Design, Dynamics, and Control of Upper-Limb Exoskeleton Robots

Abstract

Modern technology has enabled great improvements in the design and control of exoskeletons, which can assist users in various applications, including rehabilitation, muscle fatigue reduction, and power augmentation. However, existing power augmentation exoskeletons still face challenges in user comfort and transparency to the user. To improve the power augmentation, an active-passive shoulder exoskeleton was designed in a previous study, which combines the benefits of active and passive actuators, and was controlled by an electromyography-based (EMG) method. However, EMG-based control is sensitive to probe placement and unsuitable for factory use, while force/torque sensors add cost and depend on reliable contact. Therefore, we pursue model-based controllers of this active passive platform, without EMG or force/torque sensors. We first built a high-fidelity skeletal shoulder model in MapleSim, to guide our exoskeleton mechanical and controller designs. It was combined with the exoskeleton model to evaluate the proposed methods. To reduce unnecessary fatigue induced by human exoskeleton misalignment, it is important to understand the moving joint center of the human shoulder complex. The scapular kinematics is especially complex, so we proposed a simplified scapulothoracic model and validated it using bone-pin measurement data. To reduce human effort, a low impedance is required, but the long support chains in shoulder exoskeletons inherently make it prone to vibration. Hence, we proposed a model based vibration attenuation (VA) method for the exoskeleton in question. Static and dynamic human efforts were separately compensated, and the vibration attenuator was derived from identified structural elasticity. Furthermore, variable impedance can improve user comfort, but existing variable impedance profiles require expert tuning; thus, a new variable impedance law (Var-V) was proposed based on human biomechanics, which requires minimal tuning. To evaluate the proposed VA method and variable impedance law, we developed: i) a high-fidelity human-exoskeleton model in MapleSim; ii) a new 1-degree-of-freedom (DOF) human-exoskeleton adaptation model in MATLAB (CNS-MTG); iii) human-in-the-loop (HITL) experiments based on surface electromyography (sEMG). The MapleSim model assumes a perfect human adaptation that is not gradual, but it is more realistic than the 1-DOF adaptation model. The CNS-MTG adaptation model combined the human motor learning with muscle torque generator models, so that it has the advantages of both models. Two sets of HITL experiments were conducted: one for the VA method with a single participant, and the other for both the VA method and variable impedance laws with ten participants.