基于神经网络的严反馈块非线性系统的鲁棒控制

针对非匹配不确定性的严反馈块非线性系统，基于神经网络提出一种鲁棒控制方法．利用Lyapunov稳定性定理推导出RBF神经网络的全调节律，用于处理系统中的非线性参数不确定性，提高了神经网络的在线逼近能力；采用神经网络和鲁棒控制方法，利用已知信息的同时，对控制系数矩阵未知时的设计问题进行处理，避免了控制器可能的奇异问题；引入非线性跟踪微分器，解决了Backstepping设计中的“计算膨胀”问题．运用Lyapunov稳定性定理证明了闭环系统的所有信号均最终一致有界．

NN-based robust control for strict-feedback block nonlinear systems

Based on neural networks, a robust control design method is proposed for strict-feedback block nonlinear systems with mismatched uncertainties. Radial-basis-function (RBF) neural networks are used to identify the nonlinear parametric uncertainties of the system. And the adaptive tuning rules for updating all the parameters of the RBF neural networks are derived using the Lyapunov stability theorem to improve the approximate ability of the networks on-line. Considering the known information, neural network and robust control are used to deal with the design problem when control coefficient matrices are unknown. For every subsystem, a nonlinear tracking differentiator is introduced to solve the "computer explosion" problem in backstepping design. It is proved that all the signals of the closed-loop system are uniform ultimate bounded.