This paper proposes an adaptive gravity compensation (AGC) control strategy for a cable-driven upper-limb exosuit intended to assist the wearer with lifting tasks. Unlike most model-based control techniques used for this human-robot interaction task, the proposed control design does not assume knowledge of the anthropometric parameters of the wearer's arm and the payload. Instead, the uncertainties in human arm parameters, such as mass, length, and payload, are estimated online using an indirect adaptive control law that compensates for the gravity moment about the elbow joint. Additionally, the AGC controller is agnostic to the desired joint trajectory followed by the human arm. For the purpose of controller design, the human arm is modeled using a 1-DOF manipulator model. Further, a cable-driven actuator model is proposed that maps the assistive elbow torque to the actuator torque. The performance of the proposed method is verified through a co-simulation, wherein the control input realized in MATLAB is applied to the human bio-mechanical model in OpenSim under varying payload conditions. Significant reductions in human effort in terms of human muscle torque and metabolic cost are observed with the proposed control strategy. Further, simulation results show that the performance of the AGC controller converges to that of the gravity compensation (GC) controller, demonstrating the efficacy of AGC-based online parameter learning.

翻译：本文提出了一种针对绳驱动上肢软质外骨骼服的自适应重力补偿控制策略，旨在辅助穿戴者完成提举任务。与多数基于模型的人机交互控制方法不同，该控制设计无需预知穿戴者手臂的人体测量参数及负载信息。通过间接自适应控制律，系统在线估计人体手臂参数中的不确定性（如质量、长度及负载），并补偿肘关节处的重力矩。此外，该自适应重力补偿控制器不依赖于人体手臂的期望关节轨迹。在控制器设计过程中，人体手臂被建模为单自由度机械臂模型，同时构建了将辅助肘部力矩映射至驱动器力矩的绳驱动执行器模型。通过联合仿真验证所提方法性能：在MATLAB中实现的控制输入被施加至OpenSim中的人体生物力学模型，并评估不同负载条件下的效果。实验表明，该控制策略显著降低了人体肌肉力矩与代谢能耗。进一步仿真结果显示，自适应重力补偿控制器的性能收敛至传统重力补偿控制器水平，验证了基于自适应重力补偿的在线参数学习机制的有效性。