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Real-time adaptive super twisting algorithm based on PSO algorithm: application for an exoskeleton robot

Published online by Cambridge University Press:  24 April 2024

Hichame Tiaiba Open the ORCID record for Hichame Tiaiba [Opens in a new window] ,
Mohamed El Hossine Daachi  and
Tarek Madani
Hichame Tiaiba*
Affiliation:
Laboratoire d’Electronique et des Telecommunications Avancees, Universite Mohamed El Bachir El Ibrahimi de Bordj Bou-Arreridj, El-Anasser, Algerie
Mohamed El Hossine Daachi
Affiliation:
Laboratoire d’Electronique et des Telecommunications Avancees, Universite Mohamed El Bachir El Ibrahimi de Bordj Bou-Arreridj, El-Anasser, Algerie
Tarek Madani
Affiliation:
Laboratoire Images, Signaux et Systemes Intelligents, Universite Paris-Est Creteil, Vitry sur Seine, France
*
Corresponding author: Hichame Tiaiba; Email: hichame.tiaiba@univ-bba.dz
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Abstract

In this paper, an online adaptive super twisting sliding mode controller is proposed for a non-linear system. The adaptive controller has been designed in order to deal with the unknown dynamic uncertainties and give the best trajectory tracking. The adaptation is based on an optimal Particle Swarm Optimization (PSO) algorithm whose goal is online tuning the parameters through focusing on decreasing the objective function. The novelty of this study is online handling parameters setting in the conventional super twisting algorithm, bypass heavy offline calculation, and also avoid the instability and abrupt changing of the controller’s parameters for better actuators lifetime. This novel approach has been applied on an upper limb exoskeleton robot for arm rehabilitation. Despite the changes of the dynamic model of the system which defers from one patient to another due to the direct interactions between the wearer and the exoskeleton, this control technique preserves its robustness with respect to bounded external disturbances. The effectiveness of the proposed adaptive controller has been proved in simulation and then in real-time experiment with two human subjects. A comparison between the proposed approach and classic super twisting algorithm has been conducted. The obtained results show the performance and efficiency of the proposed controller.

Keywords

real timesuper twisting sliding mode controladaptive controllerexoskeleton robotparticle swarm optimizationonline tuningrobotic rehabilitation
Type
Research Article
Information
Robotica , First View , pp. 1 - 26
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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