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结合高增益观测器,针对相对阶为n的非线性系统,设计了神经网络自适应控制器。利用Lyapnov定理获得了神经网络权值的更新律和控制器的控制律,从而确保了整个闭环系统的稳定性和有界性。由于神经网络不需任何的离线训练,因而该控制器能够广泛应用于一大类非线性性系统的控制中。仿真结果验证了控制方案的有效性。
Combined with high gain observer, a neural network adaptive controller is designed for the nonlinear system with relative order n. Lyapnov theorem is used to obtain the renewal law of neural network weights and the control law of the controller so as to ensure the stability and boundedness of the entire closed-loop system. Since the neural network does not require any offline training, the controller can be widely used in the control of a large class of nonlinear systems. The simulation results verify the effectiveness of the control scheme.