Adaptive Neural Backstepping Control Approach for Tracker Design of Wheelchair Upper-Limb Exoskeleton Robot System

Ayman A. Aly, Kuo-Hsien Hsia, Fayez F. M. El-Sousy, Saleh Mobayen (), Ahmed Alotaibi, Ghassan Mousa and Dac-Nhuong Le
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Ayman A. Aly: Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Kuo-Hsien Hsia: Department of Electrical Engineering, National Yunlin University of Science and Technology, 123 University Road, Douliou, Yunlin 64002, Taiwan
Fayez F. M. El-Sousy: Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj 16278, Saudi Arabia
Saleh Mobayen: Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 640301, 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 94682, Saudi Arabia
Dac-Nhuong Le: King Salman Center for Disability Research, Riyadh 94682, Saudi Arabia

Mathematics, 2022, vol. 10, issue 22, 1-16

Abstract: In this study, the desired tracking control of the upper-limb exoskeleton robot system under model uncertainty and external disturbance is investigated. For this reason, an adaptive neural network using a backstepping control strategy is designed. The difference between the actual values of the upper-limb exoskeleton robot system and the desired values is considered as the tracking error. Afterward, the auxiliary variable based on the tracking error is defined and the virtual control input is obtained. Then, by using the backstepping control procedure and Lyapunov stability concept, the convergence of the position tracking error is proved. Moreover, for the compensation of the model uncertainty and the external disturbance that exist in the upper-limb exoskeleton robot system, an adaptive neural-network procedure is adopted. Furthermore, for the estimation of the unknown coefficient related to the parameters of the neural network, the adaptive law is designed. Finally, the simulation results are prepared for demonstration of the effectiveness of the suggested method on the upper-limb exoskeleton robot system.

Keywords: neural network; adaptive law; backstepping control; external disturbance; wheelchair robot; tracker design (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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