机器人交互中实时保障安全与柔顺控制，应对不确定和力约束。
Safety-Critical Adaptive Impedance Control via Nonsmooth Control Barrier Functions under State and Input Constraints
- 用非光滑屏障函数统一处理位置速度约束，实现安全控制
- 在7自由度机械臂上验证，参数不确定性下仍保持安全与跟踪精度
- 适合人机协作、高动态接触任务，对模型误差鲁棒
安全的物理交互对机器人操作臂在人机协作及高接触任务中的部署至关重要，其中不确定性、外部干扰和执行器限制可能同时影响性能与安全性。本文提出一种在线自适应阻抗控制框架，在未知动力学条件下实现关节状态安全并保证柔顺交互。该方法结合基于二次规划的安全滤波器与新型位置-速度非光滑控制屏障函数（NCBF），通过统一的一阶相对阶屏障函数实现位置与速度约束的强制满足。未知动态通过区间型2型模糊逻辑系统在线补偿，执行器转矩极限通过软约束与精确罚函数恢复可行解。引入扰动观测器增强机制以提升对建模误差和外部交互力的鲁棒性。通过复合李雅普诺夫分析，证明了安全集的前向不变性以及阻抗跟踪误差的统一最终有界性。在具有严重参数不确定性与外部交互力矩的7自由度机械臂仿真中，验证了安全约束满足与鲁棒阻抗跟踪能力。
原文摘要 · Abstract (English)
Safe physical interaction is critical for deploying robotic manipulators in human-robot interaction and contact-rich tasks, where uncertainty, external forces, and actuator limitations can compromise both performance and safety. We propose an online adaptive impedance control framework that enforces joint-state safety while achieving compliant interaction under uncertain dynamics. The approach combines a quadratic-program-based safety filter with a novel composed position-velocity non-smooth control barrier function (NCBF), enabling joint position and velocity constraints to be enforced through a unified relative-degree-one barrier. Unknown dynamics are compensated online using an interval type-2 fuzzy logic system, while actuator torque limits are handled through soft constraints with exact penalty recovery of feasible solutions. A disturbance-observer-enhanced safety mechanism improves robustness against modelling errors and external interaction forces. Using composite Lyapunov analysis, we prove forward invariance of the safe set and the uniform ultimately boundedness of the impedance-tracking error. Simulations on a 7-DOF manipulator with severe parametric uncertainty and external interaction wrenches demonstrate safe constraint satisfaction and robust impedance tracking.
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