摘要
针对一类未知的纯反馈非线性离散系统,提出了基于反步法设计的自适应神经网络控制方法.为避免反步法设计中可能出现的因果矛盾问题,首先将系统进行等价变换,然后利用隐函数定理证实了理想虚拟控制输入和实际控制输入的存在性.利用高阶神经网络估计这些控制量,并基于反步法设计自适应神经网络控制系统,证明了闭环系统半全局一致最终有界.仿真结果验证了所提出方法的有效性.
For a class of pure-feedback discrete-time nonlinear systems, adaptive neural network control based on backstepping design is proposed. To avoid the causality contradiction problem in backstepping design, the system is firstly transformed through a coordinate transformation. Then implicit function theorem is exploited to assert the existence of the desired virtual controls and practical control. By using high-order neural networks to approximate the desired controls, an effective adaptive neural network control system is developed by backstepping design. The closed- loop system is proved to be semi-globally uniformly ultimately bounded. Simulation result illustrates the effectiveness of the proposed control.
出处
《控制与决策》
EI
CSCD
北大核心
2009年第4期488-493,498,共7页
Control and Decision
基金
国家自然科学基金项目(60828007)
国家973计划项目(2009CB320601)
国家创新研究群体科学基金项目(60821063)
高等学校学科创新引智计划项目(B08015)
关键词
非线性系统
神经网络
自适应控制
反步法
离散系统
Nonlinear systems
Neural networks
Adaptive control
Backstepping
Discrete systems
