Abstract
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.
Highlights
Over the last two decades, an increasing number of neurological disorders such as stroke, spinal cord injury, and Parkinson’s disease have been observed in different age groups
The key highlights of the present work are as follows: (i) The input-output feedback linearization approach is represented to linearize the nonlinear dynamics of the lower limb exoskeleton system (ii) A robust offline linear quadratic regulator (LQR)-based neural-fuzzy control scheme is designed to deal with payload uncertainties (iii) A disturbance estimator is proposed using an online adaptation of firing strength in offline designed LQR-NF architecture (iv) The simulation results are carried out for the RLQRNF control scheme and compared with an exponential reaching law-based sliding mode control (ERL-SM) to track the desired gait trajectory during passive therapeutic training (v) The robustness performance of the proposed control scheme (RLQR-NF) is investigated by varying payload parameters and inducing different forms of external disturbances
The design procedure of RLQR-NF control is organized into two parts: first, the offline training of a robust LQR-based adaptive neuro-fuzzy inference system (ANFIS) training dataset to deal with parametric uncertainties, and second, the online training of the LQR-based ANFIS architecture using the adaptive law of weights to compensate for the external disturbances
Summary
Over the last two decades, an increasing number of neurological disorders such as stroke, spinal cord injury, and Parkinson’s disease have been observed in different age groups. In this work, a new robust LQR-based neural-fuzzy control scheme is designed for the lower limb exoskeleton system with parametric uncertainties and external disturbances during passive gait rehabilitation training. (i) The input-output feedback linearization approach is represented to linearize the nonlinear dynamics of the lower limb exoskeleton system (ii) A robust offline LQR-based neural-fuzzy control scheme is designed to deal with payload uncertainties (iii) A disturbance estimator is proposed using an online adaptation of firing strength in offline designed LQR-NF architecture (iv) The simulation results are carried out for the RLQRNF control scheme and compared with an exponential reaching law-based sliding mode control (ERL-SM) to track the desired gait trajectory during passive therapeutic training (v) The robustness performance of the proposed control scheme (RLQR-NF) is investigated by varying payload parameters and inducing different forms of external disturbances.
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