摘要
为解决任意初态下的轨迹跟踪问题,针对一类含参数和非参数不确定性的非线性系统,提出基于滤波误差初始修正的自适应迭代学习控制方法.利用修正滤波误差信号设计学习控制器,并以Lyapunov方法进行收敛性能分析.依据类Lipschitz条件处理非参数不确定性,对于处理过程中出现的未知时变参数向量,利用自适应迭代学习机制进行估计.经过足够多次迭代后,藉由修正滤波误差在整个作业区间收敛于零,实现滤波误差本身在预设的作业区间也收敛于零.仿真结果表明了本文所提控制方法的有效性.
This paper presents a filtering-error rectified adaptive iterative learning control method to tackle the trajectory-tracking problem for a class of both parametric and nonparametric uncertain systems in the presence of arbitrary initial states. A novel rectification is made to modify the filtering-error error signal such that the learning control design and performance analysis could be simplified and easy for implementation. The proposed learning control design is a Lyapunov synthesis-based adaptive iterative learning control scheme. The Lipschitz-like assumption is used for handling nonparametric uncertainties, where the estimation for unknown time-varying parameters is given by learning mechanisms.As iteration increases, the rectified filtering-error converges to zero over the entire time interval, and the filtering-error itself converges to zero on the specified interval. Numerical results are presented to demonstrate effectiveness of the proposed learning control scheme.
出处
《自动化学报》
EI
CSCD
北大核心
2016年第4期545-555,共11页
Acta Automatica Sinica
基金
国家自然科学基金(61174034
61374103
61573320)
浙江省高等学校访问学者专业发展项目(FX2013206)资助~~
关键词
迭代学习控制
初值问题
参数不确定性
非参数不确定性
LYAPUNOV方法
Iterative learning control
initial condition problem
parametric uncertainties
nonparametric uncertainties
Lyapunov approach
