关键词: 自主水下航行器, 反步法, 确定学习, 神经网络辨识, 学习控制

Abstract: Converting the depth control problem of Autonomous Underwater Vehicles（AUV） to the analysis of nonlinear strict-feedback system, an adaptive learning control method based on backstepping and deterministic learning is proposed. The Input-to-State Stability（ISS） modular neural network controller that is designed through backstepping method achieves the convergence of partial neural weights and locally-accurate approximation of unknown system dynamics by using neural network identification under the Persistent Excitation（PE） condition, and in order to avoid the controller singularity problem, the ISS-type small gain theorem is used to guarantee the stability of the closed-loop system. The learned knowledge acquired from the dynamical pattern can store as constant neural weights, which can be used to design learning controller. The simulation results show that the controller avoids duplication of training to perform the same task, which improves the performance of the system and verifies the effectiveness of the method.

Key words: autonomous underwater vehicles, backstepping, deterministic learning, neural network identification, learning control