Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0952-1976(01)00023-9
Title: Performance-based control system design automation via evolutionary computing
Authors: Tan, K.C. 
Li, Y.
Keywords: CACSD
Design automation
Evolutionary algorithm
Global optimization
Parallel implementation
Issue Date: Aug-2001
Source: Tan, K.C., Li, Y. (2001-08). Performance-based control system design automation via evolutionary computing. Engineering Applications of Artificial Intelligence 14 (4) : 473-486. ScholarBank@NUS Repository. https://doi.org/10.1016/S0952-1976(01)00023-9
Abstract: 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-the-computer" 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. © 2002 Published by Elsevier Science Ltd.
Source Title: Engineering Applications of Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/57040
ISSN: 09521976
DOI: 10.1016/S0952-1976(01)00023-9
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