Dual-Modal Hybrid Control for an Upper-Limb Rehabilitation Robot - Guang Feng; Jiaji Zhang; Guokun Zuo; Maoqin Li; Dexin Jiang; Lei Yang (Machines, 2022)
The recovery treatment of motor dysfunction plays a crucial role in rehabilitation therapy. Rehabilitation robots are partially or fully replacing therapists in assisting patients in exercise by advantage of robot technologies. However, the rehabilitation training system is not yet intelligent enough to provide suitable exercise modes based on the exercise intentions of patients with different motor abilities. In this paper, a dual-modal hybrid self-switching control strategy (DHSS) is proposed to automatically determine the exercise mode of patients, i.e., passive and assistive exercise mode. In this strategy, the potential field method and the ADRC position control are employed to plan trajectories and assist patients’ training. Dual-modal self-switching rules based on the motor and impulse information of patients are presented to identify patients’ motor abilities. Finally, the DHSS assisted five subjects in performing the training with an average deviation error of less than 2 mm in both exercise modes. The experimental results demonstrate that the muscle activation of the subjects differed significantly in different modes. It also verifies that DHSS is reasonable and effective, which helps patients to train independently without therapists.