Control strategy and experimental research of a cable-driven lower limb rehabilitation robot

Wang, Yanlin; Wang, Keyi; Mo, Zong-Jun

doi:10.1177/0954406220952510

Cited by 18 publications

(19 citation statements)

References 31 publications

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“…Study (Jung and Bae, 2016) used system identification and linear quadratic methods to determine the plant transfer functions and the controller. Other studies (Zou et al, 2018a(Zou et al, , 2019Wang et al, 2020) listed the system parameters without describing how they were obtained. Using the tentative PID controllers, the current work performed several tests on the active cable-driven robotic system, where the actual force outputs were recorded.…”

Section: Discussionmentioning

confidence: 99%

“…Velocity disturbance is a common issue in force-controlled cable-driven robotic systems, but how to compensate the velocity disturbance is the key issue. The disturbance compensators described in the literature (Jung and Bae, 2016;Zhang et al, 2017;Zou et al, 2018a,b;Wang et al, 2019Wang et al, , 2020, due to different system dynamics, could not be easily applied in the current study. The current work presented a novel approach to address this issue.…”

Section: Introductionmentioning

confidence: 98%

“…In contrast to force control in a static situation, force control during movement had further disturbance from the actuator velocity (Zou et al, 2018a(Zou et al, , 2019. To compensate such velocity-related disturbances, a force compensator using the structure invariance principle was investigated (Zhang et al, 2017;Zou et al, 2018a,b;Wang et al, 2020), while disturbance observers for force control were also implemented (Jung and Bae, 2016;Wang et al, 2019). These force control algorithms, combined with disturbance compensation, often result in complex control strategies.…”

Section: Introductionmentioning

confidence: 99%

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Development of an Active Cable-Driven, Force-Controlled Robotic System for Walking Rehabilitation

et al. 2021

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In a parallel development to traditional rigid rehabilitation robotic systems, cable-driven systems are becoming popular. The robowalk expander product uses passive elastic bands in the training of the lower limbs. However, a well-controlled assistance or resistance is desirable for effective walking relearning and muscle training. To achieve well-controlled force during locomotion training with the robowalk expander, we replaced the elastic bands with actuator-driven cables and implemented force control algorithms for regulation of cable tensions. The aim of this work was to develop an active cable-driven robotic system, and to evaluate force control strategies for walking rehabilitation using frequency-domain analysis. The system parameters were determined through experiment-assisted simulation. Then force-feedback lead controllers were developed for static force tracking, and velocity-feedforward lead compensators were implemented to reduce velocity-related disturbances during walking. The technical evaluation of the active cable-driven robotic system showed that force-feedback lead controllers produced satisfactory force tracking in the static tests with a mean error of 5.5%, but in the dynamic tests, a mean error of 13.2% was observed. Further implementation of the velocity-feedforward lead compensators reduced the force tracking error to 9% in dynamic tests. With the combined control algorithms, the active cable-driven robotic system produced constant force within the four cables during walking on the treadmill, with a mean force-tracking error of 10.3%. This study demonstrates that the force control algorithms are technically feasible. The active cable-driven, force-controlled robotic system has the potential to produce user-defined assistance or resistance in rehabilitation and fitness training.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Development of an Active Cable-Driven, Force-Controlled Robotic System for Walking Rehabilitation

et al. 2021

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“…and Wang Y.L. et al proposed a compound controller based on modern control theory to eliminate the surplus force caused by the motion of the endeffector and external interference [24]- [25], which effectively improve the loading accuracy of the tension force of the cable-driven units. Zi B. et al designed a twolevel control algorithm based on the fuzzy algorithm and PID algorithm.…”

Section: Dofs Lle (Lower Limb Exoskeletonmentioning

confidence: 99%

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

Wang

et al. 2021

IEEE Access

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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.

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“…Wang Y.L. et al [ 33 ] studied a compound control strategy of the active and passive training mode for a CDLR to reduce the surplus force generated by the movement of the end-effector and interference. Zi B.…”

Section: Introductionmentioning

confidence: 99%

Safety Evaluation and Experimental Study of a New Bionic Muscle Cable-Driven Lower Limb Rehabilitation Robot

Wang

et al. 2020

Sensors

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Safety is a significant evaluation index of rehabilitation medical devices and a significant precondition for practical application. However, the safety evaluation of cable-driven rehabilitation robots has not been reported, so this work aims to study the safety evaluation methods and evaluation index of cable-driven rehabilitation robots. A bionic muscle cable (BM cable) is proposed to construct a bionic muscle cable-driven lower limb rehabilitation robot (BM-CDLR). The working principle of the BM-CDLR is introduced. The safety performance factors are defined based on the mechanical analysis of the BM-CDLR. The structural safety evaluation index and the use safety evaluation index of the BM-CDLR are given by comprehensively considering the safety performance factors and a proposed speed influence function. The effect of the structural parameters of the elastic elements in the BM cable on the safety performance factors and safety of the BM-CDLR is analyzed and verified by numerical simulations and experimental studies. The results provide the basis for further study of the compliance control strategy and experiments of the human-machine interaction of the BM-CDLR.

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Control strategy and experimental research of a cable-driven lower limb rehabilitation robot

Cited by 18 publications

References 31 publications

Development of an Active Cable-Driven, Force-Controlled Robotic System for Walking Rehabilitation

Development of an Active Cable-Driven, Force-Controlled Robotic System for Walking Rehabilitation

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

Safety Evaluation and Experimental Study of a New Bionic Muscle Cable-Driven Lower Limb Rehabilitation Robot

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