Performance-based control system design automation via evolutionary computing

Tan, K.C. and Li, Y. (2001) Performance-based control system design automation via evolutionary computing. Engineering Applications of Artificial Intelligence, 14(4), pp. 473-486. (doi: 10.1016/S0952-1976(01)00023-9)



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This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD) automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’ controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing, with good closed-loop performance and robustness in the presence of practical constraints and perturbations.

Item Type:Articles
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
Q Science > QA Mathematics > QA76 Computer software
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Research Group:Intelligent Systems
Journal Name:Engineering Applications of Artificial Intelligence
Publisher:Elsevier Science
Copyright Holders:Copyright © 2001 Elsevier
First Published:First published in Engineering Applications of Artificial Intelligence 14(4):473-486
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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