非线性扩展结构大系统自适应神经网络跟踪控制  

Adaptive neural network tracking control for a class of nonlinear largescale systems with expanding construction

作　　者：李小华[1] 徐波[1] 刘洋[1] 

机构地区：[1]辽宁科技大学电子与信息工程学院,辽宁鞍山114051

出　　处：《控制与决策》2016年第10期1860-1866,共7页Control and Decision

基　　金：国家自然科学基金项目(61273011)

摘　　要：针对一类非线性关联大系统在结构扩展时的跟踪控制问题,提出一种采用自适应神经网络的控制方法.该方法要求在不改变原结构系统控制律的前提下设计新加入子系统的控制律和自适应律,使扩展后所有子系统都具有很好的跟踪性能.这里主要利用神经网络的逼近功能以及Backstepping技术来设计自适应律和控制律,通过Lyapunov理论证明在该控制器的作用下闭环系统的所有信号均是有界的,并可使系统准确跟踪.仿真结果验证了所提出方法的有效性.An adaptive neural network（NN） control approach is presented for the tracking control problem of a class of nonlinear large-scale interconnected systems when its structure is expanded. The method needs to design the control laws and adaptive laws of the newly added subsystems without changing the decentralized control laws and adaptive laws of the original subsystems, so that all subsystems in the expanded system have good tracking performances. The approximation function of the NN and the Backstepping technique are used to design the decentralized control laws and adaptive laws of the newly added subsystems. It is proved by the Lyapunov theory that all signals in the closed-loop expanded system controlled by the proposed controllers are bounded, and the expanded system can track accurately. Simulation results show the effectiveness of the proposed method.

关 键 词：非线性大系统 扩展结构 分散控制 自适应跟踪控制 BACKSTEPPING 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]