This study aimed to propose an extended state observer fuzzy-approximation-based active disturbances rejection control (FAADRC) method for a dual-arm humanoid robotic system. The purpose of this control system was to provide disturbance estimation and compensation to enable the humanoid robots to track any continuous desired trajectory, even in the presence of environmental disturbances and parametric uncertainties. The proposed active disturbances rejection controller was analyzed using mathematical modeling, and the robot dual-arm motion information of a number of cases when they simulated the trajectory was examined to verify the model. The extended state observer adaptive fuzzy-approximation control strategy was designed combining the synthesis of the robust design, active disturbances rejection control, and Lyapunov function method so that the proposed FAADRC did not need to know the arms model of the humanoid robot precisely. In the control system proposed in this study, once the desired trajectories of the robot’s dual-arm positions were given, the FAADRC system was closed to any unknown functions and to the derivative of the virtual control law of the humanoid robot system. In this case, a robust controller based on an extended state observer was designed to realize the disturbance estimation and compensation. Using the proposed trajectory tracking, not only were the coordinate motions of a humanoid robot’s two arms generated, but the arms could also be controlled to move to the desired positions. The proposed closed-loop system under the FAADRC design was effective, and the asymptotic stability was successfully achieved. The numerical simulation showed the tracking error comparison and the estimated errors of the extended state observer. Two experimental tests were carried out to prove the performance of the algorithm presented in this study. The experimental results showed that the proposed FAADRC exhibited a better performance than the regular proportional integral derivative controller.

中文翻译：

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基于扩展状态观测器模糊逼近的仿人机器人主动干扰抑制控制方法

本研究旨在为双臂仿人机器人系统提出一种基于扩展状态观测器模糊逼近的主动干扰抑制控制 (FAADRC) 方法。该控制系统的目的是提供干扰估计和补偿，以使仿人机器人能够跟踪任何连续的期望轨迹，即使存在环境干扰和参数不确定性。采用数学建模对提出的自抗扰控制器进行分析，并检查了机器人双臂在模拟轨迹时的一些运动信息，以验证模型。结合鲁棒设计、自抗扰控制、和 Lyapunov 函数方法，使得所提出的 FAADRC 不需要精确地知道人形机器人的手臂模型。在本研究提出的控制系统中，一旦给出了机器人双臂位置的期望轨迹，FAADRC 系统就对任何未知函数和仿人机器人系统的虚拟控制律的导数关闭。在这种情况下，设计了一个基于扩展状态观测器的鲁棒控制器来实现扰动估计和补偿。使用所提出的轨迹跟踪，不仅可以生成人形机器人的两个手臂的坐标运动，而且还可以控制手臂移动到所需的位置。所提出的FAADRC设计下的闭环系统是有效的，并且成功地实现了渐近稳定性。数值模拟显示了扩展状态观测器的跟踪误差比较和估计误差。进行了两个实验测试来证明本研究中提出的算法的性能。实验结果表明，所提出的 FAADRC 表现出比常规比例积分微分控制器更好的性能。