一类非线性系统的反演变结构控制及应用

针对一类非线性系统 ,将其变换为易于设计变结构控制规律的规范型。利用小脑关节神经网络( CMAC)估计系统中的不确定函数 ,利用 CMAC神经网络和多面滑模技术估计出变换后系统的状态 ,最后利用变结构控制技术设计出控制器 ,特点是无需已知不确定性函数及其各阶导数的上界 ,与经典设计方法相比 ,所提出的方案允许非参数化不确定性。最后将此方法应用于空空弹控制系统的设计中 ,仿真结果表明了该方法的有效性

Backstepping-based variable structure control for a class of nonlinear systems and its application

A class of nonlinear systems is transformed into the form which is convenience for designing variable structure controller. Uncertain functions in the system are estimated by Cerebella Model Arithmetic Computer (CMAC) neural network. The states of the transformed system are estimated by using CMAC neural network and multi sliding surfaces. The controller is designed by using variable structure control. No up bounds of unknown functions and their derivatives are required. Compared with the classical backstepping method, the nonparametric uncertainties are allowed in the proposed scheme. The proposed scheme is applied to the controller design of a air to air missile control system. The simulation results show the effectiveness of the proposed method.