The University of Southampton

SESG6036 Advanced Control Design

Module Overview

This module covers the topics on advanced control systems analysis and design. To attend this module, students are expected to have learned the classical continuous-time control analysis and design, both in the frequency domain and the time domains. Basic understanding and knowledge of nonlinear systems is also required. In this module, the discrete-time counterparts of the continuoustime control analysis and design will be studied, motivated by the common implementation of controllers using microprocessors or computers. How to handling uncertainty in engineering by advanced control techniques, e.g., robust control and adaptive control will also be studied. Both the theoretical aspects and some of the practical aspects of discrete-time are discussed.

Aims and Objectives

Module Aims

• To provide students with an introduction to advanced theory and techniques of control system analysis and design, as well as some issues in their implementation, both in linear and nonlinear framework. • To introduce several application areas of advanced control techniques, especially those related to electro-mechanical systems, active vibration and noise control, and aero systems.

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • The fundamental principles of digital control, robust control, and adaptive control.
  • The practical issues in implementing a control algorithm, in particular the implementation using digital computer
  • The methods and techniques to analyse and design complex control systems which involve uncertainties which are commonly found in real engineering practice.
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • Appreciate the need for increased use of digital control to replace some analogue systems.
  • Grasp the speed at which these systems advance year by year. The course will equip you with the ability to develop your knowledge further as technology advances.
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Construct suitable mathematical models applicable to the design of relevant control systems.
  • Further advance the MATLAB/SIMULINK programming skills to facilitate control system analysis and synthesis.
  • Apply control algorithms using microprocessor/microcontroller to control real systems in a hardware implementation.
  • Manipulate and modify standard design methods to solve control problems of non-conventional systems.
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Gain awareness of the practical control issues existing in industrial applications
  • Analyse control-related dynamics for a variety of engineering systems with appropriate mathematical tools and techniques
  • Design advanced controllers (digital, robust, or adaptive) for engineering systems. You will become familiar with the typical system architectures used.
  • Identify various control problems and choose the most suitable control design approach to solve a certain particular problem


This module consists of three parts: Part I: Computer Controlled Systems • Introduction to computer-controller systems and sampled-data systems: examples, definition, analysis and design approaches, practical problems. • Discrete-time modelling: sampling and holding, z-transform, state-space/transfer function representation of digital systems. • Analysis of discrete-time/digital systems: solution properties, poles/eigenvalues, eigenvectors, stability, structural decomposition, controllability/observability, stabilizability/detectability. • Control design of discrete-time/digital systems: Pole assignment methods, Optimal control method (LQR), LQG/Kalman filtering optional. • Implementation issues in digital control. Part II: Handling Uncertainties: Robust Control and Adaptive Control: • Uncertainties and Constraints: state constraints, control constraints and other constraints; • Basic Principle of Model Predictive Control : Prediction, Optimization, Incorporating constraints, quadratic programming; • Robustness and Disturbances: Setpoint tracking and integral action, Robustness to disturbances, handling disturbances; • Practical issues in model predictive control. Part III: Active Control of Sound and Vibration: • Feedforward Control: The error surface, Steepest descent algorithm, Convergence and robustness, Newton’s algorithm; • Digital Control: Digital FIR filter, The LMS adaptation algorithm, Convergence and robustness; • Robust Feedback Control: Active Control, Sensitivity function, robust stability; •Modal Control of Vibration: Control of SDOF system, Applications.

Special Features


Learning and Teaching

Teaching and learning methods

Teaching and learning methods • Lectures supported by hand-outs. • Revision and feedback sessions. • Demonstrations using software simulation and video materials when appropriate. Learning activities include: • MATLAB/SIMULINK based examples. • Example sheets and worked solutions. • Take home exercises and homework. Students are required to do an average of 114 hours self-study, including some practical exercises using control system design software, to complete this module.

Wider reading or practice80
Completion of assessment task10
Total study time150

Resources & Reading list

Software requirements. The software used to support the teaching and learning of this module is available within the university site licenses. Some small costs are expected to make some key texts available for students in the library.

K. J. Astrom (Author) and B. (1995). Adaptive Control. 

M. Green and D. J. N. Limebeer (2012). Linear Robust Control. 

K. J. Astrom and B. Wittenmark (1996). Computer-Controlled Systems: Theory and Design. 

M. Morari and E. Zafirou (1989). Robust process control. 

J. Doyle, B. Francis, and A. Tannenbaum (1990). Feedback Control Theory. 

G. E. Dullerud and F. Paganini. A Course in Robust Control Theory: A Convex Approach. 

C.L. Phillips and H. T. Nagle (1998). Digital Control System Analysis and Design. 

S.J. Elliott: (2001). Signal Processing for Active Control. 

K. Ogata (1987). Discrete-Time Control Systems. 

N.S. Nise (2000). Control System Engineering. 

Software requirements. Matlab/Simulink, Maple


Assessment Strategy



Exercises and Quizzes


MethodPercentage contribution
Coursework 30%
Exam  (120 minutes) 70%


MethodPercentage contribution
Exam  (120 minutes) 100%

Repeat Information

Repeat type: Internal & External

Linked modules


To study this module, you will need to have studied the following module(s):

SESM3030Control and Instrumentation


Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

Books and Stationery equipment

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

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