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.

[1]  K. Krishnakumar,et al.  Genetic algorithms - An introduction and an overview of their capabilities , 1992 .

[2]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[3]  Dennis S. Bernstein,et al.  Benchmark Problems for Robust Control Design , 1991, 1991 American Control Conference.

[4]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[5]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[6]  R. Stengel,et al.  Stochastic robustness of linear control systems , 1990 .

[7]  A. A. Torn.  Cluster Analysis Using Seed Points and Density-Determined Hyperspheres as an Aid to Global Optimization , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  R. Stengel,et al.  Technical notes and correspondence: Stochastic robustness of linear time-invariant control systems , 1991 .

[9]  Christopher Ian Marrison,et al.  The design of control laws for uncertain dynamic systems using stochastic robustness metrics , 1995 .

[10]  Dennis S. Bernstein,et al.  A Benchmark Problem for Robust Control Design , 1990, 1990 American Control Conference.

[11]  B. Efron.  The jackknife, the bootstrap, and other resampling plans , 1987 .

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  G. Sullivan.  Natural and artificial low-level seeing systems - Visual interpretation of known objects in constrained scenes , 1992 .

[14]  Robert F. Stengel,et al.  Robustness of solutions to a benchmark control problem , 1992 .

[15]  J. Schmee.  Applied Statistics—A Handbook of Techniques , 1984 .

[16]  R. Stengel,et al.  Stochastic robustness synthesis applied to a benchmark control problem , 1995 .