论文链接：https://www.sciencedirect.com/science/article/pii/S0967066125000899

视频链接：https://b23.tv/YeNy2FO

        机械臂控制系统在实际应用中不可避免地受到建模不确定性的影响，这些不确定性会显著降低系统的运动控制性能。传统方法通常将系统中的不确定性视为集总扰动，并采用干扰估计与衰减抑制（disturbance estimation and attenuation control, DEAC）方法进行处理。尽管现有DEAC 方法在抑制幅值适中、变化缓慢的常规扰动方面表现出良好控制效果，但在应对瞬时剧烈扰动（instantaneous violent disturbance, IVD）时，其性能往往不尽人意。IVD 具有持续时间短、干扰幅值大的特点，其幅值常常超出机器人关节的控制输入饱和值，导致机械臂在受到 IVD 影响时实际轨迹发生显著偏离。由于跟踪误差急剧增大，传统反馈控制器往往会产生过激的控制信号以减小误差，从而引发控制器饱和。这种饱和效应不仅显著降低了系统的相位裕度与稳定余量，而且可能诱发其他复杂非线性现象，最终导致控制性能急剧恶化甚至系统崩溃。

        针对上述问题，本文提出了一种融合轨迹规划与控制策略的双环控制框架。具体而言，在内环中，本文设计了一种非线性自适应鲁棒控制器，该控制器能够同时处理系统中的结构不确定性和非结构不确定性，保证常规工况下的高性能运动跟踪；在外环中，设计了一种最短时间同步轨迹规划算法，以确保偏离的轨迹能够在最短时间内收敛至期望轨迹，从而间接实现轨迹恢复。对比实验结果表明，相较于先进控制方法和基于轨迹平滑处理的方法，本文所提出的控制框架在多自由度机器人受到IVD作用时，能够大幅降低机器人受扰动后的振荡程度，显著缩短偏离轨迹收敛至期望轨迹所需的时间，并有效降低轨迹回复过程中的能量损耗，从而有效提升系统在极端扰动环境下的鲁棒性和动态性能。

        Zixuan Huo, Mingxing Yuan*, Junsheng Huang, Shuaikang Zhang, Xuebo Zhang,Enhanced robust performance oriented integrated planning and control of a robotic manipulator with online instantaneous violent disturbances, Control Engineering Practice, 2025. 

Abstract

        Robotic manipulators suffer from various modeling uncertainties, which generally deteriorate motion control performance. These uncertainties are commonly treated as a lumped disturbance which is then addressed by those disturbance estimation and attenuation control (DEAC) approaches. Although existing DEAC algorithms have shown their effectiveness of rejecting normal disturbances with moderate amplitudes and slow variations, they cannot work well in the presence of an instantaneous violent disturbance (IVD). An IVD is typically characterized by the short duration and large amplitude which exceeds the control input limit of a robotic joint. Consequently, the actual trajectory of a robotic manipulator will deviate from its desired trajectory significantly. Given this issue, an enhanced robust performance oriented two-loop framework which integrates minimum-time trajectory planning and nonlinear control is proposed in this paper. Specifically, a nonlinear adaptive robust controller is synthesized in the inner loop to handle both structured and unstructured uncertainties, while a synchronized trajectory planning algorithm is devised in the outer loop to force the deviated trajectory converging to the desired trajectory in minimum time. Comparative experiments on a robotic manipulator under IVDs show that the deviated trajectory is recovered fastest by the proposed approach.