A fuzzy neural network-based fractional-order Lyapunov-based robust control strategy for exoskeleton robots: Application in upper-limb rehabilitation

Amir Razzaghian

Mathematics and Computers in Simulation (MATCOM), 2022, vol. 193, issue C, 567-583

Abstract: The paper investigates a novel fractional-order Lyapunov-based robust controller based on a fuzzy neural network (FNN) compensator for exoskeleton robotic systems. First, a finite-time fractional-order nonsingular fast terminal sliding mode control (FONFTSMC) method is designed. Second, a FNN algorithm is constructed to approximate the model uncertainty and external disturbances. Then, finite-time stability of the closed-loop control system is proved using Lyapunov stability theorem and adaptive law is derived through it. The proposed fuzzy neural network-based FONFTSMC (FNN-FONFTSMC) guarantees finite-time convergence and robustness against uncertainties for the exoskeleton robots trajectory tracking. Finally, to illustrate the effectiveness of the proposed control strategy, an upper-limb exoskeleton robot is provided as a case study in rehabilitation. The simulation results confirm the superiority of the proposed control method.

Keywords: Exoskeleton robots; Fuzzy neural network; Lyapunov stability theorem; Fractional calculus; Terminal sliding mode control; Finite-time stability (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:193:y:2022:i:c:p:567-583

DOI: 10.1016/j.matcom.2021.10.022

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