Executive Development Programme in Robust Control Systems Design Techniques

This course is tailored for senior engineers, managers, and executives in the control systems industry seeking to enhance their expertise in robust control systems design. Participants will gain advanced knowledge in stability analysis, controller design, and fault-tolerant systems, equipping them with the tools to address complex industrial challenges effectively.

Students will learn to apply cutting-edge design techniques, including model predictive control, adaptive control, and robust optimization, to improve system performance and reliability. Practical case studies and hands-on workshops will ensure a deep understanding and the ability to implement these techniques in real-world scenarios.

1. 1: Introduction to Robust Control Systems: Learners will study the basic principles of robust control systems, including stability, performance, and robustness. They will gain foundational knowledge in control theory and system dynamics.
2. 2: Linear Time-Invariant Systems: This module covers the analysis and design of linear time-invariant (LTI) systems, focusing on frequency response and pole-zero analysis. Learners will develop skills in modeling and analyzing LTI systems.
3. 3: Robust Stability Analysis: Learners will explore methods for assessing the stability of control systems under uncertainty, including structured singular value (µ) analysis and structured uncertainty description. Practical skills include using MATLAB for robust stability analysis.
4. 4: H8 Control Design: This module introduces H8 control theory, which aims to minimize the worst-case impact of disturbances on system performance. Learners will learn to design H8 controllers using convex optimization techniques.
5. 5: Model Predictive Control: This module focuses on model predictive control (MPC) techniques, which use models to predict future system behavior and optimize control actions. Learners will gain hands-on experience in designing and implementing MPC strategies.
6. 6: Fault Detection and Diagnosis: Learners will study methods for detecting and diagnosing faults in control systems, including residual generation and fault isolation techniques. Practical skills include developing fault detection algorithms and integrating them into control systems.
7. 7: Adaptive Control Systems: This module covers adaptive control techniques that adjust control strategies based on system dynamics and environmental changes. Learners will learn to design and implement adaptive controllers for real-time applications.
8. 8: Sliding Mode Control: Sliding mode control (SMC) is introduced as a robust control approach that ensures system behavior lies within a predefined sliding surface. Learners will design and simulate SMC controllers and understand their advantages and limitations.
9. 9: Advanced Topics in Robust Control: This module delves into more advanced topics such as mixed-sensitivity optimization and robust control design for networked control systems. Learners will apply advanced control techniques to complex real-world scenarios.
10. 10: Capstone Project: In this module, learners will work on a comprehensive project that integrates the concepts and skills learned throughout the programme. They will design and implement a robust control system for a specific application, demonstrating their ability to apply theoretical knowledge in practice.