Dynamic Event-Triggered Model Reference Adaptive Control with Sensor Attack

How to achieve reference signal tracking under sensor bias attacks is a challenging topic in cyber-physical systems. This paper investigates the model reference adaptive control (MRAC) problem with a dynamic event-triggered strategy (ETS) in the presence of sensor bias attack. Unlike the existing results, we propose an enhanced MRAC control law to achieve both asymptotic stability and Zeno-free behavior. The proposed framework combines a deviation estimator with ETS and MRAC, where the bias estimator is integrated with the adaptive control gains, which are also triggered. Additionally, adaptive parameters incorporating a time-varying component are formulated, providing a theoretical guarantee for achieving the desired objectives. Simulation experiments are conducted to validate the effectiveness and feasibility of the proposed approach, demonstrating its practical applicability.