为机器人系统设计自适应控制，兼顾不确定性补偿与动态约束保持。
Adaptive Artificial Time-Delay Control with Barrier Lyapunov Constraints for Euler-Lagrange Robots
- 用时延估计在线识别状态相关不确定项。
- 通过障碍李雅普诺夫函数实现位置和速度的时变约束。
- 实验证明在动态扰动下仍能严格满足安全约束。
本文针对欧拉-拉格朗日系统中同时存在状态相关不确定性与时变状态约束的问题，提出一种新型自适应控制框架。该框架结合基于人工时延的不确定性估计（即时延估计）与障碍李雅普诺夫函数，实现约束感知控制。具体地，理论推导了时延估计误差的状态相关上界，并设计自适应律在线估计其参数，从而无需先验模型即可实时补偿不确定性。为保证约束满足，控制器利用障碍李雅普诺夫函数对位置和速度施加时变边界。通过李雅普诺夫分析证明了系统的全局稳定性。五自由度机械臂实验结果表明，相比现有方法，该框架在动态不确定性下仍能严格遵守安全关键约束。
原文摘要 · Abstract (English)
This paper addresses the challenge of simultaneously compensating for state-dependent uncertainties and enforcing time-varying state constraints in Euler-Lagrange systems, a common requirement in robotics that remains underserved by existing control designs. A novel adaptive control framework is developed that combines an artificial time-delay-based uncertainty estimation strategy, also known as time-delay estimation, with a barrier Lyapunov function to enforce constraint-aware control design. Specifically, a state-dependent upper bound on the time-delay estimation approximation error is analytically formulated, and an adaptive law is constructed to estimate its parameters online, enabling real-time state-dependent uncertainty compensation without relying on prior model knowledge. To ensure constraint compliance, the barrier Lyapunov function-based controller enforces time-varying bounds on both position and velocity. The resulting architecture is provably stable via Lyapunov analysis. Experimental results on a five-degree-of-freedom robotic manipulator validate the framework's capability, compared with the state of the art, in maintaining strict adherence to safety-critical constraints under dynamic uncertainties.
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