On-line multiobjective automatic control system generation by evolutionary algorithms

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.

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On-line multiobjective automatic control system generation by evolutionary algorithms
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.
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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.

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 Science > School of Engineering
ID Code:2198
Deposited On:26 Feb 2010 10:37

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