Lower Limb Exoskeleton Control via Linear Quadratic Regulator and Disturbance Observer

Castro, D.; Zhong, Chun-Hao; Braghin, Francesco; Liao, Wei-Hsin

doi:10.1109/robio.2018.8665159

Cited by 10 publications

(11 citation statements)

References 14 publications

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“…On the other hand, few researchers have explored the optimal control, especially the linear quadratic regulator (LQR), to realize the natural gait [ 28 , 29 , 30 , 31 ]. The LQR scheme with full-state feedback yields control measures concerning the whole body compared to PD control for every independent joint [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, the formulation work has not considered the uncertain factors in system dynamics. Castro et al [ 31 ] proposed an integral-aided LQR (LQRi) and unknown input disturbance observer (UIO) to address external interferences of the lower limb exoskeleton system. The results of the proposed control are compared with proportional-derivative control and found to be more effective.…”

Section: Introductionmentioning

confidence: 99%

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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%

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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“…Previous research that has investigated assistive exoskeleton control techniques for adapting to different users can be categorized into: (1) model-based adaptive control techniques [26][27][28][43][44][45][46], and (2) RL approaches [35,[37][38][39][40]. More recently, DRL control methods have been investigated for learning lower and upper limb exoskeleton-based joint control tasks such as assisting in fall prevention with a hip exoskeleton or augmenting knee movements with a knee-based exoskeleton [47][48][49][50].…”

Section: Related Workmentioning

confidence: 99%

“…Traditional adaptive control techniques rely on predefined dynamics models of both the exoskeleton and user to adapt to user-specific characteristics such as walking speed or step length [18,44], and to synchronize with the movements of exoskeleton users through feedback from exoskeleton-user interactions [27,28,45,46].…”

Section: Adaptive Model-based Control Approachesmentioning

confidence: 99%

“…Exoskeleton-user interactions can result in additional unmodelled nonlinearities such as friction and user perturbations [51][52][53]. These complex dynamics models have often been simplified by considering: (1) only the forces resulting from the interaction of the user and exoskeleton [28], or (2) combining the exoskeleton and user as a single body [26,27,44,46]. A simplified dynamics model, however, is not able to represent fully the user-exoskeleton interactions, which can in turn affect the accuracy of the controller [24,53,54].…”

Section: Introductionmentioning

confidence: 99%

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A model-free deep reinforcement learning approach for control of exoskeleton gait patterns

2021

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Lower-body exoskeleton control that adapts to users and provides assistance-as-needed can increase user participation and motor learning and allow for more effective gait rehabilitation. Adaptive model-based control methods have previously been developed to consider a user’s interaction with an exoskeleton; however, the predefined dynamics models required are challenging to define accurately, due to the complex dynamics and nonlinearities of the human-exoskeleton interaction. Model-free deep reinforcement learning (DRL) approaches can provide accurate and robust control in robotics applications and have shown potential for lower-body exoskeletons. In this paper, we present a new model-free DRL method for end-to-end learning of desired gait patterns for over-ground gait rehabilitation with an exoskeleton. This control technique is the first to accurately track any gait pattern desired in physiotherapy without requiring a predefined dynamics model and is robust to varying post-stroke individuals’ baseline gait patterns and their interactions and perturbations. Simulated experiments of an exoskeleton paired to a musculoskeletal model show that the DRL method is robust to different post-stroke users and is able to accurately track desired gait pattern trajectories both seen and unseen in training.

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Investigating User Volitional Influence on Step Length in Powered Exoskeleton Designed for Users with SCI

Cheng¹,

Fong²,

Tan³

et al. 2022

2022 International Conference on Rehabilitation Robotics (ICORR)

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Lower Limb Exoskeleton Control via Linear Quadratic Regulator and Disturbance Observer

Cited by 10 publications

References 14 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

A model-free deep reinforcement learning approach for control of exoskeleton gait patterns

Investigating User Volitional Influence on Step Length in Powered Exoskeleton Designed for Users with SCI

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