Title :
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
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;
Conference_Titel :
Robot Motion and Control, 2002. RoMoCo '02. Proceedings of the Third International Workshop on
Print_ISBN :
83-7143-429-4
DOI :
10.1109/ROMOCO.2002.1177143
