Application of PSO-BP Network Algorithm in AUV Depth Control

Abstract

In order to improve the depth performance of AUV in parking, a PSO-BP algorithm for the depth control is presented. The algorithm can use the standard particle swarm (PSO) as BP neural network learning method, and which can be evolved in the AUV depth adaptive control. The adaptive controller has adopted the double neural network unit. One of controllers is made use the input terminal to output control quantity on the basis of current displacement and vertical acceleration of the AUV. The other can be recognized on-line by the AUV model identifier. The numerical simulations are given to verify the AUV depth adaptive control by the controller. The results show that the proposed algorithm can significantly improve the AUV depth control performance. The convergence speed of AUV depth control is 4.5 times than the PID algorithm, so the efficiency of the AUV depth is vastly perfected.