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12 - 16 June 2017

Course details

  • Duration: 5 days
  • Fees:
    - per module £2000
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**Pre-requisites: To undertake this module, it is recommended that applicants take the ‘Modelling & Simulation’ and ‘Modern Control Systems Design’ modules which are also offered via the CPD route**

Module leaders

Jon Love, Richard Hughes, Geoff Lewis, Prof Gary Montague, Dr Mark Willis, Dr Jie Zhang

More information

Course aims

To develop a quantitative understanding of the various complex techniques that underpin advanced (and modern) process control strategies, and an appreciation of how and when to apply them. 

Learning objectives

  • To develop a quantitative understanding of the least squares based techniques for model identification and estimation. 
  • To become familiar with the minimum variance methods as a basis for studying the techniques of self tuning and adaptive control. 
  • To provide a basis for applying these techniques in an industrial context. 
  • To develop an in-depth understanding of generalised predictive control (GPC) as a vehicle for explaining the principles of model predictive control (MPC). 
  • To appreciate the functionality of commercially available packages for realising model predictive control. 
  • To introduce some of the techniques of non-linear control. 

Course structure

This module will be of one week's full-time intensive study consisting of a variety of lectures, informal tutorials for problem solving and structured computer-based laboratory work. The time allocation for practical work provides for use of Matlab and Simulink for exercises on linear regression, data transformation, estimation, MPC and Kalman filtering.