Classical control theory deals with the basic principles of systems and control theory in the frequency domain and the main mathematical tool is transfer functions. Modern control theory deals with systems properties and control design in the time domain and the main mathematical tool is state-space equations. Building on these, optimal control algorithms allow us to obtain the best performance. However, optimal control algorithms are not always tolerant to uncertainties in the control system or the environment. This course is offered to systematically present the robust control theory, which concerns the design of controllers with a guaranteed performance in the face of uncertainties. It minimizes/characterizes the worst-case performance over a wide range of possibilities/uncertainties, rather than to optimize for the best performance in the ideal situation. The course will cover standard topics in robust control theory (linear fractional transformation, small-gain theorem, H-infinity control, etc.) as well as the latest developments, such as the UDE (uncertainty and disturbance estimator)-based robust control strategy and the robust control of time-delay systems recently developed by the Instructor.
Learning Objectives
- Understand the basic principles of robust control
- Understand different ways to model uncertainties
- Master small-gain theorem
- Master linear fraction transformation
- Master algebraic Riccati equations
- Master H∞ Control theory
- Master UDE-based robust control theory
- Understand robust control of time-delay systems
