ACS6116 Advanced Industrial Control

Module Description

The aim of this module is to provide students with an introduction to some of the advanced control techniques used in modern control engineering research and industrial applications. The module will cover both theory and practice, involving analysis and design.

Different control techniques and applications may be covered in different years. In all cases, the basic principles and concepts of a particular control technique will be introduced, and comparisons and contrasts will be made with other techniques. Subsequently, the design, analysis and implementation of advanced controllers or control laws will be covered, starting from the requirements of the basic control problem for the application at hand (i.e. stability in the presence of constraints; disturbance and noise rejection). Controller design will be illustrated by industrially relevant case studies.

In 2018/19, the focus of the module will be on model predictive control, one of the foremost advanced control techniques used in industry
Credits: 15 (Spring semester)

Pre-requisites: ACS317, ACS6102 or equivalent

Module Leader

Photo of Paul Trodden

Dr Paul Trodden
Amy Johnson Building

If you have any questions about the module please talk to us during the lectures or the labs in the first instance. It is likely that other students will learn from any questions you ask as well, so don’t be afraid to ask questions.

Outside of lectures please contact one of us via email, or drop in to see one of us.

Learning Outcomes

Learning Outcomes

By the end of the module, students will be able to satisfy the following general learning outcomes:

1. Describe and explain the principles of more than one advanced control technique.
2. Analyse practical performance specifications and convert these into functional requirements on controllers.
3. Design, implement and evaluate an advanced control system against these requirements.
4. Compare and contrast different advanced control solutions to a particular control problem or application.

The additional learning outcomes specific to model predictive control in 2018/19 are:

1. Describe the receding-horizon principle, and hence compare and contrast LQ-optimal control and MPC. [SM1fl]
2. Construct a constrained finite-horizon optimal control problem – including constraint, model and cost definition – re-formulate it as an optimization problem, and recall and evaluate the analytical expression for the control law in the unconstrained case. [SM1fl, EA1fl]
3. Analyse, design, implement and simulate MPC controllers with
guaranteed properties, including feasibility, stability and
offset-free tracking. [EA1fl, D1fl, EP3fl]

This module satisfies the AHEP3 (Accreditation of Higher Education Programmes, Third Edition) Learning Outcomes that are listed in brackets after each learning outcome above. For further details on AHEP3 Learning Outcomes, see the downloads section of our accreditation webpage.



1. Introduction to model predictive control

2. The unconstrained LQ regulation problem:
a. fundamental properties
b. formulating the problem and obtaining the solution
c. finite- and receding-horizon implementations, and LQ-MPC

3. Unconstrained LQ-MPC:
a. stability and performance
b. guaranteeing stability

4. Constrained MPC:
a. introduction and constraint modelling
b. formulating and solving the problem
c. stability analysis
d. stability analysis via Lyapunov’s direct method applied to the value function
e. feasibility implies stability I (terminal equality constraints)
f. feasibility implies stability II (generalized terminal constraints)

5. Offset-free tracking and disturbance rejection
a. the ∆u formulation
b. the steady-state target optimization approach

6. Examples and industrial applications

Teaching Methods

Learning and Teaching Methods

  • Lectures: 30 hours
  • Tutorials: 6 hours
  • Laboratory sessions: 12 hours
  • Independent Study: 100 hours

The lectures, supported by tutorials, cover all learning outcomes in terms of informing (providing technical content and information) and instruction. The tutorials will provide additional practice and opportunity to solve problems and apply the technical knowledge acquired during lectures and from independent study. The laboratory sessions, with accompanying coursework, will provide more hands-on opportunities to design, implement and apply advanced control systems.

Teaching Materials

Learning and Teaching Materials

All teaching materials will be available via MOLE.

The resources available on MOLE include a detailed set of notes (including tutorial questions), lecture slides, everything required for the continuous assessment and an example exam paper.



The assessment comprises a 2-hour written exam (60%) and continuous assessment (two assignments, worth 40% in total).

The resit for this module is usually by examination only



Students are encouraged to provide feedback and evaluation during the module, both to the lecturers and via MOLE discussion groups. Students will have the opportunity to provide formal feedback via the Faculty of Engineering Student Evaluation Survey at the end of the module.

Student Evaluation

Student Evaluation

Students are encouraged to provide feedback during the module direct to the lecturer. Students will also have the opportunity to provide formal feedback via the Faculty of Engineering Student Evaluation Survey at the end of the module.

Recommended Reading

Recommended Reading

1. Rawlings, J. B., and Mayne, D. Q., 2009, Model predictive control: Theory and design, Nob Hill, [available in Information Commons, 629.8 (R)]

2. Maciejowski, J. M., 2002, Predictive control with constraints, Prentice Hall, [available in Information Commons, 629.8 (M)]

3. Rossiter, J.A., 2003, Model-based predictive control: a practical approach, CRC Press, [available in Information Commons, 629.8 (R)]

4. Camacho E. and Bordons C., 1996, Model predictive control, Springer, [available in Information Commons, 629.8 (C)]

5. Rossiter, J.A., 2018, A first course in predictive control, CRC Press, [available in Information Commons, 629.8 (R)]

6. Kouvaritakis, B., and Cannon, M., 2016, Model Predictive Control: Classical, Robust and Stochastic, Springer, [available in Information Commons, 629.8 (K)]