Robust control system design using random search and genetic algorithms

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.