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Robust Data-Driven Iterative Learning Control for Linear-Time-Invariant and Hammerstein–Wiener Systems
- Source :
- IEEE Transactions on Cybernetics. 53:1144-1157
- Publication Year :
- 2023
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2023.
-
Abstract
- Iterative learning control (ILC) relies on a finite-time interval output predictor to determine the output trajectory in each trial. Robust ILCs intend to model the uncertainties in the predictor and to guarantee the convergence of the learning process subject to such model errors. Despite the vast literature in ILCs, parameterizing the uncertainties with the stochastic errors in the predictor parameters identified from system I/O data and thus robustifying the ILC have not yet been targeted. This work is devoted to solving such problems in a data-driven fashion. The main contributions are two-fold. First, a data-driven ILC method is developed for LTI systems. The relationship is established between the errors in the predictor matrix and the stochastic disturbances to the system. Its robust monotonic convergence (RMC) is then linked with the closed-loop learning gain matrix that contains the predictor uncertainties and is analyzed based on a closed-form expectation of this gain matrix multiplied with its own transpose, that is, in a mean-square sense (MS-RMC). Second, the data-driven ILC and MS-RMC analysis are extended to nonlinear Hammerstein-Wiener (H-W) systems. The advantages of the proposed methods are finally verified via extensive simulations in terms of their convergence and uncorrelated tracking performance with the stochastic parametric uncertainties.
- Subjects :
- Computer science
Iterative learning control
Robust statistics
Interval (mathematics)
Computer Science Applications
Human-Computer Interaction
LTI system theory
Matrix (mathematics)
Control and Systems Engineering
Control theory
Transpose
Convergence (routing)
Electrical and Electronic Engineering
Software
Information Systems
Parametric statistics
Subjects
Details
- ISSN :
- 21682275 and 21682267
- Volume :
- 53
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Cybernetics
- Accession number :
- edsair.doi.dedup.....f2af55087088a108cb2d9ee2d2109691