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Hexapod Robot Wall-Following Control Using Fuzzy Controller with Advanced Differential Evolution
|關鍵字:||機器人;hexapod robot;沿牆;差分演化法;Wall-Following;Differential Evolution||出版社:||電機工程學系所||引用:|| C. F. Juang, “Combination of on-line clustering and Q-value based GA for reinforcement fuzzy system design,” IEEE Trans. Fuzzy Systems, vol. 13, no. 3, pp. 289-302, June 2005.  R. Alcala, J. Alcala-Fdez, J. Casillas, O. Cordon, and F. Herrera, “Local identification of prototypes for genetic learning of accurate TSK fuzzy rule-based systems,” International Journal of Intelligent Systems, vol. 22, no. 9, pp. 909-941, Sep. 2007.  E. G. Mansoori, M. J. Zolghadri, S. D. Katebi, “SGERD: A steady-state genetic algorithm for extracting fuzzy classification rules from data,” IEEE Trans. Fuzzy Systems, vol. 16, no. 4, pp. 1061-1071, Aug. 2008.  A. Chatterjee, K. Pulasinghe, K. Watanabe, and K. Izumi, “A particle swarm-optimized fuzzy-neural network for voice-controlled robot systems,” IEEE Trans. Ind. Electron., vol. 52, no. 6, pp. 1478–1489, Dec. 2005.  C. F. Juang and C. Lo, “Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence,” Fuzzy Sets and Systems, vol. 159, no. 21, pp. 2910-2926, Nov. 2008.  K. D. Sharma, A. Chatterjee, and A. Rakshit, “A hybrid approach for design of stable adaptive fuzzy controllers employing Lyapunov theory and particle swarm optimization,” IEEE Trans. Fuzzy Systems, vol. 17, no. 2, pp. 329-342, April 2009.  F. J. Lin, L. T. Teng, J. W. Lin and S. Y. Chen, “Recurrent functional-link-based fuzzy-neural-network-controlled induction-generator system using improved particle swarm optimization, IEEE Trans. Ind. Electron., vol. 56, no. 5, pp. 1557-1577, May 2009.  C. F. Juang, C. M. Hsiao, and C. H. Hsu, “Hierarchical cluster-based multi-species particle swarm optimization for fuzzy system optimization,” IEEE Trans. Fuzzy Systems, vol. 18, no. 1, pp. 14-26, Feb. 2010.  Rainer Storn and Kenneth Price, “Differential Evolution – A simple and efficient adaptive scheme for global optimization over continuous spaces,” International Computer Science Institute, 1947. Rainer Storn and Kenneth Price, “Differential Evolution – A simple and efficient Heuristic for Global Optimization over Continuous Spaces,” Journal of Global Optimization, pp.341-359, Nov. 1997. Leandro dos Santos Coelho and Viviana Cocco Mariani, “Combining of Chaotic Differential Evolution and Quadratic Programming for Economic Dispatch Optimization With Valve-Point Effect,” IEEE Transactions on Power Systems, vol.21 no.2, May 2006. Cheng-Hung Chen, Cheng-Jian Lin and Chin-Teng Lin, “Nonlinear System Control Using Adaptive Neural Fuzzy Networks Based on a Modified Differential Evolution,” IEEE Trans. on Systems, vol. 39, no. 4, pp. 459-473,July 2009. C.H. Hsu and C.H. Juang, “Evolutionary Robot Wall-Following Control Using Type-2 Fuzzy Controller with Species-DE Activated Continuous ACO,” IEEE Trans Fuzzy Systems , Dec. 2012.||摘要:||
This thesis proposes advanced species differential evolution (ASDE) algorithm designed fuzzy controller (FC) to perform hexapod robot wall following. The ASDE uses the concept of the species (clustering). The solution vectors are clustered into different species based on their performances at each iteration. The ASDE dynamically generates a species-based mutant vector or a general mutant vector in the mutation operation according to an iteration-based adaptive probability value. The ASDE is applied to design an FC for robot wall-following control. All of the free parameters in the FC are learned through ASDE, which avoids the time-consuming manual design task. The FC inputs are three infrared distance sensor values. The FC controls the swing angle changes of the left- and right-middle legs of the hexapod robot to perform a suitable turning direction while moving forward at the same time. A new cost function is defined to quantitatively evaluate the performance an FC. Two different training environments are created for building this wall-following behavior without an exhaustive collection of input-output training pairs in advance. Simulations are conducted to verify the effectiveness of the evolutionary wall-following learning approach. Comparisons with other advanced differential evolution algorithms show that the ASDE achieves better performance in the wall-following control task.
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