Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.01597 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916510617829376 |
|---|---|
| author | Ubbink, Johan Viljoen, Ruan Aertbeliën, Erwin Decré, Wilm De Schutter, Joris |
| author_facet | Ubbink, Johan Viljoen, Ruan Aertbeliën, Erwin Decré, Wilm De Schutter, Joris |
| contents | Model predictive control (MPC) has become increasingly popular for the control of robot manipulators due to its improved performance compared to instantaneous control approaches. However, tuning these controllers remains a considerable hurdle. To address this hurdle, we propose a practical MPC formulation which retains the more interpretable tuning parameters of the instantaneous control approach while enhancing the performance through a prediction horizon. The formulation is motivated at hand of a simple example, highlighting the practical tuning challenges associated with typical MPC approaches and showing how the proposed formulation alleviates these challenges. Furthermore, the formulation is validated on a surface-following task, illustrating its applicability to industrially relevant scenarios. Although the research is presented in the context of robot manipulator control, we anticipate that the formulation is more broadly applicable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01597 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Instantaneous to Predictive Control: A More Intuitive and Tunable MPC Formulation for Robot Manipulators Ubbink, Johan Viljoen, Ruan Aertbeliën, Erwin Decré, Wilm De Schutter, Joris Robotics Model predictive control (MPC) has become increasingly popular for the control of robot manipulators due to its improved performance compared to instantaneous control approaches. However, tuning these controllers remains a considerable hurdle. To address this hurdle, we propose a practical MPC formulation which retains the more interpretable tuning parameters of the instantaneous control approach while enhancing the performance through a prediction horizon. The formulation is motivated at hand of a simple example, highlighting the practical tuning challenges associated with typical MPC approaches and showing how the proposed formulation alleviates these challenges. Furthermore, the formulation is validated on a surface-following task, illustrating its applicability to industrially relevant scenarios. Although the research is presented in the context of robot manipulator control, we anticipate that the formulation is more broadly applicable. |
| title | From Instantaneous to Predictive Control: A More Intuitive and Tunable MPC Formulation for Robot Manipulators |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.01597 |