基于动态约束重构的控制障碍函数用于高维机器人的安全关键控制
Dynamic Constraint Reconstruction Based Control Barrier Functions for Safety-Critical Control of High-Dimensional Manipulators
浏览论文内容
中文总结 AI 辅助
针对高维机器人受干扰及模型不确定问题，提出基于动态约束重构的控制障碍函数框架，用扩展状态观测器估计干扰并重构安全约束，引入安全裕度，仿真表明该方法能在强干扰下实现零安全违规并提升轨迹跟踪性能。
中文摘要 AI 辅助
控制障碍函数（CBF）为约束非线性系统提供形式上的安全保证，但其有效性依赖于精确的系统动力学。在受未知干扰和模型不确定性影响的高维机器人中，由标称动力学构建的固定安全约束可能与实际系统行为不一致，导致安全性能下降或过度保守。本文提出一种基于动态约束重构的控制障碍函数（DCR-CBF）框架，用于受干扰机器人的安全关键控制。采用扩展状态观测器在线估计集总干扰，并将估计干扰纳入高阶控制障碍函数以根据估计的真实动力学重构安全约束。为解决估计不准确问题，引入安全裕度并推导充分条件以保证在有界估计误差下的前向不变性。对4自由度挖掘机器人的仿真研究表明，所提DCR-CBF方法在强未知干扰下实现零安全违规，同时与标准和鲁棒CBF方法相比显著提高轨迹跟踪性能。
英文摘要
Control barrier functions (CBFs) provide formal safety guarantees for constrained nonlinear systems, but their effectiveness relies on accurate system dynamics. In high-dimensional manipulators subject to unknown disturbances and model uncertainties, fixed safety constraints constructed from nominal dynamics may become inconsistent with the actual system behavior, leading to safety degradation or excessive conservatism. This paper proposes a dynamic constraint reconstruction based control barrier function (DCR-CBF) framework for safety-critical control of disturbed robotic manipulators. An extended state observer is employed to estimate lumped disturbances online, and the estimated disturbance is incorporated into high-order control barrier functions to reconstruct safety constraints according to the estimated true dynamics. To address estimation inaccuracies, a safety margin is introduced, and a sufficient condition is derived to guarantee forward invariance under bounded estimation errors. Simulation studies on a 4-DOF excavation manipulator demonstrate that the proposed DCR-CBF method achieves zero safety violation under strong unknown disturbances while significantly improving trajectory-tracking performance compared with standard and robust CBF methods.
