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. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization (HPSO) algorithm based on immune theory and nonlinear decreased inertia weight (NDIW) strategy 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. 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 said that the proposed algorithm is feasible for application to AU Vs.