A sensor-fault resilient artificial pancreas using type-3 fuzzy logic and predictive controls

We propose a fault-tolerant artificial pancreas architecture for type 1 diabetes management that leverages advanced artificial intelligence (AI) methods. The system combines a step-forward predictive controller with a type-3 fuzzy logic system (FLS) in a dual-loop structure, augmented by a real-time sensor fault detection and compensation unit. The fault detection unit uses fuzzy prediction to estimate and correct sensor fault coefficients, thereby mitigating the impact of corrupted glucose measurements. Closed-loop stability is established through Lyapunov-based analysis, which informs the design of the adaptive compensator. Performance was evaluated using simulation studies on a modified Bergman model that incorporates patient variability and external disturbances. Results show that the proposed AI-based controller achieves greater robustness, adaptability, and fault tolerance compared with conventional control approaches. These findings demonstrate the promise of integrating predictive control with fuzzy logic for reliable intelligent healthcare systems, offering new opportunities for safe and effective AI-driven solutions in biomedical engineering.