In the concept of physical human-robot interaction (pHRI), the most important criterion is the safety of a human operator interacting with a high degrees of freedom (DoF) robot. Therefore, a robust control scheme is of high demand to establish safe pHRI and stabilize nonlinear, high DoF systems. In this paper, an adaptive decentralized control strategy is designed to accomplish mentioned objectives. To do so, human upper limb model and exoskeleton model are decentralized and augmented at the subsystem level to be able to design a decentralized control action. Moreover, human exogenous force (HEF) that can resist exoskeleton motion is estimated using radial basic function neural networks (RBFNNs). Estimating both human upper limb and robot rigid body parameters along with HEF estimation makes the controller adaptable to different operators, ensuring their physical safety. The \emph{barrier Lyapunov function} (BLF), on the other hand, is employed to guarantee that the robot will work in a safe workspace while ensuring stability by adjusting the control law. Additionally, unknown actuator uncertainty and constraints are considered in this study to ensure a smooth and safe pHRI. Then, the asymptotic stability of the whole system is established by means of the \emph{virtual stability} concept and \emph{virtual power flows} (VPFs). Numerical and experimental results are provided and compared to PD controller to demonstrate the excellent performance of the proposed controller. As a result, the proposed controller accomplished all the control objectives with nearly zero error and low computed torque, ensuring physical safety in pHRI.

翻译：在人体-机器人物理互动(pHRI)概念中,最重要的标准是操作者与高度自由(DoF)机器人互动的人体操作者的安全性。 因此, 强大的控制机制是建立安全的 PHRI 和稳定非线性高的 doF 系统。 在本文中, 适应性分散控制战略的设计是为了实现上述目标。 为此, 人体上肢模型和外骨骼模型在子系统一级被分散和扩展, 以便能够设计分散的控制行动。 此外, 能够抵抗外骨骼运动的人体外源力量 {HEF] 正在使用远程基本功能神经网络( RBFNNNSs) 进行估算。 将人体上肢和机器人僵硬体参数与 HEF 估算结合起来, 使控制者适应不同的操作者, 确保其身体安全。 为了保证机器人在安全的工作空间里工作正常工作, 通过调整控制法律确保稳定性, 未知的操作者不确定性和限制 。 此外, 本项研究中考虑的相对稳定性概念的稳定性 。