A genetic algorithm based optimisation method for iterative learning control systems

In this paper genetic algorithms are proposed as a method to implement optimality based iterative learning control algorithms. The strength of the proposed method is that it can cope with nonlinearities and hard constraints in the problem definition whereas most of the existing algorithms would fail...

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Bibliographic Details
Published inRoMoCo'02 : proceedings of the third International Workshop on Robot Motion and Control pp. 423 - 428
Main Authors Hatzikos, V., Owens, D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Control systems
Convergence
Genetic algorithms
Iterative algorithms
Iterative methods
Manipulators
Modems
Motion control
Optimal control
Optimization methods
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ISBN9788371434297
8371434294
DOI10.1109/ROMOCO.2002.1177143

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Summary:In this paper genetic algorithms are proposed as a method to implement optimality based iterative learning control algorithms. The strength of the proposed method is that it can cope with nonlinearities and hard constraints in the problem definition whereas most of the existing algorithms would fail. Simulation examples show that this approach results in fast convergence for linear plants.
ISBN:9788371434297
8371434294
DOI:10.1109/ROMOCO.2002.1177143