让机器人在学习中实时保证安全，无需完美初始猜测。
Safe Online Control-Informed Learning
- 用扩展卡尔曼滤波在线更新系统参数，实现高效自适应。
- 软正切屏障函数确保学习与控制全程满足安全约束。
- 适合需要高可靠性的自动驾驶、机械臂等安全关键系统。
本文提出一种面向安全关键自主系统的安全在线控制感知学习框架。该框架将最优控制、参数估计与安全约束统一于在线学习过程。通过扩展卡尔曼滤波实现系统参数的实时增量更新，提升不确定性下的鲁棒性与数据效率。采用软正切屏障函数，在学习与控制过程中强制满足约束，且不依赖高质量初始猜测。理论分析证明了收敛性与安全性保障，框架在倒立摆与机械臂系统上验证了有效性。
原文摘要 · Abstract (English)
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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