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.