Sliding Mode Tracking Control of a Wire-Driven Upper-Limb Rehabilitation Robot with Nonlinear Disturbance Observer

Niu, Jie; Yang, Qianqian; Wang, Xiaoyun; Song, Rong

doi:10.3389/fneur.2017.00646

Cited by 28 publications

(12 citation statements)

References 34 publications

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“…The position tracking of the end-effector is realized through the PID controller, and the PID controller parameters can be adjusted by the fuzzy algorithm according to the change of cable tensions to ensure the safety of the patient [20]. In addition, the robust adaptive control method [26], sliding mode control method [27]- [28], and force-position control method [29] were studied to realize position tracking control of the robot.…”

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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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 CDRR used in this study was reported previously [35], but was upgraded from three cables to redundant four cabledriven robot (Fig. 1).…”

Section: A Kinematics Of Cdrrmentioning

confidence: 99%

Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

et al. 2020

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Rehabilitation robots play an increasingly important role in the recovery of motor function for stroke. To ensure a natural physical human-robot interaction (pHRI) and enhance the active participation of subjects, it is necessary for the robots to understand the human intention and cooperate actively with humanlike characteristics. This study proposed a hybrid active control algorithm with human motion intention detection. The motion intention was defined as the desired position and velocity, which were continuously estimated according to the human upper-limb model and minimum jerk model, respectively. The motion intention was then fed into a hybrid force and position controller of an upper-limb cable driven rehabilitation robot (CDRR). And a three-dimensional reaching task without predefined trajectory was employed to validate the effectiveness of the proposed control algorithm. Experimental results showed that the control algorithm could continuously recognize the human motion intention and enabled the robot better movement performance indicated as smaller offset error, smoother trajectory, and lower impact. The proposed method could guarantee a natural pHRI and improve the engagement of the subjects, which has great potential in clinical applications.

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“…Let

. When the disturbance varies slowly relative to the observer dynamics, which is commonly assumed in observer design [ 48 , 49 ], it is reasonable that

, so we have

…”

Section: Control Strategymentioning

confidence: 99%

Disturbance-Estimated Adaptive Backstepping Sliding Mode Control of a Pneumatic Muscles-Driven Ankle Rehabilitation Robot

Zhu

Zuo

et al. 2017

Sensors

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A rehabilitation robot plays an important role in relieving the therapists’ burden and helping patients with ankle injuries to perform more accurate and effective rehabilitation training. However, a majority of current ankle rehabilitation robots are rigid and have drawbacks in terms of complex structure, poor flexibility and lack of safety. Taking advantages of pneumatic muscles’ good flexibility and light weight, we developed a novel two degrees of freedom (2-DOF) parallel compliant ankle rehabilitation robot actuated by pneumatic muscles (PMs). To solve the PM’s nonlinear characteristics during operation and to tackle the human-robot uncertainties in rehabilitation, an adaptive backstepping sliding mode control (ABS-SMC) method is proposed in this paper. The human-robot external disturbance can be estimated by an observer, who is then used to adjust the robot output to accommodate external changes. The system stability is guaranteed by the Lyapunov stability theorem. Experimental results on the compliant ankle rehabilitation robot show that the proposed ABS-SMC is able to estimate the external disturbance online and adjust the control output in real time during operation, resulting in a higher trajectory tracking accuracy and better response performance especially in dynamic conditions.

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Sliding Mode Tracking Control of a Wire-Driven Upper-Limb Rehabilitation Robot with Nonlinear Disturbance Observer

Cited by 28 publications

References 34 publications

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

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

Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

Disturbance-Estimated Adaptive Backstepping Sliding Mode Control of a Pneumatic Muscles-Driven Ankle Rehabilitation Robot

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