Liu Chengguo, Zhao Kai, Si Weiyong, Li Junyang, Yang Chenguang
IEEE Trans Cybern. 2025 Jun;55(6):3005-3016. doi: 10.1109/TCYB.2025.3555104. Epub 2025 May 16.
Human-robot interaction (HRI) is a crucial component in the field of robotics, and enabling faster response, higher accuracy, as well as smaller human effort, is essential to improve the efficiency, robustness, and applicability of HRI-driven tasks. In this article, we develop a novel neuroadaptive admittance control with human motion intention (HMI) estimation and output error constraint for natural and stable interaction. First, the interaction force information of the robot is utilized to predict the HMI and the stiffness in the admittance model is dynamically updated based on surface electromyography (sEMG) signals of the human upper limb to achieve human-like compliance. Then, based on the designed error transformation mechanism, an innovative prescribed performance control (PPC) is proposed that allows the trajectory error to converge to the given constraint range within a predefined time for any bounded initial conditions, thus enabling the robot to maintain a comprehensive performance of moving in the desired direction as guided by the human. Also, an adaptive neural network (NN) is employed to compensate for the uncertainty of robotics systems to improve the tracking accuracy further. According to the Lyapunov stability analysis criterion, our approach ensures that all states of the closed-loop system remain globally uniformly ultimately bounded. Finally, a series of real-world robot experiments demonstrate the effectiveness of the proposed framework.
人机交互（HRI）是机器人技术领域的一个关键组成部分，实现更快的响应、更高的精度以及更小的人力投入，对于提高HRI驱动任务的效率、鲁棒性和适用性至关重要。在本文中，我们开发了一种新颖的神经自适应导纳控制方法，该方法具有人类运动意图（HMI）估计和输出误差约束，以实现自然且稳定的交互。首先，利用机器人的交互力信息来预测HMI，并基于人类上肢的表面肌电图（sEMG）信号动态更新导纳模型中的刚度，以实现类人顺应性。然后，基于设计的误差变换机制，提出了一种创新的预设性能控制（PPC）方法，该方法允许轨迹误差在预定义的时间内收敛到给定的约束范围内，对于任何有界的初始条件均是如此，从而使机器人能够在人类引导下朝着期望的方向保持全面的运动性能。此外，采用自适应神经网络（NN）来补偿机器人系统的不确定性，以进一步提高跟踪精度。根据李雅普诺夫稳定性分析准则，我们的方法确保闭环系统的所有状态保持全局一致最终有界。最后，一系列实际的机器人实验证明了所提出框架的有效性。
