Executive Development Programme in Stability Analysis and Controller Tuning

This course is designed for experienced engineers and managers engaged in control systems and process automation. It equips participants with advanced skills in stability analysis and controller tuning, enabling them to optimize system performance and reliability. Participants will learn to apply sophisticated tools and methodologies to analyze system stability and fine-tune controllers for optimal response, integrating theoretical knowledge with practical applications.

Upon completion, attendees will be able to conduct thorough stability assessments, select appropriate control strategies, and implement precise tuning techniques, thereby enhancing the efficiency and stability of complex industrial processes.

1. 1. Introduction to Stability Analysis: Learners will study the basics of system stability, including definitions and common types of stability. They will gain an understanding of how to analyze the stability of linear time-invariant systems using classical methods.
2. 2. Frequency Response Analysis: This module covers the frequency response of systems, enabling learners to analyze system behavior over a range of frequencies. Practical skills include using Bode plots and Nyquist diagrams for stability assessment.
3. 3. Root Locus Analysis: Learners will explore the root locus method for analyzing how the poles of a system move in the complex plane as a system parameter changes. They will learn to predict and design the system's dynamic behavior.
4. 4. PID Controller Basics: This module introduces proportional-integral-derivative (PID) controllers, covering their basic structure and how they are used in control systems. Practical skills include tuning PID controllers for different types of systems.
5. 5. Advanced PID Tuning Techniques: Learners will delve into more sophisticated PID tuning methods, such as Ziegler-Nichols and Cohen-Coon techniques, and apply these to enhance control system performance.
6. 6. State-Space Control: This module transitions to state-space representation and control design, covering topics like controllability, observability, and state feedback controllers. Practical skills include designing state-space controllers and analyzing their performance.
7. 7. Model Predictive Control (MPC): Learners will study MPC, a control technique that uses a process model to predict future behavior and optimize control actions. Practical skills include implementing MPC controllers and tuning them for real-world applications.
8. 8. Robust Control Techniques: This module covers robust control methods designed to handle uncertainties in the system model or external disturbances. Practical skills include designing robust controllers and evaluating their performance under varying conditions.
9. 9. Nonlinear Control Systems: Learners will explore control strategies for nonlinear systems, including Lyapunov methods and adaptive control. Practical skills include applying nonlinear control techniques to stabilize and control nonlinear systems.
10. 10. Real-World Case Studies: This module provides learners with the opportunity to apply the concepts and skills learned throughout the programme to real-world control system design and analysis cases. Practical skills include problem-solving, system analysis, and control design in complex scenarios.