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Contraction Mapping-Based Robust Convergence of Iterative Learning Control With Uncertain, Locally Lipschitz Nonlinearity.
- Source :
-
IEEE Transactions on Systems, Man & Cybernetics. Systems . Feb2020, Vol. 50 Issue 2, p442-454. 13p. - Publication Year :
- 2020
-
Abstract
- This paper studies the output tracking control problems for multiple-input, multiple-output (MIMO) locally Lipschitz nonlinear (LLNL) systems subject to iterative operation and uncertain, iteration-varying external disturbances and initial conditions. Under the assumption of a linear, P-type iterative learning control (ILC) update law, a double-dynamics analysis (DDA) approach is proposed to show the convergence of the ILC process in the presence of the locally Lipschitz nonlinearities and iteration-varying uncertainties. The DDA approach results in a contraction mapping-based convergence condition that guarantees both: 1) the boundedness of all system trajectories and 2) the robust convergence of the output tracking error. Further, a basic system relative degree condition is given that provides a necessary and sufficient (NAS) guarantee of the convergence of the ILC process. As a corollary, it is noted that in the absence of iteration-varying uncertainties, the results likewise provide an NAS convergence guarantee for MIMO LLNL systems. The simulations are presented to illustrate the ideas. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ITERATIVE learning control
*MIMO systems
Subjects
Details
- Language :
- English
- ISSN :
- 21682216
- Volume :
- 50
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Systems, Man & Cybernetics. Systems
- Publication Type :
- Academic Journal
- Accession number :
- 141257666
- Full Text :
- https://doi.org/10.1109/TSMC.2017.2780131