Report
Overcoming Output Constraints in Iterative Learning Control Systems by Reference Adaptation
| Τίτλος: | Overcoming Output Constraints in Iterative Learning Control Systems by Reference Adaptation |
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| Συγγραφείς: | Meindl, Michael, Molinari, Fabio, Raisch, Jörg, Seel, Thomas |
| Πηγή: | IFAC-PapersOnLine 53 (2020) 1480-1486 |
| Έτος έκδοσης: | 2020 |
| Συλλογή: | Computer Science |
| Θεματικοί όροι: | Electrical Engineering and Systems Science - Systems and Control |
| Περιγραφή: | Iterative Learning Control (ILC) schemes can guarantee properties such as asymptotic stability and monotonic error convergence, but do not, in general, ensure adherence to output constraints. The topic of this paper is the design of a reference-adapting ILC (RAILC) scheme, extending an existing ILC system and capable of complying with output constraints. The underlying idea is to scale the reference at every trial by using a conservative estimate of the output's progression. Properties as the monotonic convergence above a threshold and the respect of output constraints are formally proven. Numerical simulations and experimental results reinforce our theoretical results. Comment: Submitted to IFAC World Congress 2020 |
| Τύπος εγγράφου: | Working Paper |
| DOI: | 10.48550/arXiv.2002.00662 |
| Σύνδεσμος πρόσβασης: | http://arxiv.org/abs/2002.00662 |
| Αριθμός Καταχώρησης: | edsarx.2002.00662 |
| Βάση Δεδομένων: | arXiv |
| DOI: | 10.48550/arXiv.2002.00662 |
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