NDLI: Robust higher-order ILC for non-linear discrete-time systems with varying trail lengths and random initial state shifts

Content Provider	IET Digital Library
Author	Wei, Yun Shan Li, Xiao Dong
Abstract	This study addresses a robust iterative learning control (ILC) scheme for non-linear discrete-time systems in which both the trail lengths and the initial state shifts could be randomly variant in iteration domain. The proposed higher-order ILC law guarantees that as the iteration number goes to infinity, the ILC tracking errors at the desired output trail period are bounded in mathematical expectation, and the bound of tracking errors is proportional to the random initial state shifts. Specifically, the ILC tracking errors in mathematical expectation can be driven to zero as the expectation of initial state shifts is zero. Two numerical examples are carried out to demonstrate the effectiveness of the proposed higher-order ILC law.
Starting Page	2440
Ending Page	2447
Page Count	8
ISSN	17518644
Volume Number	11
e-ISSN	17518652
Issue Number	Issue 15, Oct (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/11/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2017.0008
Journal	IET Control Theory & Applications
Publisher Date	2017-06-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Discrete Control System Discrete Time System Higher-order ILC Law ILC Tracking Error Interpolation And Function Approximation Iterative Learning Control Nonlinear Control System Nonlinear Discrete-time System Numerical Analysis Random Initial State Shift Robust Control Robust Higher-order ILC Self Adjusting Control System Varying Trail Lengths
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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