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S surface controllers have been proven to provide effective motion control for an autonomous underwater vehicle (AUV).However, it is difficult to adjust their control parameters manually.Choosing the optimum parameters for the controller of a particular AUV is a significant challenge.To automate the process, a modified particle swarm optimization (MPSO) algorithm was proposed.It was based on immune theory, and used a nonlinear regression strategy for inertia weight to optimize AUV control parameters.A semi-physical simulation system for the AUV was developed as a platform to verify the proposed control method, and its structure was considered.The simulation results indicated that the semi-physical simulation platform was helpful, the optimization algorithm has good local and global searching abilities, and the method can be reliably used for an AUV.
S surface controllers have been proven to provide effective motion control for an autonomous underwater vehicle (AUV) .However, it is difficult to adjust their control parameters manually. Choosing the optimum parameters for the controller of a particular AUV is a significant challenge. Auto automate the process, a modified particle swarm optimization (MPSO) algorithm was proposed. It was based on immune theory, and used a nonlinear regression strategy for inertia weight to optimize AUV control parameters. A semi-physical simulation system for the AUV was developed as a platform to verify the proposed control method, and its structure was considered. the simulation results indicated that the semi-physical simulation platform was helpful, the optimization algorithm has good local and global searching abilities, and the method can be securely used for an AUV.