Improved PSO-based S Plane Controller for motion control of underwater vehicle
Based on the analysis of fuzzy control, combined with the form of PID control, the S plane control has been applied. For the default that particle swarm optimization (PSO) presents the phenomenon of precocity and getting in local minima, the basic PSO algorithm is improved. Based on the self-adaptive idea, a concept of dynamic learning factor is introduced. This method could improve the coordination between global and local searching ability and apt to search out global optimum quickly. Another concept of chasten factor is also introduced, the optimizing ability in PSO algorithm which applied in the S plane algorithm in parameter optimization. Finally, the improved PSO algorithm is applied to the S curve motion control of underwater vehicle. The feasibility and advantages of this method are demonstrated by simulation test results.