Fuzzy neural network control of AUV based on IPSO

Abstract

Fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainty in manual adjustments, a novel immune particle swarm optimization (IPSO) algorithm based on immune theory and nonlinear decreasing inertia weight (NDIW) strategy was proposed. Owing to the restraint factor and NDIW strategy, IPSO algorithm can effectively prevents premature convergence and keeps balance between global and local searching ability. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. IPSO algorithm has the fastest convergence velocity and finds the best solutions compared with GA, IGA, basic PSO algorithm etc. in simulation experiments. The experimental results on the AUV simulation platform show that IPSO-based controllers perform well and have strong abilities against current disturbance. Simulation results verify the feasibility in application to AUV.