Robust control system design using random search and genetic algorithms

Christopher I. Marrison; Robert F. Stengel

doi:10.1109/9.587338

Robust control system design using random search and genetic algorithms

Christopher I. Marrison, Robert F. Stengel

Mechanical & Aerospace Engineering

Research output: Contribution to journal › Article › peer-review

79 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 language	English (US)
Pages (from-to)	835-839
Number of pages	5
Journal	IEEE Transactions on Automatic Control
Volume	42
Issue number	6
DOIs	https://doi.org/10.1109/9.587338
State	Published - 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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10.1109/9.587338

Cite this

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KW - Probabilistic methods

KW - Robust control design and analysis

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Robust control system design using random search and genetic algorithms

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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