基于DSC的自适应补偿神经网络控制  

Adaptive Neural Network Control with Compensators Based on Dynamic Surface Control(DSC)

作　　者：李红春[1] 梅建东[2] 

机构地区：[1]江海职业技术学院,江苏扬州225101 [2]扬州职业大学,江苏扬州225000

出　　处：《扬州职业大学学报》2012年第2期25-30,共6页Journal of Yangzhou Polytechnic College

摘　　要：针对一类可转化为"标准块控制形"的MIMO非线性系统,基于动态面控制技术,提出一种鲁棒自适应神经网络控制算法。采用径向基函数神经网络逼近不确定性模型,通过引入一阶滤波器,消除后推设计中由于反复对虚拟控制的求导而导致的复杂性问题,同时补偿项的引入可避免反馈线性化方法中可能出现的控制器奇异性问题,无需控制增益矩阵正定、可逆的条件。利用李亚普诺夫方法,证明了闭环系统是半全局一致终结有界,适当选取设计常数,跟踪误差可收敛到原点的一个小邻域内。计算机仿真结果表明此法的有效性。Based on dynamic surface control, a systematic procedure for synthesis of robust adaptive neural network control is proposed for a class of MIMO nonlinear systems which could be turned to ＂standard block control type＂ ,with unneeded inverse gain matrix in this paper. By employing radial basis function neural net- works （RBFNNs） to approximate uncertain nonlinear system functions, the problem of explosion of complexity in traditional back stepping design, which is caused by repeated differentiations of certain nonlinear functions such as virtual control, is overcome by introducing the first order filter. Moreover, the possible controller sin- gularity in feedback linearization is avoided without projection algorithm. Using Lyapunov method, the closed- loop system is proved to be semi-globally uniformly ultimately bounded, with tracking error converging to a small neighborhood of origin by appropriately choosing design constants. Simulation results demonstrate the ef- fectiveness of the proposed method.

关 键 词：自适应控制 神经网络 不确定非线性系统 动态面控制 块控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]