Title :
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
fDate :
6/1/1997 12:00:00 AM
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;
Journal_Title :
Automatic Control, IEEE Transactions on
