design using Fuzzy Logic Toolbox will be discussed and demonstrated.

2. Use of MATLAB and Simulink in Control
- An introduction to MATLAB and Simulink; Block diagram reduction using MATLAB; Linear system analysis using MATLAB; Dynamic modeling using Simulink.

3. Modelling and Control of a Magnetic Levitation System
- The dynamic model of a Magnetic Levitation System will be derived. Subsequently, an appropriate intelligent controller will be designed and simulation using Simulink. The control gains will be tuned automatically.  

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