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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.04856 |
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| _version_ | 1866914179846242304 |
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| author | Dzhumageldyev, Kerim Airaldi, Filippo Dabiri, Azita |
| author_facet | Dzhumageldyev, Kerim Airaldi, Filippo Dabiri, Azita |
| contents | Optimal control strategies are often combined with safety certificates to ensure both performance and safety in safety-critical systems. A prominent example is combining Model Predictive Control (MPC) with Control Barrier Functions (CBF). Yet, efficient tuning of MPC parameters and choosing an appropriate class $\mathcal{K}$ function in the CBF is challenging and problem dependent. This paper introduces a safe model-based Reinforcement Learning (RL) framework where a parametric MPC controller incorporates a CBF constraint with a parameterized class $\mathcal{K}$ function and serves as a function approximator to learn improved safe control policies from data. Three variations of the framework are introduced, distinguished by the way the optimization problem is formulated and the class $\mathcal{K}$ function is parameterized, including neural architectures. Numerical experiments on a discrete double-integrator with static and dynamic obstacles demonstrate that the proposed methods improve performance while ensuring safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04856 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safe model-based Reinforcement Learning via Model Predictive Control and Control Barrier Functions Dzhumageldyev, Kerim Airaldi, Filippo Dabiri, Azita Systems and Control Optimal control strategies are often combined with safety certificates to ensure both performance and safety in safety-critical systems. A prominent example is combining Model Predictive Control (MPC) with Control Barrier Functions (CBF). Yet, efficient tuning of MPC parameters and choosing an appropriate class $\mathcal{K}$ function in the CBF is challenging and problem dependent. This paper introduces a safe model-based Reinforcement Learning (RL) framework where a parametric MPC controller incorporates a CBF constraint with a parameterized class $\mathcal{K}$ function and serves as a function approximator to learn improved safe control policies from data. Three variations of the framework are introduced, distinguished by the way the optimization problem is formulated and the class $\mathcal{K}$ function is parameterized, including neural architectures. Numerical experiments on a discrete double-integrator with static and dynamic obstacles demonstrate that the proposed methods improve performance while ensuring safety. |
| title | Safe model-based Reinforcement Learning via Model Predictive Control and Control Barrier Functions |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.04856 |