论文标题
上肢外骨骼的物理人像互动控制具有分散的神经自适应控制方案
Physical Human-Robot Interaction Control of an Upper Limb Exoskeleton with a Decentralized Neuro-Adaptive Control Scheme
论文作者
论文摘要
在物理人类机器人相互作用（PHRI）的概念中，最重要的标准是人类操作员与高度自由度（DOF）机器人相互作用的安全性。因此，强大的控制方案的需求很高，以建立安全的PHRI并稳定非线性，高DOF系统。在本文中，自适应分散的控制策略旨在实现上述目标。为此，人类的上肢模型和外骨骼模型在子系统级别进行了分散和增强，以实现分散的控制动作设计。此外，使用径向基函数神经网络（RBFNN）估算可以抵抗外骨骼运动的人类外源力（HEF）。估计人体上肢和机器人刚体参数，以及HEF估计，使控制器适应了不同的操作员，从而确保了它们的身体安全。使用障碍Lyapunov功能（BLF）来确保机器人可以在安全工作区中运行，同时通过调整控制法来确保稳定。在本研究中还考虑了未知的执行器不确定性和限制因素，以确保平滑安全的PHRI。然后，通过所提出的稳健控制器下的虚拟稳定性概念和虚拟功率流（VPF）建立了整个系统的渐近稳定性。提出了实验结果，并将其与比例衍生（PD）和比例综合衍生物（PID）控制器进行了比较。为了显示设计控制器的鲁棒性及其良好的性能，实验以不同的速度，不同的人类使用者以及未知的干扰存在。拟议的控制器在控制机器人方面表现出完美的性能，而PD和PID控制器甚至无法确保机器人手腕关节的稳定运动。
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.
