Risk-Aware MPC for Stochastic Systems with Runtime Temporal Logics
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929331214745600 |
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| author | Engelaar, Maico H. W. Zhang, Zengjie Lazar, Mircea Haesaert, Sofie |
| author_facet | Engelaar, Maico H. W. Zhang, Zengjie Lazar, Mircea Haesaert, Sofie |
| contents | This paper concerns the risk-aware control of stochastic systems with temporal logic specifications dynamically assigned during runtime. Conventional risk-aware control typically assumes that all specifications are predefined and remain unchanged during runtime. In this paper, we propose a novel, provably correct model predictive control scheme for linear systems with additive unbounded stochastic disturbances that dynamically evaluates the feasibility of runtime signal temporal logic specifications and automatically reschedules the control inputs accordingly. The control method guarantees the probabilistic satisfaction of newly accepted specifications without sacrificing the satisfaction of the previously accepted ones. The proposed control method is validated by a robotic motion planning case study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03165 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Risk-Aware MPC for Stochastic Systems with Runtime Temporal Logics Engelaar, Maico H. W. Zhang, Zengjie Lazar, Mircea Haesaert, Sofie Systems and Control Logic in Computer Science This paper concerns the risk-aware control of stochastic systems with temporal logic specifications dynamically assigned during runtime. Conventional risk-aware control typically assumes that all specifications are predefined and remain unchanged during runtime. In this paper, we propose a novel, provably correct model predictive control scheme for linear systems with additive unbounded stochastic disturbances that dynamically evaluates the feasibility of runtime signal temporal logic specifications and automatically reschedules the control inputs accordingly. The control method guarantees the probabilistic satisfaction of newly accepted specifications without sacrificing the satisfaction of the previously accepted ones. The proposed control method is validated by a robotic motion planning case study. |
| title | Risk-Aware MPC for Stochastic Systems with Runtime Temporal Logics |
| topic | Systems and Control Logic in Computer Science |
| url | https://arxiv.org/abs/2402.03165 |