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.
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
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
adaptive control
neural networks
uncertain nonlinear systems
dynamic surface control
blockcontrol
