为缆索机器人设计新自适应律，提升抗干扰与轨迹跟踪精度
Time-delay Control Using a New Nonlinear Adaptive Law for Cable-Driven Robots
- 基于时延估计的无模型控制框架，融合分数阶非奇异终端滑模
- 实测误差降低超三成，动态响应更快且抑制抖振更有效
- 适合高精度工业场景，尤其对负载变化敏感的机器人系统
缆索驱动机械臂具有强非线性与低结构刚度，在时变不确定性和外部扰动下精确控制困难。本文提出一种基于时延估计（TDE）的自适应分数阶非奇异终端滑模（AFONTSM）控制策略。在无模型框架内，结合分数阶非奇异终端滑模误差动力学与快速终端滑模趋近律，构建鲁棒控制器。主要贡献在于引入自适应指数项的新自适应律，形成非线性自适应机制，在不同工况下增强调节能力，可在平滑跟踪中抑制噪声引起的抖振，同时在轨迹反转时保持或提升自适应增益。李雅普诺夫分析证明了跟踪误差的最终一致有界性。实验表明，相较于基线方法，所提控制器使两关节的均方根误差（RMSE）分别降低34.52%和31.11%，积分时间加权绝对误差（ITAE）分别下降33.79%和32.97%，积分平方控制时间（ISCT）分别减少6.69%和17.77%。与其他近期自适应律对比显示，本方法具备更快的自适应响应、更稳定的增益演化及更优的抖振抑制效果。额外的负载测试进一步验证了该方法的鲁棒性与可重复性。
原文摘要 · Abstract (English)
Cable-driven manipulators exhibit strong nonlinearities and low structural stiffness, which make precise control challenging under time-varying uncertainties and external disturbances. This paper presents a time-delay-estimation (TDE)-based adaptive fractional-order nonsingular terminal sliding mode (AFONTSM) control strategy for cable-driven robots. A robust controller is constructed within a TDE-based model-free framework by combining fractional-order nonsingular terminal sliding mode error dynamics with a fast terminal sliding mode reaching law. The main contribution is a new adaptive law that introduces an adaptive exponential term into the update gain to form a nonlinear adaptive mechanism. This design improves adaptive regulation under different operating conditions by suppressing noise-induced chattering during smooth tracking while preserving or enhancing the adaptive gain during trajectory reversal. Lyapunov analysis proves the ultimate uniform boundedness of the tracking error. Experimental results show that, compared with the baseline method, the proposed controller reduces RMSE by 34.52% and 31.11%, ITAE by 33.79% and 32.97%, and ISCT by 6.69% and 17.77% for the two joints, respectively. Further comparisons with recently reported adaptive laws demonstrate that the proposed law provides faster adaptive response, more stable gain evolution, and improved chattering suppression. Additional payload tests further verify the robustness and repeatability of the proposed method.
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