针对机器人表面处理中的动态力约束，提出鲁棒自适应控制方法确保质量稳定。
Robust Adaptive Time-Varying Control Barrier Function with Application to Robotic Surface Treatment
- 结合鲁棒自适应CBF与输入到状态安全，应对模型不确定性和干扰
- 通过集成员识别降低保守性，在仿真与实机上验证质量达标
- 适合需动态力控制的工业机器人任务，如精密喷涂或打磨
基于集合不变性技术的控制屏障函数（CBFs）可用于实现时变约束，如保持与动态障碍物的安全距离。然而，现有方法常忽略模型不确定性。为此，本文提出一种基于CBFs的鲁棒自适应控制器设计，可在考虑参数不确定性与加性扰动的情况下，实现时变约束。首先，利用鲁棒自适应控制屏障函数（RaCBFs）处理模型不确定性，并引入输入到状态安全（ISSf）概念以保证对输入扰动的鲁棒性。为进一步缓解鲁棒性带来的固有保守性，还引入集成员识别方案。在需要时变力边界以确保均匀质量的机器人表面处理任务中，通过数值仿真和真实机器人实验验证了该方法的有效性，证明质量可被形式化保证在可接受范围内。
原文摘要 · Abstract (English)
Set invariance techniques such as control barrier functions (CBFs) can be used to enforce time-varying constraints such as keeping a safe distance from dynamic objects. However, existing methods for enforcing time-varying constraints often overlook model uncertainties. To address this issue, this paper proposes a CBFs-based robust adaptive controller design endowing time-varying constraints while considering parametric uncertainty and additive disturbances. To this end, we first leverage Robust adaptive Control Barrier Functions (RaCBFs) to handle model uncertainty, along with the concept of Input-to-State Safety (ISSf) to ensure robustness towards input disturbances. Furthermore, to alleviate the inherent conservatism in robustness, we also incorporate a set membership identification scheme. We demonstrate the proposed method on robotic surface treatment that requires time-varying force bounds to ensure uniform quality, in numerical simulation and real robotic setup, showing that the quality is formally guaranteed within an acceptable range.
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