Dynamics Based Fuzzy Adaptive Impedance Control for Lower Limb Rehabilitation Robot

Liang, Xu; Wang, Weiqun; Hou, Zeng-Guang; Zhang, Xu; Ren, Shixin; Wang, Jiaxing; Peng, Liang

doi:10.1007/978-3-030-04239-4_28

Cited by 2 publications

(1 citation statement)

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“…Moreover, numerous investigations have been reported on the adaptive control strategy of rehabilitation robot design in the active training process. The method of the impedance control has been presented as an intelligent control method for rehabilitation robot (Jezernik et al , 2004; Xie et al , 2016; Li et al , 2018; Wang and Song, 2019; Liang et al , 2018), which can better the interaction performance of the human–robot system by fine-tuning the level of assistance in proportion to patients’ motion intention and motion state. The non-adjustable parameters of the human–robot system have limited traditional constant-coefficient impedance control method.…”

Section: Introductionmentioning

confidence: 99%

sEMG-based variable impedance control of lower-limb rehabilitation robot using wavelet neural network and model reference adaptive control

Fereydooni

Siahkali

Shayanfar

et al. 2020

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Purpose This paper aims to propose an innovative adaptive control method for lower-limb rehabilitation robots. Design/methodology/approach Despite carrying out various studies on the subject of rehabilitation robots, the flexibility and stability of the closed-loop control system is still a challenging problem. In the proposed method, surface electromyography (sEMG) and human force-based dual closed-loop control strategy is designed to adaptively control the rehabilitation robots. A motion analysis of human lower limbs is performed by using a wavelet neural network (WNN) to obtain the desired trajectory of patients. In the outer loop, the reference trajectory of the robot is modified by a variable impedance controller (VIC) on the basis of the sEMG and human force. Thenceforward, in the inner loop, a model reference adaptive controller with parameter updating laws based on the Lyapunov stability theory forces the rehabilitation robot to track the reference trajectory. Findings The experiment results confirm that the trajectory tracking error is efficiently decreased by the VIC and adaptively correct the reference trajectory synchronizing with the patients’ motion intention; the model reference controller is able to outstandingly force the rehabilitation robot to track the reference trajectory. The method proposed in this paper can better the functioning of the rehabilitation robot system and is expandable to other applications of the rehabilitation field. Originality/value The proposed approach is interesting for the design of an intelligent control of rehabilitation robots. The main contributions of this paper are: using a WNN to obtain the desired trajectory of patients based on sEMG signal, modifying the reference trajectory by the VIC and using model reference control to force rehabilitation robot to track the reference trajectory.

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