A Hybrid Modeling and Control Framework for Intelligent Wheelchairs Using Timed Petri Nets and Machine Learning

Abstract: This study proposes a hybrid modeling and control framework for intelligent wheelchair systems that integrates formal methods with adaptive artificial intelligence to ensure safety, robustness, and real-time performance. The approach combines Timed and Colored Petri Nets for formal safety enforcement with machine learning techniques, including a Multi-Layer Perceptron, Q-learning, and fuzzy logic. The system is validated through simulation and FPGA-based implementation, demonstrating improved command accuracy, safety compliance, and response time compared to baseline approaches. The main contribution lies in the integration of formal verification with adaptive intelligence within a real-time embedded system for assistive mobility.

Ayoub Elbazzazi, Ikram Dahamou and Cherki Daoui. “A Hybrid Modeling and Control Framework for Intelligent Wheelchairs Using Timed Petri Nets and Machine Learning”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.3 (2026). http://dx.doi.org/10.14569/IJACSA.2026.01703107

@article{Elbazzazi2026,
title = {A Hybrid Modeling and Control Framework for Intelligent Wheelchairs Using Timed Petri Nets and Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.01703107},
url = {http://dx.doi.org/10.14569/IJACSA.2026.01703107},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {3},
author = {Ayoub Elbazzazi and Ikram Dahamou and Cherki Daoui}
}