发表机构
Nanjing University of Aeronautics and Astronautics; Donghai Laboratory; China Academy of Machinery Beijing Research Institute of Mechanical and Electrical Technology Co., Ltd.（南京航空航天大学; 东海实验室; 中国机械工业北京机电技术研究院有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对缆索驱动机器人的控制挑战，提出基于TDE的AFONTSM控制策略及新型非线性自适应律，实验表明其跟踪精度、自适应性能及鲁棒性优于基线方法与现有自适应律。
AI 中文摘要
缆索驱动机械臂具有强非线性和低结构刚度，这使得在时变不确定性和外部扰动下的精确控制极具挑战性。本文提出一种基于时滞估计（TDE）的自适应分数阶非奇异终端滑模（AFONTSM）控制策略，用于缆索驱动机器人。在基于TDE的无模型框架内，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，构建了鲁棒控制器。主要贡献是提出一种新型自适应律，该自适应律在更新增益中引入自适应指数项，形成非线性自适应机制；该设计通过在平滑跟踪期间抑制噪声引起的抖振，同时在轨迹反转期间保持或增强自适应增益，从而改善不同工况下的自适应调节。李雅普诺夫分析证明了跟踪误差的最终一致有界性。实验结果表明，与基线方法相比，所提控制器对两个关节的RMSE分别降低34.52%和31.11%，ITAE分别降低33.79%和32.97%，ISCT分别降低6.69%和17.77%；与近期报道的自适应律的进一步对比显示，所提自适应律具有更快的自适应响应、更稳定的增益演化和更好的抖振抑制效果；额外的负载测试进一步验证了所提方法的鲁棒性和可重复性。
英文摘要
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.
