基于神经网络的一类非线性系统的稳定自适应控制器设计方法

A Design Approach of Neural Network Based Stable Adaptive Controller for a Class of Nonlinear Systems

针对一类非线性连续时间系统，其中非线性函数未知，提出了一种基于神经网络的稳定自适应控制方案．由于控制律的选择基于Ｌｙａｐｕｎｏｖ 稳定性理论，因此，该控制方案不仅能够解决这类非线性系统的跟踪问题，而且使得整个闭环系统渐近稳定，克服了许多神经网络控制系统中存在的稳定性问题．

This paper proposes a design scheme of neural network based stable adaptive controller for a class of nonlinear continuous systems with unknown nonlinear function.Due to the fact that the control law is derived based on the stability theory of Lyapunov,the scheme can not only solve the tracking problem of the class of nonlinear systems,but also can it guarantee the asymptotic stability of the closed systems which is superior to many neural network based control schemes.The effectiveness of the proposed scheme is demonstrated by simulation.