Adaptive backstepping repetitive learning control design for nonlinear discrete-time systems with periodic uncertainties
Adaptive backstepping repetitive learning control design for nonlinear discrete-time systems with periodic uncertainties
复制标题
具有周期性不确定性的非线性离散时间系统的自适应反步重复学习控制设计
DOI：
10.1002/acs.2492
复制
发表时间：
2015
影响因子：
3.1
通讯作者：
Guangda Hu
中科院分区：
文献类型：
--
作者：
Qiao Zhu;Jianxin Xu;Shiping Yang;Guangda Hu
研究了一类不确定非线性离散时间系统的跟踪问题，其中不确定性包括参数不确定性和外部干扰，具有已知的周期性。重复学习控制（RLC）是处理周期性未知分量的有效工具。利用反推方法，设计了具有周期参数估计的自适应RLC律。通过将参数估计推迟到最后一步进行，克服了鲁棒自适应控制中难以解决的参数过参数化问题。结果表明，在存在周期不确定性的情况下，所提出的无过参数化的自适应RLC律能够保证整个闭环系统状态的完美跟踪和有界性.此外，所开发的控制器的有效性证明了单连杆柔性关节机器人的实施例。版权所有© 2014约翰威利父子有限公司.
This paper addresses a tracking problem for uncertain nonlinear discrete‐time systems in which the uncertainties, including parametric uncertainty and external disturbance, are periodic with known periodicity. Repetitive learning control (RLC) is an effective tool to deal with periodic unknown components. By using the backstepping procedures, an adaptive RLC law with periodic parameter estimation is designed. The overparameterization problem is overcome by postponing the parameter estimation to the last backstepping step, which could not be easily solved in robust adaptive control. It is shown that the proposed adaptive RLC law without overparameterization can guarantee the perfect tracking and boundedness of the states of the whole closed‐loop systems in presence of periodic uncertainties. In addition, the effectiveness of the developed controller is demonstrated by an implementation example on a single‐link flexible‐joint robot. Copyright © 2014 John Wiley & Sons, Ltd.