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针对无人水下航行器(UUV)在水动力参数变化和外界不确定干扰下的航速控制问题,提出一种基于李雅普诺夫方法的自适应神经网络控制算法。引入RBF神经网络来估计建模误差和海流干扰,并设计自适应学习律来保证神经网络权值的最优估计,保证了系统的航速误差收敛到零。仿真试验结果表明设计的控制器在航速控制过程中可有效抑制UUV载体的模型不确定性及海流干扰,且控制参数易于调节。
Aimed at the speed control problem of unmanned underwater vehicle (UUV) under the variation of hydrodynamic parameters and the uncertainties of the outside world, an adaptive neural network control algorithm based on Lyapunov method is proposed. The RBF neural network is introduced to estimate the modeling error and the current disturbance, and an adaptive learning law is designed to ensure the optimal estimation of the neural network weights and ensure that the system’s speed error converges to zero. The simulation results show that the designed controller can effectively restrain the model uncertainty and current disturbance of UUV carrier during the course of speed control, and the control parameters can be easily adjusted.