Observer-based Iterative and Repetitive Learning Control for a Class of Nonlinear Systems  

作　　者：Sheng Zhu Xuejie Wang Hong Liu Sheng Zhu;Xuejie Wang;Hong Liu

机构地区：[1]the department of information and electrical engineering,zhejiang university city college,Hangzhou 310015,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2018年第5期990-998,共9页自动化学报（英文版）

基　　金：supported by the Third Level of Hangzhou 131 Young Talent Cultivation Plan Funding;2018 Soft Science Research Project of Zhejiang Provincial Science and Technology Department Zhejiang Province Construction and participate in the“The Belt and Road”Technology Innovation Community Path Research(2018C35029)

摘　　要：In this paper, both output-feedback iterative learning control(ILC) and repetitive learning control(RLC) schemes are proposed for trajectory tracking of nonlinear systems with state-dependent time-varying uncertainties. An iterative learning controller, together with a state observer and a fully-saturated learning mechanism, through Lyapunov-like synthesis, is designed to deal with time-varying parametric uncertainties. The estimations for outputs, instead of system outputs themselves, are applied to form the error equation, which helps to establish convergence of the system outputs to the desired ones. This method is then extended to repetitive learning controller design. The boundedness of all the signals in the closed-loop is guaranteed and asymptotic convergence of both the state estimation error and the tracking error is established in both cases of ILC and RLC. Numerical results are presented to verify the effectiveness of the proposed methods.In this paper, both output-feedback iterative learn- ing control （ILC） and repetitive learning control （RLC） schemes are proposed for trajectory tracking of nonlinear systems with state-dependent time-varying uncertainties. An iterative learning controller, together with a state observer and a fully-saturated learning mechanism, through Lyapunov-like synthesis, is de- signed to deal with time-varying parametric uncertainties. The estimations for outputs, instead of system outputs themselves, are applied to form the error equation, which helps to establish con- vergence of the system outputs to the desired ones. This method is then extended to repetitive learning controller design. The boundedness of all the signals in the closed-loop is guaranteed and asymptotic convergence of both the state estimation error and the tracking error is established in both cases of ILC and RLC. Numerical results are presented to verify the effectiveness of the proposed methods.

关 键 词：Iterative learning control （ILC） observers repetitive learning control （RLC） time-varying parametrization. 

分 类 号：TP271.32[自动化与计算机技术—检测技术与自动化装置] TP273[自动化与计算机技术—控制科学与工程]