Robust Output-Feedback MPC for Nonlinear Systems with Applications to Robotic Exploration
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916688359849984 |
|---|---|
| author | Brown, Scott Khajenejad, Mohammad Suresh, Aamodh Martinez, Sonia |
| author_facet | Brown, Scott Khajenejad, Mohammad Suresh, Aamodh Martinez, Sonia |
| contents | This paper introduces a novel method for robust output-feedback model predictive control (MPC) for a class of nonlinear discrete-time systems. We propose a novel interval-valued predictor which, given an initial estimate of the state, produces intervals which are guaranteed to contain the future trajectory of the system. By parameterizing the control input with an initial stabilizing feedback term, we are able to reduce the width of the predicted state intervals compared to existing methods. We demonstrate this through a numerical comparison where we show that our controller performs better in the presence of large amounts of noise. Finally, we present a simulation study of a robot navigation scenario, where we incorporate a time-varying entropy term into the cost function in order to autonomously explore an uncertain area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09768 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Output-Feedback MPC for Nonlinear Systems with Applications to Robotic Exploration Brown, Scott Khajenejad, Mohammad Suresh, Aamodh Martinez, Sonia Systems and Control This paper introduces a novel method for robust output-feedback model predictive control (MPC) for a class of nonlinear discrete-time systems. We propose a novel interval-valued predictor which, given an initial estimate of the state, produces intervals which are guaranteed to contain the future trajectory of the system. By parameterizing the control input with an initial stabilizing feedback term, we are able to reduce the width of the predicted state intervals compared to existing methods. We demonstrate this through a numerical comparison where we show that our controller performs better in the presence of large amounts of noise. Finally, we present a simulation study of a robot navigation scenario, where we incorporate a time-varying entropy term into the cost function in order to autonomously explore an uncertain area. |
| title | Robust Output-Feedback MPC for Nonlinear Systems with Applications to Robotic Exploration |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2504.09768 |