Safe Online Control-Informed Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866918261673689088 |
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| author | Zhou, Tianyu Liang, Zihao Lu, Zehui Mou, Shaoshuai |
| author_facet | Zhou, Tianyu Liang, Zihao Lu, Zehui Mou, Shaoshuai |
| contents | This paper proposes a Safe Online Control-Informed Learning framework for safety-critical autonomous systems. The framework unifies optimal control, parameter estimation, and safety constraints into an online learning process. It employs an extended Kalman filter to incrementally update system parameters in real time, enabling robust and data-efficient adaptation under uncertainty. A softplus barrier function enforces constraint satisfaction during learning and control while eliminating the dependence on high-quality initial guesses. Theoretical analysis establishes convergence and safety guarantees, and the framework's effectiveness is demonstrated on cart-pole and robot-arm systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13868 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safe Online Control-Informed Learning Zhou, Tianyu Liang, Zihao Lu, Zehui Mou, Shaoshuai Systems and Control Machine Learning Optimization and Control This paper proposes a Safe Online Control-Informed Learning framework for safety-critical autonomous systems. The framework unifies optimal control, parameter estimation, and safety constraints into an online learning process. It employs an extended Kalman filter to incrementally update system parameters in real time, enabling robust and data-efficient adaptation under uncertainty. A softplus barrier function enforces constraint satisfaction during learning and control while eliminating the dependence on high-quality initial guesses. Theoretical analysis establishes convergence and safety guarantees, and the framework's effectiveness is demonstrated on cart-pole and robot-arm systems. |
| title | Safe Online Control-Informed Learning |
| topic | Systems and Control Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2512.13868 |