On the constrained feedback linearization control based on the MILP representation of a ReLU-ANN
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929356804194304 |
|---|---|
| author | Do, Huu-Thinh Prodan, Ionela |
| author_facet | Do, Huu-Thinh Prodan, Ionela |
| contents | In this work, we explore the efficacy of rectified linear unit artificial neural networks in addressing the intricate challenges of convoluted constraints arising from feedback linearization mapping. Our approach involves a comprehensive procedure, encompassing the approximation of constraints through a regression process. Subsequently, we transform these constraints into an equivalent representation of mixed-integer linear constraints, seamlessly integrating them into other stabilizing control architectures. The advantage resides in the compatibility with the linear control design and the constraint satisfaction in the model predictive control setup, even for forecasted trajectories. Simulations are provided to validate the proposed constraint reformulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03334 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the constrained feedback linearization control based on the MILP representation of a ReLU-ANN Do, Huu-Thinh Prodan, Ionela Systems and Control In this work, we explore the efficacy of rectified linear unit artificial neural networks in addressing the intricate challenges of convoluted constraints arising from feedback linearization mapping. Our approach involves a comprehensive procedure, encompassing the approximation of constraints through a regression process. Subsequently, we transform these constraints into an equivalent representation of mixed-integer linear constraints, seamlessly integrating them into other stabilizing control architectures. The advantage resides in the compatibility with the linear control design and the constraint satisfaction in the model predictive control setup, even for forecasted trajectories. Simulations are provided to validate the proposed constraint reformulation. |
| title | On the constrained feedback linearization control based on the MILP representation of a ReLU-ANN |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2405.03334 |