Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.10538 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909187249799168 |
|---|---|
| author | Fink, Michael Brüdigam, Tim Wollherr, Dirk Leibold, Marion |
| author_facet | Fink, Michael Brüdigam, Tim Wollherr, Dirk Leibold, Marion |
| contents | Handling uncertainty in model predictive control comes with various challenges, especially when considering state constraints under uncertainty. Most methods focus on either the conservative approach of robustly accounting for uncertainty or allowing a small probability of constraint violation. In this work, we propose a linear model predictive control approach that minimizes the probability that linear state constraints are violated in the presence of additive uncertainty. This is achieved by first determining a set of inputs that minimize the probability of constraint violation. Then, this resulting set is used to define admissible inputs for the optimal control problem. Recursive feasibility is guaranteed and input-to-state stability is proved under assumptions. Numerical results illustrate the benefits of the proposed model predictive control approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10538 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Minimal Constraint Violation Probability in Model Predictive Control for Linear Systems Fink, Michael Brüdigam, Tim Wollherr, Dirk Leibold, Marion Systems and Control Handling uncertainty in model predictive control comes with various challenges, especially when considering state constraints under uncertainty. Most methods focus on either the conservative approach of robustly accounting for uncertainty or allowing a small probability of constraint violation. In this work, we propose a linear model predictive control approach that minimizes the probability that linear state constraints are violated in the presence of additive uncertainty. This is achieved by first determining a set of inputs that minimize the probability of constraint violation. Then, this resulting set is used to define admissible inputs for the optimal control problem. Recursive feasibility is guaranteed and input-to-state stability is proved under assumptions. Numerical results illustrate the benefits of the proposed model predictive control approach. |
| title | Minimal Constraint Violation Probability in Model Predictive Control for Linear Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2402.10538 |