Robust dynamic control algorithm for uncertain powered wheelchairs based on sliding neural network approach

Mohsen Bakouri; Abdullah Alqarni; Sultan Alanazi; Ahmad Alassaf; Ibrahim AlMohimeed; Mohamed Abdelkader Aboamer; Tareq Alqahtani; Mohsen Bakouri; Abdullah Alqarni; Sultan Alanazi; Ahmad Alassaf; Ibrahim AlMohimeed; Mohamed Abdelkader Aboamer; Tareq Alqahtani

doi:10.3934/math.20231373

AIMS Mathematics

2023, Volume 8, Issue 11: 26821-26839. doi: 10.3934/math.20231373

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Research article Special Issues

Robust dynamic control algorithm for uncertain powered wheelchairs based on sliding neural network approach

Department of Medical Equipment Technology, College of Applied Medical Science, Majmaah University, Majmaah 11952, Saudi Arabia

Received: 23 July 2023 Revised: 31 August 2023 Accepted: 11 September 2023 Published: 20 September 2023
MSC : 68T07, 92B20

The dynamic model of mobile wheelchair technology requires developing and implementing an intelligent control system to improve protection, increasing performance efficiency, and creating precise maneuvering in indoor and outdoor spaces. This work aims to design a robust tracking control algorithm based on a reference model for operating the kinematic model of powered wheelchairs under the variation of system parameters and unknown disturbance signals. The control algorithm was implemented using the pole placement method in combination with the sliding mode control (PP-SMC) approach. The design also adopted a neural network approach to eliminate system uncertainties from perturbations. The designed method utilized the sinewave signal as an essential input signal to the reference model. The stability of a closed-loop control system was achieved by adopting the Goa reaching law. The performance of the proposed tracking control system was evaluated in three scenarios under different conditions. These included assessing the tracking under normal operation conditions, considering the tracking performance by changing the dynamic system's parameters and evaluating the control system in the presence of uncertainties and external disturbances. The findings demonstrated that the proposed control method efficiently tracked the reference signal within a small error based on mean absolute error (MAE) measurements, where the range of MAE was between 0.08 and 0.12 in the presence of uncertainties or perturbations.
- dynamic system,
- uncertain system,
- powered wheelchair, sliding mode control (SMC),
- pole placement method,
- neural networks approach
Citation: Mohsen Bakouri, Abdullah Alqarni, Sultan Alanazi, Ahmad Alassaf, Ibrahim AlMohimeed, Mohamed Abdelkader Aboamer, Tareq Alqahtani. Robust dynamic control algorithm for uncertain powered wheelchairs based on sliding neural network approach[J]. AIMS Mathematics, 2023, 8(11): 26821-26839. doi: 10.3934/math.20231373

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Abstract

The dynamic model of mobile wheelchair technology requires developing and implementing an intelligent control system to improve protection, increasing performance efficiency, and creating precise maneuvering in indoor and outdoor spaces. This work aims to design a robust tracking control algorithm based on a reference model for operating the kinematic model of powered wheelchairs under the variation of system parameters and unknown disturbance signals. The control algorithm was implemented using the pole placement method in combination with the sliding mode control (PP-SMC) approach. The design also adopted a neural network approach to eliminate system uncertainties from perturbations. The designed method utilized the sinewave signal as an essential input signal to the reference model. The stability of a closed-loop control system was achieved by adopting the Goa reaching law. The performance of the proposed tracking control system was evaluated in three scenarios under different conditions. These included assessing the tracking under normal operation conditions, considering the tracking performance by changing the dynamic system's parameters and evaluating the control system in the presence of uncertainties and external disturbances. The findings demonstrated that the proposed control method efficiently tracked the reference signal within a small error based on mean absolute error (MAE) measurements, where the range of MAE was between 0.08 and 0.12 in the presence of uncertainties or perturbations.

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