基于模糊高斯基函数神经网络的滑模控制

研究了一类不确定非线性系统的滑模变结构控制,提出了一种基于模糊高斯基函数神经网络的滑模变结构控制器设计方法.模糊神经网络能以任意精度逼近非线性连续函数,因此应用自适应模糊-神经网络控制理论来逼近动态系统的非线性函数,而且将滑模控制方法与模糊-神经网络控制理论相结合,可以使控制器对系统干扰和逼近误差具有很强的鲁棒性.与常规方法相比,这种控制器不仅保证了闭环系统的稳定性,而且有效地消除了颤动现象.仿真结果表明了该方法的有效性.

Sliding mode control based on fuzzy Gaussian function neural network

A new sliding mode method is proposed for a class of nonlinear systems in the presence of disturbances and parameter variations.The variable structure control system based on fuzzy Gaussian function neural network is presentd.Because of the advantages of fuzzyneural systems,which can uniformly approximate nonlinear continuous functions to arbitrary accuracy,adaptive fuzzy-neural control theory is then employed to derive the update laws for approximating the uncertain nonlinear functions of the dynamic system.Furthermore,the sliding mode control method is incorporated into the adaptive fuzzyneural control scheme so that the derived controller is robust with respect to disturbances and approximate errors.Compared with conventional methods,the proposed approach not only assures closed-loop stability,but also diminishes chattering.Simulation results demonstrate the effectiveness of the method.