Automating control system design via a multiobjective evolutionary algorithm

Tan, K.C. and Li, Y. (2005) Automating control system design via a multiobjective evolutionary algorithm. In: Applications of Multi-Objective Evolutionary Algorithms. World Scientific Series on Advances in Natural Computation, pp. 155-176. ISBN 9812561064

Abstract

This chapter presents a performance-prioritized computer aided control system design (CACSD) methodology using a multi-objective evolutionary algorithm. The evolutionary CACSD approach unifies different control laws in both the time and frequency domains based upon performance satisfactions, without the need of aggregating different design criteria into a compromise function. It is shown that control engineers' expertise as well as settings on goal or priority for different preference on each performance requirement can be easily included and modified on-line according to the evolving trade-offs, which makes the controller design interactive, transparent and simple for real-time implementation. Advantages of the evolutionary CACSD methodology are illustrated upon a non-minimal phase plant control system, which offer a set of low-order Pareto optimal controllers satisfying all the conflicting performance requirements in the face of system constraints.

• Item Type: Book Sections
• Status: Published
• Glasgow Author(s) Enlighten ID: Li, Professor Yun
• Authors: Tan, K.C., and Li, Y.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Research Group: Intelligent Systems
• Publisher: World Scientific Series on Advances in Natural Computation
• ISBN: 9812561064
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher.