Paper Title:
A Clonal Selection Algorithm Based Optimal Iterative Learning Control with Random Disturbance
  Abstract

Clonal selection algorithm is improved and proposed as a method to solve optimization problems in iterative learning control. And a clonal selection algorithm based optimal iterative learning control algorithm with random disturbance is proposed. In the algorithm, at the same time, the size of the search space is decreased and the convergence speed of the algorithm is increased. In addition a model modifying device is used in the algorithm to cope with the uncertainty in the plant model. In addition a model is used in the algorithm cope with the uncertainty in the plant model. Simulations show that the convergence speed is satisfactory regardless of whether or not the plant model is precise nonlinear plants. The simulation test verify the controlled system with random disturbance can reached to stability by using improved iterative learning control law but not the traditional control law.

  Info
Periodical
Chapter
Chapter 9: Manufacturing Engineering and Simulation
Edited by
Paul P. Lin and Chunliang Zhang
Pages
2299-2302
DOI
10.4028/www.scientific.net/AMM.105-107.2299
Citation
X. H. Hao, Q. Gu, "A Clonal Selection Algorithm Based Optimal Iterative Learning Control with Random Disturbance", Applied Mechanics and Materials, Vols. 105-107, pp. 2299-2302, 2012
Online since
September 2011
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$32.00
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