Adaptive Iterative Learning Control Mechanism for Nonlinear Systems subject to High-Order Internal Model

This technical note addresses an adaptive iterative learning control (AILC) problem for nonlinear dynamical systems with partially unknown iteration-varying parameter. Referring to the scheme of state-space, an AILC effort is presented for randomly varying reference tracking together with initial sh...

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Bibliographic Details
Published in:	2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) pp. 599 - 604
Main Authors:	Zhou, Wei, Yu, Miao
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.05.2018
Subjects:	Adaptation models Adaptive iterative learning control (AILC) High-order internal model (HOIM) Initial shift issue Iterative learning control Iteratively varying parameter Mathematical model Non-repetitive factor Nonlinear systems Target tracking Trajectory Uncertainty
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Summary:	This technical note addresses an adaptive iterative learning control (AILC) problem for nonlinear dynamical systems with partially unknown iteration-varying parameter. Referring to the scheme of state-space, an AILC effort is presented for randomly varying reference tracking together with initial shift problem in iteration domain. Furthermore, the AILC technique is extended to systems with several parameters in discussion. A simulation example confirms the validity of the proposed method.
DOI:	10.1109/DDCLS.2018.8515997