Shuffled Frog Leaping Algorithm Research Based Optimal Iterative Learning Control

DOI

10.2991/ameii-15.2015.159How to use a DOI?

Keywords

iterative learning control; optimization; shuffled frog leaping algorithm; nonlinear; filter

Abstract

To solve the problems of nonlinear and input constraints in the iterative learning control system, using real-coded shuffled frog leaping algorithm to solve optimization problem1 in iterative learning control, .A shuffled frog leaping algorithm(SFLA) based optimal iterative learning control is proposed. The algorithm combines the advantages of memetic algorithm and particle swarm optimization to simplify the algorithm of parameter selection, reduce the search space and improve the convergence rate. The proposed approach benefits from the design of a low-pass FIR filter. This filer successfully removes unwanted high frequency components of the input signal, which are generated by SFLA algorithm method due to the random nature of SFLA algorithm search. Simulation are used to illustrate the performance of this new approach, and they demonstrate good results in terms of convergence speed and tracking of the reference signal.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaohong Hao
AU  - Hua Wang
AU  - Zhuoyue Li
AU  - Qun Gu
PY  - 2015/04
DA  - 2015/04
TI  - Shuffled Frog Leaping Algorithm Research Based Optimal Iterative Learning Control
BT  - Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics
PB  - Atlantis Press
SP  - 854
EP  - 860
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-15.2015.159
DO  - 10.2991/ameii-15.2015.159
ID  - Hao2015/04
ER  -