Reach-Avoid Model Predictive Control with Guaranteed Recursive Feasibility via Input Constrained Backstepping
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866911567191212032 |
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| author | Ding, Jianqiang Bhave, Nishant Jayesh Deka, Shankar A. |
| author_facet | Ding, Jianqiang Bhave, Nishant Jayesh Deka, Shankar A. |
| contents | This letter proposes a novel sampled-data model predictive control framework for continuous control-affine nonlinear systems that provides rigorous reach-avoid and recursive feasibility guarantees under physical constraints. By propagating both input and output constraints through backstepping process, we present a constructive approach to synthesize a reach-avoid invariant set that complies with control input limits. Using this reach-avoid set as a terminal set, we prove that the proposed sampled-data MPC framework recursively admits feasible control inputs that safely steer the continuous system into the target set under fast sampling conditions. Numerical results demonstrate the efficacy of the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03407 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reach-Avoid Model Predictive Control with Guaranteed Recursive Feasibility via Input Constrained Backstepping Ding, Jianqiang Bhave, Nishant Jayesh Deka, Shankar A. Optimization and Control Systems and Control This letter proposes a novel sampled-data model predictive control framework for continuous control-affine nonlinear systems that provides rigorous reach-avoid and recursive feasibility guarantees under physical constraints. By propagating both input and output constraints through backstepping process, we present a constructive approach to synthesize a reach-avoid invariant set that complies with control input limits. Using this reach-avoid set as a terminal set, we prove that the proposed sampled-data MPC framework recursively admits feasible control inputs that safely steer the continuous system into the target set under fast sampling conditions. Numerical results demonstrate the efficacy of the proposed approach. |
| title | Reach-Avoid Model Predictive Control with Guaranteed Recursive Feasibility via Input Constrained Backstepping |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2604.03407 |