基于神经网络的非完整移动机器人鲁棒跟踪控制

针对受非完整条件约束的移动机器人存在的高度非线性、不确定性和外部干扰，提出了一种基于神经网络的鲁棒跟踪控制策略。该控制策略能够对系统中的未知的不确定性和干扰进行补偿。基于Lyapunov方法对控制系统进行设计，保证了系统的稳定性，改善了系统的动态性能。速度跟踪误差、神经网络权值误差和边界估计误差全局有界。仿真实验表明，该控制方法具有很强的鲁棒性和自适应能力。

Neural Network-based Robust Tracking Control for Nonholonomic Mobile Robot

To deal with the unknown factors of nonholonomic mobile robot, such as model uncertainties and external disturbances, a robust controller with bounded estimation based on neural network was proposed. The knowledge of dynamics parameters and external disturbances of the mobile robot were assumed to be unknown. The proposed controller can overcome the uncertainties and the disturbances. And the stability of the proposed controller was proven by using Lyapunov function. The simulation results demonstrate that the proposed method has good robustness and adaptation.