Robust control system design using random search and genetic algorithms

Author

Marrison, Christopher I. ; Stengel, Robert F.

Author_Institution

Oliver Wyman & Co., New York, NY, USA

Volume

42

Issue

6

fYear

1997

fDate

6/1/1997 12:00:00 AM

Firstpage

835

Lastpage

839

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

Keywords

Monte Carlo methods; compensation; control system synthesis; genetic algorithms; minimisation; robust control; search problems; statistical analysis; compensators; genetic algorithms; noisy Monte Carlo estimates; performance robustness; random search; robust control system design; stability; statistical tools; stochastic robustness cost function minimization; Algorithm design and analysis; Clustering algorithms; Cost function; Genetic algorithms; Monte Carlo methods; Robust control; Robust stability; Robustness; Stochastic processes; System analysis and design;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.587338

Filename

587338