Ho Daniel W C, Li Junmin, Niu Yugang
Department of Mathematics, City University of Hong Kong, Kowloon, Hong Kong.
IEEE Trans Neural Netw. 2005 May;16(3):625-35. doi: 10.1109/TNN.2005.844907.
In this paper, an adaptive neural controller for a class of time-delay nonlinear systems with unknown nonlinearities is proposed. Based on a wavelet neural network (WNN) online approximation model, a state feedback adaptive controller is obtained by constructing a novel integral-type Lyapunov-Krasovskii functional, which also efficiently overcomes the controller singularity problem. It is shown that the proposed method guarantees the semiglobal boundedness of all signals in the adaptive closed-loop systems. An example is provided to illustrate the application of the approach.
本文针对一类具有未知非线性的时滞非线性系统，提出了一种自适应神经控制器。基于小波神经网络（WNN）在线逼近模型，通过构造一种新型的积分型Lyapunov-Krasovskii泛函，得到了一种状态反馈自适应控制器，该控制器还有效地克服了控制器奇异性问题。结果表明，所提方法保证了自适应闭环系统中所有信号的半全局有界性。给出了一个例子来说明该方法的应用。
