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Fuzzy Controller Design for Ball and Beam System with an Improved Ant Colony Optimization

Authors: Yeong-Hwa Chang, Chia-Wen Chang, Hung-Wei Lin, C.W. Tao

Abstract:

In this paper, an improved ant colony optimization (ACO) algorithm is proposed to enhance the performance of global optimum search. The strategy of the proposed algorithm has the capability of fuzzy pheromone updating, adaptive parameter tuning, and mechanism resetting. The proposed method is utilized to tune the parameters of the fuzzy controller for a real beam and ball system. Simulation and experimental results indicate that better performance can be achieved compared to the conventional ACO algorithms in the aspect of convergence speed and accuracy.

Keywords: Ant colony algorithm, Fuzzy control, ball and beamsystem

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061529

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References:


[1] M. Dorigo, L.M. Gambardella, "Ant colony system : a cooperative learning approach to the traveling salesman problem, " IEEE Tran. on Evolutionary Computation, vol. 1, no. 1, pp. 53-66, 1997.
[2] M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: optimization by a colony of cooperating agents," IEEE Tran. on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 26, no. 1, pp. 29-41, 1996.
[3] C. Blum, "Ant colony optimization: Introduction and recent trends, " Physics of Life Reviews, vol. 2, no. 4, pp. 353-373, 2005.
[4] Y. Li and S. Gong, "Dynamic ant colony optimisation for TSP, " The International Journal of Advanced Manufacturing Technology, vol. 22, pp. 528-533, 2003.
[5] C.F. Tsai, C.W. Tsai, and C.C. Tseng, "A new hybrid heuristic approach for sloving large travling salesman problem, " Information Sciences, vol. 166, no. 1, pp. 67-81, 2004.
[6] S.C. Negulescu, C.V. Kifor, and C. O, "Ant colony solving multiple constrains problem: Vehicle route allocation, " International Journal of Computers, Communications and Control, vol. 3, no. 4, pp. 366-373, 2008.
[7] J. Heinonen and F. pettersson, "Hybrid ant colony optimization and visibility studies applied to a job-shop scheduling problem, " Applied Mathematics and Computation, vol. 187, no. 2, pp. 989-998, 2007.
[8] L.Y. Tseng and S.C. Liang , "A hybrid metaheuristic for the quadratic assignment problem," Computational Optimization and Applications, vol. 14, no. 1, pp. 85-113, 2006.
[9] S. Tsutsui, "Solving the quadratic assignment problems using parallel ACO with symmetric multi processing, " Transactions of the Japanese Society for Artificial Intelligence, vol. 24, no. 1, pp. 46-57, 2009.
[10] A.P. Engelbrecht, Computational Intelligence: An Introduction, 2nd, Wiley, 2007.
[11] C. Martinez, O. Castillo, and O. Montiel, "Comparison between ant colony and genetic algorithms for fuzzy system optimization, " Studies in Computational Intelligence, vol. 154, no. 4, pp. 71-86, 2008.
[12] C.F. Juang and C. Lo, "Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm, " Fuzzy Sets and Systems, vol. 159, no. 21, pp. 2910-2926, 2008.
[13] T. Stutzle, H.H. Hoos, "Max-Min ant system, " Future Generation Computer Systems, vol. 16, no. 8, pp. 889-914, 2000.