摘要
针对一类具有未建模动态和动态扰动且状态不可量测的非线性系统,利用神经网络逼近未知函数设计K-滤波器重构系统状态,提出一种自适应输出反馈控制策略.通过对未建模动态的新刻画,避免动态信号的引入.采用动态面设计方法,取消理论分析中产生的未知连续函数的估计,降低设计的复杂性.利用Lyapunov方法证明了闭环系统的所有信号是半全局一致终结有界的,并通过仿真结果验证了所提出方案的有效性.
An output feedback adaptive control scheme is proposed for a class of nonlinear systems with unmodeled dynamics and dynamic uncertainties as well as the unmeasured states. Neural networks are used to approximate the unknown continuous functions, and the unknown system states are reconstructed by using K-filters. By the novel description to unmodeled dynamics, the dynamic signal used to dominate the unmodeled dynamics in the existing literature is avoided. Based on the dynamic surface design method, the estimations of the unknown continuous functions produced in the course of theoretical analysis are removed. The complexity of the design is reduced. By using the method of the Lyapunov function, all the signals in the closed-loop control system are proved to be bounded semi-globally, uniformly and ultimately. Simulation results illustrate the effectiveness of the proposed approach.
出处
《控制与决策》
EI
CSCD
北大核心
2014年第12期2129-2136,共8页
Control and Decision
基金
国家自然科学基金项目(61174046)
关键词
自适应控制
输出反馈控制
动态面控制
未建模动态
adaptive control
output feedback control
dynamic surface control
unmodeled dynamics
作者简介
夏晓南（1970-），女，博士生，从事智能控制、鲁棒自适应控制的研究
张天平（1964-），男，教授，博士生导师，从事鲁棒自适应控制、非线性控制等研究．
