HPSO-based fuzzy neural network control for AUV

查看全文 作　　者：Lei ZHANG Yongjie PANG Yumin SU Yannan [1]LIANG

机构地区：[1]School of Naval Architecture, Harbin Engineering University, Harbin Heilongjiang 150001, China

出　　处：《控制理论与应用(英文版)》索引2008年第6卷第3期,共5页

基　　金：the National Natural Science Foundation of China (No.50579007) 摘　　要：A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments,a hybrid particle swarm optimization (HPSO) algorithm based on immune theory and nonlinear decreasing inertia weight (NDIW) strategy is proposed. Owing to the restraint factor and NDIW strategy,an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile,the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA,IGA,and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs. 关键词：模糊网络系统自动控制系统自适性控制计算机技术

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HPSO-based fuzzy neural network control for AUV

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