Non -fragile Pl control of Takagi-Sugeno fuzzy artificial pancreas systems with actuator vulnerabilities

This paper proposes a robust non-fragile proportional-integral (NFPI) control strategy based on an adaptive event-triggered scheme (AETS) for networked artificial pancreas systems (APSs) subject to physiological nonlinearity and actuator vulnerabilities. Firstly, a global dynamic modeling framework is established to accurately characterize complex glucose-insulin interactions. It utilizes the sector nonlinearity technique to construct a nonlinear Takagi-Sugeno (T-S) fuzzy model, explicitly accommodating patient variability as norm-bounded parameter uncertainties. Secondly, an augmented NFPI control strategy is synthesized to ensure precise insulin infusion and system stability despite actuator vulnerabilities. This design explicitly incorporates gain fluctuations and faults into the synthesis process to immunize the system against actuator fragility, thereby guaranteeing robust therapeutic efficacy. In addition, a state-dependent AETS is introduced to reduce energy consumption while maintaining rigorous glycemic safety. By dynamically adjusting thresholds according to system deviation, this mechanism significantly curtails redundant transmissions without compromising the capability to suppress acute postprandial surges. Subsequently, less conservative sufficient conditions based on linear matrix inequalities (LMIs) are derived to guarantee asymptotic stability and H∞ performance. Leveraging a Lyapunov-Krasovskii functional (LKF) accounting for asynchronous membership functions, the controller gains and triggering parameters are jointly synthesized via a unified co-design. Finally, simulations validate the framework’s effectiveness in maintaining robust normoglycemia and optimizing communication resources.