Fuzzy Sliding Mode Control for 6-DOF Parallel Robot Based on Support Vector Machines

Abstract:

To realize the trajectory tracking control for six degrees of freedom parallel robot, a fuzzy sliding mode control strategy based on support vector machines is presented. Sliding mode control algorithm has complete adaptability to system disturbance and siring in sliding mode, which is used to automatically track the uncertainty of system parameters and external disturbance. Fuzzy algorithm is applied to improve the sliding mode control performance. Fuzzy support vector machines have strong treatment of non-linear signal and generalization ability, which is used to reduce the chattering in sliding mode control. The parameters of fuzzy support machines controller are optimized by hybrid learning algorithm, which combines least square algorithm with improved genetic algorithm, to get the optimal control performance for the object. The simulation results demonstrated that the control strategy designed gets tracking effect with high precision and speed, as well as reduces chattering of control under the condition of model error and external disturbance.

• Periodical: Key Engineering Materials (Volumes 467-469)
• Pages: 645-651
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.467-469.645
• Online since: February 2011
• Authors: De Quan Zhu, Tao Mei, Min Zhou Luo
• Keywords: Fuzzy Control, Genetic Algorithm (GA), Least Square Algorithm, Parallel Robot, Sliding Mode Control (SMC), Support Vector Machine (SVM)

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