Command Filter Backstepping Sliding Model Control for Lower‐Limb Exoskeleton

Abstract

A command filter adaptive fuzzy backstepping control strategy is proposed for lower-limb assisting exoskeleton. Firstly, the humanrobot model is established by taking the human body as a passive part, and a coupling torque is introduced to describe the interaction between the exoskeleton and human leg. Then, Vicon motion capture system is employed to obtain the reference trajectory. For the purpose of obviating the "explosion of complexity" in conventional backstepping, a second-order command filter is introduced into the sliding mode control strategy. The fuzzy logic systems (FLSs) are also applied to handle with the chattering problem by estimating the uncertainties and disturbances. Furthermore, the stability of the closed-loop system is proved based on the Lyapunov theory. Finally, simulation results are presented to illustrate the effectiveness of the control strategy.

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

“…Although the exoskeleton systems exploit the gait of healthy humans to replicate the same using predefined trajectory control schemes, however, in practice, they are unable to attain the proper gait trajectory because of the parametric uncertainties and external disturbances (PUEDs). Therefore, various robust control strategies have been designed to deal with the limitations of classical trajectory tracking control in lower limb exoskeleton systems [ 18 , 19 , 20 , 21 , 22 , 23 ]. Ajayi et al [ 18 ] proposed a bounded control scheme for the rehabilitation of the knee ankle joint of a user in a sitting position.…”

“…The simulation results are presented without and with the effect of the human interaction torque. Yang et al [ 19 ] presented a sliding mode control (SMC) scheme where a second-order command filter-aided backstepping is incorporated to avert the “explosion of complexity.” Moreover, the fuzzy logic is exploited to counter the chattering issues of the control scheme during the estimation of structured and unstructured uncertainties. In another work on robust control, Long et al [ 20 ] presented a hybrid strategy where SMC is augmented with a cerebellar model articulation controller (CMAC) to predict the motion intent of the subject.…”

“…The simulation results are presented without and with the effect of the human interaction torque. Yang et al [19] presented a sliding mode control (SMC) scheme where a second-order command filter-aided backstepping is incorporated to avert the "explosion of complexity." Moreover, the fuzzy logic is exploited to counter the chattering issues of the control scheme during the estimation of structured and unstructured uncertainties.…”

Observer-based finite-time control for trajectory tracking of lower extremity exoskeleton

“…According to the expression of jju i jj 2 = je 1i j p + 1 + e 2 2i + e 2 3i , we can obtain jju i jj ø je 1i j (p + 1)=2 . Based on equation (14), equation ( 12) can be rewritten as…”

“…In Fallaha and Saad, 13 a novel SMC using non-linear model-based switching functions is designed to enhance robustness and reduce chattering. In Yang et al, 14 an adaptive fuzzy backstepping SMC strategy is designed for a lower extremity exoskeleton, and a fuzzy logic approach is used to estimate external disturbances. The aforementioned methods assume that all the components of the state vector are available, and some methods of state estimation are utilized when the system state is unknown.…”