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

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.

@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},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Ayoub Elbazzazi and Ikram Dahamou and Cherki Daoui},
  doi       = {10.14569/IJACSA.2026.01703107},
  url       = {https://doi.org/10.14569/IJACSA.2026.01703107}
}