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中文摘要：
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。
英文摘要：
In order to deal with the parameter variations and uncertainties during speed control with unmanned underwater vehicles, an self-adaptive neural network controller is designed based on Lyapunov stability analysis. For the estimation error and current disturbance, the RBF neural network （NN） is introduced to estimate unknown terms where an self-adaptive law is chosen to guarantee optimal estimation of the weight of NN, meanwhile the speed error system can be converged to zero. Simulation results demonstrated that the proposed controller, whose parameters could be set easily, was effective to eliminate the disturbances caused by vehicle＇s nonlinear and model uncertainty, and could overcome current, besides, the control factor is easy to adjust.
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