A genetic algorithm based optimisation method for iterative learning control systems

Author

Hatzikos, Vasilis ; Owens, David

Author_Institution

Dept. of ACSE, Univ. of Sheffield, UK

fYear

2002

fDate

9-11 Nov. 2002

Firstpage

423

Lastpage

428

Abstract

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.

Keywords

continuous time systems; convergence; genetic algorithms; iterative methods; learning systems; linear systems; continuous time system; convergence; genetic algorithms; iterative learning control systems; linear system; minimum phase system; nonlinearities; optimisation; Control systems; Convergence; Genetic algorithms; Iterative algorithms; Iterative methods; Manipulators; Modems; Motion control; Optimal control; Optimization methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Robot Motion and Control, 2002. RoMoCo '02. Proceedings of the Third International Workshop on

Print_ISBN

83-7143-429-4

Type

conf

DOI

10.1109/ROMOCO.2002.1177143

Filename

1177143

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3144073