一类非线性非严格反馈系统的间接自适应神经网络控制  

Indirect Adaptive Neural Control Design for a Class of Nonlinear Non-Strict-Feedback Systems

作　　者：丰玲玲 张巍[1] 陈兵[1] 

机构地区：[1]青岛大学复杂性科学研究所,山东青岛266071

出　　处：《青岛大学学报（工程技术版）》2014年第4期1-7,共7页Journal of Qingdao University(Engineering & Technology Edition)

基　　金：国家自然科学基金资助项目(61074008;61174033)

摘　　要：针对一类非严格反馈非线性系统,本文提出了间接自适应神经网络控制器的设计方案,并基于系统函数界函数的单调递增性质,提出了变量分离方法,同时利用间接自适应神经网络控制技术和Backstepping(反推)相结合的方法,构造出间接自适应神经网络状态反馈控制器,所构造的间接自适应控制器,保证了闭环系统的所有信号是半全局有界的,并且系统的所有状态收敛到原点充分小的邻域内,有效地解决了一类非线性非严格反馈系统的自适应神经网络控制问题,并采用数值例子进行仿真实验。仿真结果表明,在本文所提出的控制律的作用下,不但保证了闭环系统的稳定,而且保证所有信号在闭环系统有界。该控制器为一类非严格反馈非线性系统的稳定性控制提供了理论参考。This paper proposes an indirect adaptive neural control scheme for a class of non-strict feedback nonlinear systems.Based on the monotonously increasing property of the bounding functions of system functions,a variable separation method is developed.Furthermore,the adaptive neural control technique and backstepping are combined to construct the indirect adaptive neural controller.The proposed adaptive neural controller guarantees that all the signals of the closed-loop system are bounded,and the system state variables converge to a small neighborhood of the origin.The proposed control strategy successfully solves the adaptive neural control problem of a class of non-strict-feedback nonlinear systems.And the numerical example is used to simulation experiment.The simulation results show that the proposed control law can guarantee the closed-loop system is stable and the closed-loop system trajectories are bounded.The present method provides theoretical reference for the control design of a class of nonlinear non-strict-feedback systems controlled.

关 键 词：间接自适应神经网络 变量分离 BACKSTEPPING 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] O231.2[自动化与计算机技术—控制科学与工程]