This course is concerned with the design of practical feedback controllers. Feedback is used in a control system to change the dynamics of the plant or process, and to reduce the sensitivity of the system to uncertainty from external signals (for example, disturbances and noise) and model uncertainty. If the performance specifications are achieved in the presence of the expected uncertainties, then the control is said to be robust. The course will also address issues around implementation of control systems. This module provides rigorous analysis of control systems, and studies the design of controllers and the industrial implementation of control algorithms. Nonlinearities in control systems are considered, to enable students to deal with real-life applications.

On completion of this course, students will be able to:

Comprehensively understand, and be able to apply to a range of practical situations, the basic scientific principles underlying control theory
	Comprehensively know and understand the mathematical and computer models relevant to transfer function analysis and be able to apply them to real-world models for control systems
	Extensively know and apply stability criteria related to control systems based on the following diagrams and methods: Root Locus, Bode and Routh Hurwitz stability criteria, linearization methods, state space, multivariate control, non-linear control and digital control systems
	Apply control theory techniques, such as PID controllers and multivariate control techniques, to electronic and electrical devices, such as (but not limited to) feedback amplifiers and electrical motors
	Extract data pertinent to an unfamiliar control system and apply computer based linear and non-linear control techniques when appropriate, while knowing their limitations
	Comprehensively understand and be able to apply information from Control Systems books, journal and conference papers
	Extensively know and apply mathematical modeling techniques pertinent to control systems
	Know and understand the commercial and economic context of control engineering
	Generate innovative designs for control systems and components or processes to fulfill a particular commercial or engineering context