以進階型差分演化法設計模糊控制器在六足式機器人沿牆控制

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以進階型差分演化法設計模糊控制器在六足式機器人沿牆控制

Hexapod Robot Wall-Following Control Using Fuzzy Controller with Advanced Differential Evolution

陳盈翰 , Masters　　Advisor：莊家峰　　

英文

機器人；沿牆；差分演化法； hexapod robot ； Wall-Following ； Differential Evolution

Abstract │ Reference (13) │ Altmetrics

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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.

Reference ( 13 ) 〈TOP〉

[1] 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.
連結：
[2] 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.
連結：
[3] 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.
連結：
[4] 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.
連結：
[5] 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.
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