Abstract
Iterative Learning Control (ILC) enables performance improvement by learning from previous tasks. The aim of this paper is to develop an ILC approach for Linear Parameter Varying (LPV) systems to enable improved performance and increased convergence speed compared to the linear time-invariant approach. This is achieved through dedicated analysis and norm-optimal synthesis of LPV learning filters. Application to a position-dependent motion system shows a significant improvement in accuracy and convergence rate, thereby confirming the potential of the proposed approach.
| Original language | English |
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| Title of host publication | 2017 IEEE American Control Conference, 23-26 May 2017, Seattle, Washington |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 3518-3523 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-5090-5992-8 |
| ISBN (Print) | 978-1-5090-4583-9 |
| DOIs | |
| Publication status | Published - 29 Jun 2017 |
| Event | 2017 American Control Conference (ACC 2017) - Sheraton Seattle Hotel, Seattle, United States Duration: 24 May 2017 → 26 May 2017 http://acc2017.a2c2.org/ |
Conference
| Conference | 2017 American Control Conference (ACC 2017) |
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| Abbreviated title | ACC 2017 |
| Country/Territory | United States |
| City | Seattle |
| Period | 24/05/17 → 26/05/17 |
| Internet address |