Performance-based control system design automation via evolutionary computing

K.C. Tan, Y. Li

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)
2 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)473-486
Number of pages14
JournalEngineering Applications of Artificial Intelligence
Volume14
Issue number4
DOIs
Publication statusPublished - 1 Aug 2001

Keywords

  • CACSD
  • Design automation
  • Evolutionary algorithm
  • Global optimization
  • Parallel implementation

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