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针对一类具有未知界扰动和子系统部分已知的非线性大系统,结合神经网络逼近方法、滑模控制研究了一种新的分散鲁棒自适应控制方法。所设计的分散控制器分为两部分,一是等效控制器,二是滑模控制器。滑模控制器用来减小系统的跟踪误差,起鲁棒控制作用。文中用神经网络逼近非线性未知函数,将网络权值误差引入到网络权值的自适应律中用以改善系统的动态性能。仿真算例证明了所设计的鲁棒分散控制器是有效的。
A new distributed robust adaptive control method is studied for a class of large-scale nonlinear systems with known disturbances and sub-systems known parts, in conjunction with neural network approximation and sliding mode control. The decentralized controller is divided into two parts, one is the equivalent controller, the other is the sliding mode controller. Sliding mode controller is used to reduce the tracking error of the system and play a robust control role. In this paper, the neural network is used to approximate the nonlinear unknown function, and the network weight error is introduced into the adaptive law of network weights to improve the dynamic performance of the system. The simulation example proves that the designed robust decentralized controller is effective.