Control Strategy and Experimental Research of Cable-Driven Lower Limb Rehabilitation Robot

Abstract

The passive training with fixed trajectory is more suitable for the initial rehabilitation training of patients with no muscle strength in the affected limb. In order to meet the needs of patients' rehabilitation training in different rehabilitation stages and improve the active participation and rehabilitation training effect of patients, a fuzzy sliding mode variable admittance (FSMVA) controller based on safety evaluation and supervision is proposed for the cable-driven lower limb rehabilitation robot (CDLR) in this paper. The FSMVA controller consists of an inner loop fuzzy sliding mode controller, an outer loop variable admittance controller, and a safety evaluation and supervision module. Through the safety evaluation index of the CDLR system and the comprehensive evaluation of patients' rehabilitation effect given by rehabilitation physiotherapists, a real-time adjustment algorithm of variable admittance controller parameters is designed, which can realize the real-time switching of training modes and adjustment of variable admittance controller parameters in active training mode and passive training mode. The trajectory tracking experiments with no admittance, fixed admittance, and variable admittance based on safety evaluation and supervision were carried out on the experimental platform. The experimental results show that the proposed FSMVA control strategy has high position control accuracy in the active training mode. In addition, the training intensity and the active participation of patients can be increased according to the patient's exercise ability. In the passive training mode, according to the training needs of patients, the amount of the trajectory adjustment can be increased to improve the comfort of the training process and the safety of patients. It lays a foundation for the further study of the evaluation system of the rehabilitation training process and the experiment of human-machine interaction compliance.

Effectiveness of Intelligent Control Strategies in Robot-Assisted Rehabilitation—A Systematic Review

“…Fuzzy PID controllers are used in [17,25], with [18] using fuzzy PD control. Fuzzy SMC is used for motion control in [19][20][21][22][27][28][29]. Fuzzy logic is used for impedance control in [20,26,30] and for admittance control in [21,29].…”

“…Fuzzy SMC is used for motion control in [19][20][21][22][27][28][29]. Fuzzy logic is used for impedance control in [20,26,30] and for admittance control in [21,29]. Impedance control uses the ratio of position and force to control the force output by the system, while admittance control uses this ratio to control the position output.…”

PARS, low-cost portable rehabilitation system for upper arm

“…Although proposed methodologies and robotic rehabilitation treatments offer new opportunities for the future, in order to remedy for increased healthcare personnel and medical infrastructure needs properly, designed systems should be as available as possible for the majority of the target patient population in terms of costs, usability, and portability. Despite many robotic rehabilitation systems that targets different body extremities [19] , [20] , [21] , [22] , [23] exist, their designs are generally complex in structure that lead increase in overall costs as well as control algorithm complexity. Thus various researches aim to decrease overall costs and bulkiness of the proposed systems in order to have an accessible system for the majority [24] , [25] , [26] .…”

sEMG-Based Gain-Tuned Compliance Control for the Lower Limb Rehabilitation Robot during Passive Training

“…In order to improve the safety and comfort of patients during passive rehabilitation training, numerous studies have been conducted on the compliance control strategy of the lower limb rehabilitation robot. Wang et al [ 23 ] proposed a fuzzy sliding mode variable admittance controller based on safety evaluation and supervision for the cable-driven lower limb rehabilitation robot, which can switch between active training mode and passive training mode and adjust the parameters of the admittance controller. Li et al [ 24 ] designed a multi-modal control scheme for exoskeleton rehabilitation robots, including robot-assisted mode, robot-dominant mode, and safety-stop mode, and verified the effectiveness of the scheme in upper-limb and lower-limb exoskeleton robot systems.…”