基于积分型李亚普诺夫函数的直接自适应神经网络控制

Direct Adaptive Neural Network Control Based on Integral-Type Lyapunov Function

针对一类具有下三角形函数控制增益矩阵的非线性系统 ,基于滑模控制原理 ,并利用多层神经网络的逼近能力 ,提出了一种直接自适应神经网络控制器设计的新方案 .通过引入积分型李亚普诺夫函数及残差与逼近误差和的上界函数的自适应补偿项 ,证明了闭环系统是全局稳定的 。

A new design scheme of direct adaptive neural network controller for a class of nonlinear systems with a triangular control structure is proposed. The design is based on the principle of sliding mode control and the approximation capability of multilayer neural networks(MNNs). By introducing integral-type Lyapunov function and adopting the adaptive compensation term of the upper bound function of the sum of residual as well as approximation error, the closed-loop control system is shown to be globally stable, with tracking error converging to zero.