Stewart, Paul and Stone, D. A. and Fleming, P.A. (2006) On-line multiobjective automatic control system generation by evolutionary algorithms. In: 2004 IEEE International Symposium on Industrial Electronics, 4-7 May 2004, Palais de Congres Expositions, Ajaccio, France.

Preview

PDF GP1.pdf - Whole Document Download (3538Kb)

Abstract

Evolutionary algorithms are applied to the on- line generation of servo-motor control systems. In this paper, the evolving population of controllers is evaluated at run-time via hardware in the loop, rather than on a simulated model. Disturbances are also introduced at run-time in order to pro- duce robust performance. Multiobjective optimisation of both PI and Fuzzy Logic controllers is considered. Finally an on-line implementation of Genetic Programming is presented based around the Simulink standard blockset. The on-line designed controllers are shown to be robust to both system noise and ex- ternal disturbances while still demonstrating excellent steady- state and dvnamic characteristics.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Evolutionary algorithms are applied to the on- line generation of servo-motor control systems. In this paper, the evolving population of controllers is evaluated at run-time via hardware in the loop, rather than on a simulated model. Disturbances are also introduced at run-time in order to pro- duce robust performance. Multiobjective optimisation of both PI and Fuzzy Logic controllers is considered. Finally an on-line implementation of Genetic Programming is presented based around the Simulink standard blockset. The on-line designed controllers are shown to be robust to both system noise and ex- ternal disturbances while still demonstrating excellent steady- state and dvnamic characteristics.
Keywords:	Multiobjective evolutionary algorithm, Multiobjective optimisation, Multiobjective genetic programming, Automatic controller design
Subjects:	H Engineering > H660 Control Systems G Mathematical and Computer Sciences > G700 Artificial Intelligence G Mathematical and Computer Sciences > G760 Machine Learning H Engineering > H600 Electronic and Electrical Engineering
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Engineering
Depositing User:	Paul Stewart
Date Deposited:	26 Feb 2010 10:37
Last Modified:	13 Mar 2013 08:35
URI:	http://eprints.lincoln.ac.uk/id/eprint/2198

Actions (login required)

View Item