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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Veröffentlicht in:2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) S. 599 - 604
Hauptverfasser: Zhou, Wei, Yu, Miao
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2018
Schlagworte:
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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Zusammenfassung: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