发表机构
Carnegie Mellon University; Constructor University; King Fahd University of Petroleum and Minerals（卡内基梅隆大学; 康斯特大学; 法赫德国王石油与矿业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对动力学突变导致CBF失效的问题，提出LBA-CBF，通过并行推理候选模型并自适应滤波，在仿真和实验中实现安全且高成功率的目标到达。
AI 中文摘要
控制障碍函数（CBF）通过假设的动力学模型来认证指令，因此，当安全性最为关键时，突然的、未测量的状态变化可能会破坏认证。我们提出了回看自适应控制障碍函数（LBA-CBF），该方法通过短回看窗口内的近期预测误差对有限的候选动力学模型库进行排序，并在最佳模型的容差范围内对每个模型强制执行高阶CBF条件，从而在最佳拟合适应与全库鲁棒滤波之间进行权衡。动力学可以非线性地依赖于未知参数，且无需切换模型或连续参数化估计器。我们证明了，只要保留了安全代表性候选模型，任何可行的滤波输入都满足真实CBF条件。在具有突然风向反转和未知载荷的四旋翼仿真中，LBA-CBF在所有随机初始条件下均能安全到达目标，与神谕（oracle）相匹配，而自适应和鲁棒基线方法的成功率为0-88%。包含多达250,000个模型的库可在控制循环内运行，Crazyflie 2.1和F1TENTH实验证明了对风、载荷释放以及变化的轮胎-路面摩擦的适应性。代码、视频和项目详情可在以下网址获取：此https URL
英文摘要
Control barrier functions (CBFs) certify commands through an assumed dynamics model, so an abrupt, unmeasured regime change can undermine the certificate exactly when safety matters most. We present Look-Back Adaptive Control Barrier Functions (LBA-CBF), which rank a finite bank of candidate dynamics by recent prediction error over a short look-back window and enforce the high-order CBF condition against every model within a tolerance of the best, spanning best-fit adaptation to full-bank robust filtering. The dynamics may depend nonlinearly on the unknown parameters, and no switching model or continuously parameterized estimator is required. We prove that any feasible filtered input satisfies the true CBF condition whenever a safety-representative candidate is retained. In quadrotor simulation with abrupt wind reversals and an unknown payload, LBA-CBF is safe and reaches the goal from all random initial conditions, matching an oracle, while adaptive and robust baselines achieve 0-88% success. Banks of up to 250,000 models run inside the control loop, and Crazyflie 2.1 and F1TENTH experiments demonstrate adaptation to wind, payload release, and varying tire-road friction. Code, videos, and project details are available at: https://lla-control.github.io
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