Adaptive Neural Network-Based Fixed-Time Tracking Controller for Disabilities Exoskeleton Wheelchair Robotic System

Ayman A. Aly, Mai The Vu, Fayez F. M. El-Sousy, Kuo-Hsien Hsia, Ahmed Alotaibi, Ghassan Mousa, Dac-Nhuong Le and Saleh Mobayen ()
Additional contact information
Ayman A. Aly: Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Mai The Vu: School of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
Fayez F. M. El-Sousy: Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj 16278, Saudi Arabia
Kuo-Hsien Hsia: Department of Electrical Engineering, National Yunlin University of Science and Technology, 123 University Road, Douliou 64002, Taiwan
Ahmed Alotaibi: Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Ghassan Mousa: King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia
Dac-Nhuong Le: King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia
Saleh Mobayen: Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, 123 University Road Section 3, Douliou 640301, Taiwan

Mathematics, 2022, vol. 10, issue 20, 1-17

Abstract: In this paper, an adaptive neural network approach is developed based on the integral nonsingular terminal sliding mode control method, with the aim of fixed-time position tracking control of a wheelchair upper-limb exoskeleton robot system under external disturbance. The dynamical equation of the upper-limb exoskeleton robot system is obtained using a free and typical model of the robotic manipulator. Afterward, the position tracking error between the actual and desired values of the upper-limb exoskeleton robot system is defined. Then, the integral nonsingular terminal sliding surface based on tracking error is proposed for fixed-time convergence of the tracking error. Furthermore, the adaptive neural network procedure is proposed to compensate for the external disturbance which exists in the upper-limb exoskeleton robotic system. Finally, to demonstrate the effectiveness of the proposed method, simulation results using MATLAB/Simulink are provided.

Keywords: adaptive control; neural network; fixed-time convergence; disabilities robot; wheelchair exoskeleton robot; sliding mode control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/20/3853/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/20/3853/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:20:p:3853-:d:945224

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().