Safe Online Control-Informed Learning

• URL: http://arxiv.org/abs/2512.13868v2
• Date: Tue, 23 Dec 2025 21:56:57 GMT
• Title: Safe Online Control-Informed Learning
• Authors: Tianyu Zhou, Zihao Liang, Zehui Lu, Shaoshuai Mou,
• Abstract summary: This paper proposes a Safe Online Control-Informed Learning framework for safety-critical autonomous systems.<n>The framework unifies optimal control, parameter estimation, and safety constraints into an online learning process.
• Score: 4.548770944690356
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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