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## ELEC70028 Predictive Control

Lecturer(s): Dr Eric Kerrigan

### Aims

This module will provide you with an understanding of the fundamental principles in predictive control, which is the most widely used advanced control technique in industry. The emphasis of this module is on multivariable systems with constraints. This module will give an introduction to some of the formulations currently employed in industry and show how to solve challenging optimal control problems by using state-of-the-art optimisation methods. A significant component of the module includes results that have been developed in academia during the last decade, and aims to provide you with a solid theoretical basis for understanding current and future implementations of predictive control. By the end of the course, you should be able to design and implement your own optimisation-based controllers for the real-time control of a nonlinear system using industry-standard software.

### Learning Outcomes

At the end of the module, you should be able to: - Construct a range of ?nite horizon optimal control problems with constraints. - Solve constrained ?nite horizon optimal control problems by formulating them as finite dimensional optimization problems. - Compare the advantages and disadvantages of implementing the solution to ?nite horizon optimal control problems in a receding-, decreasing- or variable-horizon fashion. - Transfer a real-world control problem into a mathematically well-de?ned optimal control problem. - Devise predictive controllers with guarantees of stability and feasibility. - Write Matlab programs that implement a predictive controller. - Appraise relatively simple papers on predictive control and be able to extract information from them in order to design a predictive controller.

### Syllabus

Formulation of a variety of optimal control problems with constraints; The receding horizon principle; Transcribing continuous-time and sampled-data control problems into discrete-time control problems; Solving constrained optimal control problems using numerical optimization software; Hard and soft constraints; Linear-quadratic predictive control; Nonlinear predictive control; Introduction to robust predictive control; Introduction to stability and feasibility in receding horizon control
Assessment
Exam Duration: N/A
Exam contribution: 0%
Coursework contribution: 100%

Term: Spring

Closed or Open Book (end of year exam): N/A

Coursework Requirement:
To be announced

Oral Exam Required (as final assessment): N/A

Prerequisite module(s): None required

Course Homepage: http://bb.imperial.ac.uk

Book List: