Stable Adaptive Control for Sampled-Data Nonlinear Systems Using Neural Networks
采样非线性系统的神经网络稳定自适应控制

A neural network-based stable adaptive control approach is developed in this paper for a class of sampled-data nonlinear systems, for which the nonlinear system dynamics are either unknown or difficult to obtain. The controller employs Radial Basis Function (RBF) neural networks to adaptively compensate for the plant nonlinearities, and the neural network parameters are adapted using stability theory. A complete stability and tracking error convergence proof is given, and the effectiveness of the proposed control approach is illustrated through simulation studies of a two-link manipulator.