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.

翻译：本文研究在欧拉-拉格朗日系统中同时补偿状态相关不确定性并强制执行时变状态约束的挑战，这是机器人领域中的常见需求，但现有控制设计尚未充分满足。我们提出了一种新型自适应控制框架，将基于人工时滞的不确定性估计策略（也称为时滞估计）与障碍李雅普诺夫函数相结合，以实现约束感知控制设计。具体而言，我们解析地推导了时滞估计近似误差的状态相关上界，并构造了自适应律以在线估计其参数，从而无需依赖先验模型知识即可实现实时状态相关不确定性补偿。为确保约束满足，基于障碍李雅普诺夫函数的控制器对位置和速度施加时变约束。通过李雅普诺夫分析，所提架构被证明是稳定的。在五自由度机械臂上的实验结果验证了该框架相较于现有技术，能够在动态不确定性下严格维持安全关键约束的能力。