基于自适应AGBFN的不确定非线性系统的跟踪控制  

Tracking control for uncertain nonlinear system based on adaptive asymmetric Gaussian basis function network

作　　者：刘亚[1] 侯霞[1] 胡寿松[1] 

机构地区：[1]南京航空航天大学自动化学院,江苏南京210016

出　　处：《控制理论与应用》2005年第1期29-34,共6页Control Theory & Applications

基　　金：国家自然科学基金重点资助项目(60234010).

摘　　要：针对一类具有未知不确定性的非线性系统,提出了一种基于观测器的自适应不对称高斯基函数网络(AGBFN)跟踪控制方案.当系统只有输出可以测量时,通过设计观测器对其进行在线状态估计,进而构造反馈控制律和自适应控制律.所提出的完全自适应AGBFN,可以在线更新网络所有参数,克服了传统RBF网络对称性约束,提高了网络的适应性和学习能力,可以有效地对消系统未知不确定项的影响.证明了闭环系统所有误差信号最终一致有界,且系统输出较好地跟踪参考模型输出.仿真结果表明了所提出方法的有效性.An observer-based adaptive asymmetric Gaussian basis function network (AGBFN) tracking control scheme is designed for a class of nonlinear system with unknown uncertain nonlinearities.When only the output of the system is measurable,the observer is designed for state estimation,and then feedback control law and adaptive control law are developed.The proposed full adaptive AGBFN,which eliminates the symmetry restriction of traditional RBFN and provides the neurons with higher flexibility,can get rid of the effect of the unknown uncertainties by updating all parameters of the AGBFN online.All the errors in closed-loop nonlinear system are uniformly ultimately bounded and the output of the system is proven to converge to a small neighborhood of the desired trajectory.Simulation shows that the proposed method is effective.

关 键 词：不对称高斯基函数 自适应神经网络 跟踪控制 不确定非线性系统 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]