Abstract
A novel solution to control an hydraulic excavator during grading tasks is proposed, featuring a Model Predictive Controller designed using Local Model Networks (LMNs), i.e. linear time-invariant dynamic models averaged by nonlinear static functions. The Local Linear Models Tree (LoLiMoT) algorithm is employed to derive an LMN from experimental data of a real excavator. Then, a nonlinear MPC law is designed and implemented on the excavator's embedded control system. To further improve the computational efficiency, a time-varying MPC law is designed as well, where the LMN is linearized in real-time around the current operating point. Experimental results, conducted with the excavator in real-world conditions, show the effectiveness of both approaches in achieving performance comparable to state-of-the-art solutions, while utilizing a more compact dataset and without the need of the hydraulic cylinders' pressure measurement.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the American Control Conference, ACC 2025 |
| Publisher | IEEE |
| Pages | 85-90 |
| Number of pages | 6 |
| ISBN (Electronic) | 979-8-3315-6937-2 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 2025 American Control Conference, ACC 2025 - Denver, United States Duration: 8 Jul 2025 → 10 Jul 2025 |
Publication series
| Name | Proceedings of the American Control Conference |
|---|---|
| ISSN (Print) | 0743-1619 |
Conference
| Conference | 2025 American Control Conference, ACC 2025 |
|---|---|
| Country/Territory | United States |
| City | Denver |
| Period | 8/07/25 → 10/07/25 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/publishing/publisher-dealsOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.