Disturbance Observer-Based Patient-Cooperative Control of a Lower Extremity Rehabilitation Exoskeleton

Chen, Chong; Zhang, Shimin; Zhu, Xiaomin; Shen, Jingyu; Xu, Zhiyao

doi:10.1007/s12541-019-00312-9

Cited by 24 publications

(9 citation statements)

References 28 publications

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“…The controller is found to be more robust towards external disturbances over payload uncertainties. Chen et al [ 26 ] proposed a disturbance estimator-based subject-cooperative control for a weight-reinforced active-assist rehabilitation device. They computed the interaction torques using a backpropagation neural network-aided disturbance observer and proved the stability using the Lyapunov theory.…”

Section: Introductionmentioning

confidence: 99%

Robust LQR-Based Neural-Fuzzy Tracking Control for a Lower Limb Exoskeleton System with Parametric Uncertainties and External Disturbances

Narayan

Dwivedy

2021

Applied Bionics and Biomechanics

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The design of an accurate control scheme for a lower limb exoskeleton system has few challenges due to the uncertain dynamics and the unintended subject’s reflexes during gait rehabilitation. In this work, a robust linear quadratic regulator- (LQR-) based neural-fuzzy (NF) control scheme is proposed to address the effect of payload uncertainties and external disturbances during passive-assist gait training. Initially, the Euler-Lagrange principle-based nonlinear dynamic relations are established for the coupled system. The input-output feedback linearization approach is used to transform the nonlinear relations into a linearized state-space form. The architecture of the adaptive neuro-fuzzy inference system (ANFIS) and used membership function are briefly explained. While varying mass parameters up to 20%, three robust neural-fuzzy datasets are formulated offline with the joint error vector and LQR control input. Thereafter, to deal with external interferences, an error dynamics with a disturbance estimator is presented using an online adaptation of the firing strength matrix. The Lyapunov theory is carried out to ensure the asymptotic stability of the coupled human-exoskeleton system in view of the proposed controller. The gait tracking results for the proposed control scheme (RLQR-NF) are presented and compared with the exponential reaching law-based sliding mode (ERL-SM) controller. Furthermore, to investigate the robustness of the proposed control over LQR control, a comparative performance analysis is presented for two cases of parametric uncertainties and external disturbances. The first case considers the 20% raise in mass values with a trigonometric form of disturbances, and the second case includes the effect of the 30% increment in mass values with a random form of disturbances. The simulation runs have shown the promising gait tracking aspects of the designed controller for passive-assist gait training.

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Section: Introductionmentioning

confidence: 99%

Robust LQR-Based Neural-Fuzzy Tracking Control for a Lower Limb Exoskeleton System with Parametric Uncertainties and External Disturbances

Narayan

Dwivedy

2021

Applied Bionics and Biomechanics

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“…The exoskeleton is connected on two rails to the aluminum alloy skeleton outside. The exoskeleton is composed of two mechanical lower limbs (Chen et al, 2020 ). On the other hand, the New Zealand Biomedical Engineering Laboratory at the University of Auckland designed the gait rehabilitation robot LOPES (lower extremity powered exoskeleton); all design criteria have been considered, as shown in Figure 2D .…”

Section: Lower Limb Exoskeleton Robot (Ller)mentioning

confidence: 99%

“…(C) Lower extremity rehabilitation exoskeleton platform. The exoskeleton is composed of two mechanical lower limbs (Chen et al, 2020 ). (D) LOPES or treadmill-based LLREs.…”

Section: Lower Limb Exoskeleton Robot (Ller)mentioning

confidence: 99%

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

et al. 2023

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Effective control of an exoskeleton robot (ER) using a human-robot interface is crucial for assessing the robot's movements and the force they produce to generate efficient control signals. Interestingly, certain surveys were done to show off cutting-edge exoskeleton robots. The review papers that were previously published have not thoroughly examined the control strategy, which is a crucial component of automating exoskeleton systems. As a result, this review focuses on examining the most recent developments and problems associated with exoskeleton control systems, particularly during the last few years (2017–2022). In addition, the trends and challenges of cooperative control, particularly multi-information fusion, are discussed.

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“…The robustness of the controller is clearly visible from the Figs. 17,18,19,20,21, where wind of different frequency and varying velocity are given as disturbance along with different terrain conditions.…”

Section: Analysis Of the Performance Of Sosmc And Astsmc On The Lower Extremity Exoskeletonmentioning

confidence: 99%

“…It produces a continuous control function, which conducts the sliding surface and its derivative to zero in finite time in the existence of the smooth matched disturbances with bounded gradient and known boundary. Several variants of STSMC have been proposed in the literature in order to overcome the difficulties previously discussed for lower extremity exoskeleton [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] by considering a standard reference signal. In [25], a comparative study of different SMC such as conventional 1st order sliding mode control (SMC), 2nd order SMC (STW), fast terminal SMC (FTSMC), and integral SMC (ISMC) were done.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Evaluation of Adaptive Super Twisting Sliding Mode Control in Lower Extremity Exoskeleton

Ezhilarasi

Nair

2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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In this paper, an integrated human-in-the-loop simulation paradigm for the design and performance analysis of a 6 DOF lower extremity exoskeleton is presented. An adaptive Super Twisting Sliding Mode Controller (ASTSMC) is designed for the trajectory tracking control of the exoskeleton by considering the human motion as reference trajectory. The dynamic model, that include linear and rotational displacement of hip, knee and ankle joints of both the legs is developed using Lagrange energy formulation. The position and angular velocity error of the wearer and the exoskeleton are being considered to establish the control law. Super twisting SMC is a robust control scheme that works effectively in the presence of external disturbances and parameter variations. However, the STSMC introduces chattering in the closed loop because of its high gain, to overcome this drawback, an adaptive STSMC is proposed for the control of exoskeleton against unknown disturbances without chattering. An adaptation scheme using Lyapunov criterion is derived that ensures the stability of the system in closed loop. The performance of the proposed control strategy is verified by implementing on the integrated CAD model of the exoskeleton along with the wearer. The effectiveness of the controller is tested under wind disturbance of varying velocity and direction. The results demonstrate improved tracking performance of the proposed control scheme with least error and less control effort compared to constant gain STSMC in normal and uneven terrain.

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Disturbance Observer-Based Patient-Cooperative Control of a Lower Extremity Rehabilitation Exoskeleton

Cited by 24 publications

References 28 publications

Robust LQR-Based Neural-Fuzzy Tracking Control for a Lower Limb Exoskeleton System with Parametric Uncertainties and External Disturbances

Robust LQR-Based Neural-Fuzzy Tracking Control for a Lower Limb Exoskeleton System with Parametric Uncertainties and External Disturbances

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

Modeling and Evaluation of Adaptive Super Twisting Sliding Mode Control in Lower Extremity Exoskeleton

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