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针对一类伴随型的ｎ阶非线性系统中存在不确定性，引入神经网络作为非线性系统的模型，基于Ｌｙａｐｕｎｏｖ稳定性理论，提出有效的控制律及参数自适应律，由全局不变集定理证明闭环系统是全局跟踪收敛的。仿真研究结果表明了本文方法的有效性，为设计高性能的神经网络控制系统提供了一种强有力的方法
For a class of concomitant n-order nonlinear systems with uncertainties, the neural network is introduced as a nonlinear system model. Based on the Lyapunov stability theory, an effective control law and parameter adaptive law are proposed. The global invariant set theorem The closed-loop system is proved to be global tracking convergence. The simulation results show the effectiveness of the proposed method and provide a powerful method for designing high performance neural network control system