Robust control system design using random search and genetic algorithms

Christopher I. Marrison, Robert F. Stengel

Research output: Contribution to journalArticlepeer-review

77 Scopus citations

Abstract

Random search and genetic algorithms find compensators to minimize stochastic robustness cost functions. Statistical tools are incorporated in the algorithms, allowing intelligent decisions to be based on "noisy" Monte Carlo estimates. The genetic algorithm includes clustering analysis to improve performance and is significantly better than the random search for this application. The algorithm is used to design a compensator for a benchmark problem, producing a control law with excellent stability and performance robustness.

Original languageEnglish (US)
Pages (from-to)835-839
Number of pages5
JournalIEEE Transactions on Automatic Control
Volume42
Issue number6
DOIs
StatePublished - 1997

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Genetic algorithms
  • Probabilistic methods
  • Robust control design and analysis

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