On iterative learning control for MIMO nonlinear systems in the presence of time-iteration-varying parameters

On iterative learning control for MIMO nonlinear systems in the presence of... In this study, the problem of adaptive iterative learning control (AILC) is considered for a class of multiple-input-multiple-output discrete-time nonlinear systems where the initial condition and reference trajectory could be randomly varying in the iteration domain. It is assumed that the considered systems are subjected to time-iteration-varying unknown parameters. The iteration-varying parameters are generated by a known high-order internal model (HOIM) that is formulated as a polynomial between two consecutive iterations. By incorporating the HOIM into the controller design, the learning convergence of ILC is guaranteed through rigorous analysis under Lyapunov theory. The illustrative example is presented to demonstrate the effectiveness of AILC method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nonlinear Dynamics Springer Journals

On iterative learning control for MIMO nonlinear systems in the presence of time-iteration-varying parameters

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Publisher
Springer Netherlands
Copyright
Copyright © 2017 by Springer Science+Business Media B.V.
Subject
Engineering; Vibration, Dynamical Systems, Control; Classical Mechanics; Mechanical Engineering; Automotive Engineering
ISSN
0924-090X
eISSN
1573-269X
D.O.I.
10.1007/s11071-017-3604-0
Publisher site
See Article on Publisher Site

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