基于RBF神经网络的一类非线性时滞系统自适应控制器设计

ADAPTIVE CONTROLLER DESIGN FOR A CLASS OF NONLINEAR TIME-DELAY SYSTEMS BASED ON RBF NEURAL NETWORKS

针对一类带有未知非线性函数的严格反馈非线性时滞系统,设计了一种自适应神经网络控制器.选择径向基函数神经网络逼近未知的非线性函数.所提出的控制方案能保证闭环系统的所有信号是全局一致最终有界的.证明了跟踪误差信号将收敛于一个小紧集内.仿真实例验证了所提出方法的有效性.

An adaptive neural network controller is presented for a class of strict-feedback nonlinear time-delay systems with unknown nonlinear functions. A radial basis function neural network is chosen to approximate the unknown nonlinear functions in this paper. The developed control scheme is able to guarantee global uniform ultimate boundedness of all the signals in the closed-loop systems. The tracking error is proven to converge to a small compact set. A simulation example illustrates the effectiveness of the proposed approach.