HPSO-based fuzzy neural network control for AUV
Lei ZHANG
Yongjie PANG
Yumin SU
Yannan LIANG
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 disturbante.It Call thus be concluded that the proposed algorithm is feasible for application to AUVs.
机标关键词：particle swarm optimizationneural network controllersimulation experimentspremature convergenceconvergence velocitysimulation platformimmune theory
资助基金：the National Natural Science Foundation of China(50579007)
论文发表日期：2008-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：5( 322-326 )
英文信息