Adaptive Impedance Control of Upper Limb Exoskeleton Robot Based on Human Motion Data

The upper limb exoskeleton robot provides an effective treatment method for patients with upper limb functional impairments. A humanoid impedance learning controller (HILC) was designed based on upper limb motion data from humans, which enhances the effectiveness of repetitive rehabilitation training based on human motion mechanisms. Based on human kinematic structures and healthy upper limb motion trajectories, a bionic robot dynamic model was developed, which served as the foundation for designing a humanoid robot control system. To address inaccuracies in the robot's dynamic model, differential adaptive estimation is utilized to compensate for model uncertainties, thereby enhancing accuracy. Additionally, the integration of impedance learning control (ILC) and feedforward control (FFC) within the system reduced the time required to reach a stable state, decreased tracking errors compared to traditional controllers, and improved system stability. Finally, experimental simulations based on ideal parameterized upper limb motion data were conducted to validate the controller's performance. The results demonstrated that the control system could accurately track the ideal parameterized upper limb motion trajectories, with the shoulder and elbow joint trajectories of the exoskeleton robot closely resembling those of a healthy human.