Within the concept of physical human-robot interaction (pHRI), the most important criterion is the safety of the human operator interacting with a high degree of freedom (DoF) robot. Therefore, a robust control scheme is in high demand to establish safe pHRI and stabilize nonlinear, high DoF systems. In this paper, an adaptive decentralized control strategy is designed to accomplish the abovementioned objectives. To do so, a human upper limb model and an exoskeleton model are decentralized and augmented at the subsystem level to enable a decentralized control action design. Moreover, human exogenous force (HEF) that can resist exoskeleton motion is estimated using radial basis 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 barrier Lyapunov function (BLF) is employed to guarantee that the robot can operate in a safe workspace while ensuring stability by adjusting the control law. Unknown actuator uncertainty and constraints are also 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 virtual stability concept and virtual power flows (VPFs) under the proposed robust controller. The experimental results are presented and compared to proportional-derivative (PD) and proportional-integral-derivative (PID) controllers. To show the robustness of the designed controller and its good performance, experiments are performed at different velocities, with different human users, and in the presence of unknown disturbances. The proposed controller showed perfect performance in controlling the robot, whereas PD and PID controllers could not even ensure stable motion in the wrist joints of the robot.

翻译：在物理人机交互（pHRI）概念中，最重要的标准是与高自由度（DoF）机器人交互的操作员安全性。因此，亟需一种鲁棒控制方案来建立安全的pHRI并稳定非线性高自由度系统。本文设计了一种自适应去中心化控制策略以实现上述目标。具体而言，将人体上肢模型和外骨骼模型在子系统层面进行去中心化与扩增，以实现去中心化控制动作设计。此外，采用径向基函数神经网络（RBFNN）估计可能阻碍外骨骼运动的人体外生力（HEF）。通过同时估计人体上肢与机器人刚体参数以及HEF估计，使控制器能够适配不同操作员，保障其物理安全。采用障碍李雅普诺夫函数（BLF）通过调整控制律，确保机器人可在安全工作空间内运行并维持稳定性。本研究还考虑了未知执行器不确定性与约束条件，以实现平滑安全的pHRI。随后，基于虚拟稳定性概念与虚拟功率流（VPF），在提出的鲁棒控制器下建立了整个系统的渐近稳定性。实验结果表明：所提控制器在不同速度、不同用户及存在未知干扰的条件下与比例-微分（PD）和比例-积分-微分（PID）控制器进行了对比。结果显示，所提控制器在机器人控制中表现优异，而PD和PID控制器甚至无法保证机器人腕关节的稳定运动。