Although impedance control has huge application potential in human–robot cooperation, its engineering application is still quite limited, owing to the high nonlinearity of the human–robot dynamics and disturbances. This article presents a novel adaptive neural network controller with extended state observer for the human–robot interaction using output feedback. The adaptive neural network with extended state observer integrates the adaptive neural network and extended state observer to combine their advantages. The proposed algorithm can address the challenges encountered in human–machine systems, for example, slow convergence of neural networks, internal and external disturbances. Output feedback is realized using tracking differentiator to avoid the costly measurements of certain states. The errors of the closed-loop system are proven to converge to a small compact set containing 0 by Lyapunov theory. Simulations and experiments were conducted to verify the effectiveness of the proposed controller. Results show that the proposed strategy offers superior convergence and better tracking performance compared with the adaptive neural network. The proposed controller can be widely applied in various human–machine interactions to enhance productivity and efficiency.

中文翻译：

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通过跟踪微分器的输出反馈，具有扩展状态观察器的人机交互自适应神经阻抗控制

尽管阻抗控制在人机协作中具有巨大的应用潜力，但由于人机动力学和干扰的高度非线性，其工程应用仍然相当有限。本文提出了一种具有扩展状态观察器的新型自适应神经网络控制器，用于使用输出反馈的人机交互。具有扩展状态观察器的自适应神经网络将自适应神经网络和扩展状态观察器结合起来，结合两者的优点。所提出的算法可以解决人机系统中遇到的挑战，例如神经网络收敛缓慢、内部和外部干扰。使用跟踪微分器实现输出反馈，以避免对某些状态进行昂贵的测量。Lyapunov 理论证明闭环系统的误差收敛到一个包含 0 的小型紧集。进行了仿真和实验以验证所提出的控制器的有效性。结果表明，与自适应神经网络相比，所提出的策略具有更好的收敛性和更好的跟踪性能。所提出的控制器可以广泛应用于各种人机交互，以提高生产力和效率。