Advanced process control
12 - 16 June 2017
**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**
Jon Love, Richard Hughes, Geoff Lewis, Prof Gary Montague, Dr Mark Willis, Dr Jie Zhang
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.
- 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.
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.