Abstract
Iterative learning control (ILC) algorithms enable high-performance control design using only approximate models of the system. To deal with severe modeling errors, a robustness filter Q is typically employed. Irrespective of the large performance enhancement, these approaches thus require a modeling effort and are subject to a performance/robustness tradeoff. The aim of this paper is to develop a fully data-driven ILC approach that does not require a modeling effort and mitigates the performance/robustness tradeoff. The main idea is to replace the use of a model by dedicated experiments on the system. Convergence conditions are developed in a finite-time framework, and insight in the convergence aspects is presented using a frequency-domain analysis. Extensions to increase the convergence speed are proposed. The developed framework is validated through experiments on a multivariable industrial flatbed printer. Both increased performance and robustness are demonstrated in a comparison with closely related model-based ILC algorithms.
Original language | English |
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Pages (from-to) | 3728-3751 |
Number of pages | 24 |
Journal | International Journal of Robust and Nonlinear Control |
Volume | 28 |
Issue number | 12 |
DOIs | |
Publication status | Published - 1 Aug 2018 |
Keywords
- Data-driven control
- Iterative learning control
- Optimal ILC
- data-driven control
- iterative learning control
- optimal ILC