Improved S surface controller and semi-physical simulation for AUV

Abstract

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.

Keywords

S surface controller / AUV / MPSO / semi-physical simulation

References

[1] Chen G., Jia J., Han Q. Study on the strategy of decreasing inertia weight in particle swarm optimization algorithm. Journal of Xi’an Jiao-tong University, 2006, 40(1): 53-56

[2] Kennedy J, Eberhart R (1995). Particle swarm optimization. Proceedings of Neural Networks, Perth, Australia, 1942–1948.

[3] Li A., Qin Z., Bao F. Particle swarm optimization algorithms. Computer Engineering and Applications, 2002, 38(21): 1-3

[4] Liu X., Xu Y. S control of automatic underwater vehicle. Ocean Engineering, 2001, 19(3): 81-84

[5] Luo F., He M. Multilayer feed-forward neural networks based on immune genetic algorithm. Computer Applications, 2005, 25(7): 1661-1662

[6] Meng T., Yu F., Jin Z., Han K. Hardware-in-the-loop simulation method for pico-satellite attitude determination and control system. Journal of Astronautics, 2007, 28(5): 1156-1160

[7] Pan Y., Xu D. Vector modeling and simulation of the autonomous underwater vehicle in spatial motions. Journal of System Simulation, 2003, 15(4): 538-540

[8] Van den Bergh F, Engelbrecht AP (2002). A new locally convergent particle swarm optimizer. Proceedings of IEEE Conference on Systems, Man, and Cybernetics, Hammamet, Tunisia, 96–101.

[9] Wang B., Wan L., Xu Y., Qin Z. Modeling and simulation of a mini AUV in spatial motion. Journal of Marine Science and Application, 2009, 8(1): 7-12

[10] Xu Y., Pang Y., Gan Y. AUV state of the art and prospect. CAAI Transactions on Intelligent Systems, 2006, 1(1): 9-16