Event-based H∞ Filtering for Networked Mass-Switching AMVs: An Augmented Lyapunov Functional Approach

In this article, we propose an event-triggered H_∞ filtering algorithm based on hysteresis quantized measurements for accurate state estimation in networked mass-switching autonomous marine vehicles (AMVs). The proposed method addresses several critical challenges in networked AMV filtering, including limited communication bandwidth, constrained energy resources, and signal transmission delays. A dynamic model is first established to capture the parameter variations caused by mass changes in AMVs. To alleviate bandwidth limitations and reduce signal chattering, a hysteresis-based quantization scheme is introduced. Furthermore, an energy-efficient event-triggered mechanism is designed to be both energy-efficient and capable of preventing excessively long periods without triggering, thereby reducing the frequency of data transmission. To facilitate rigorous stability analysis, a novel augmented Lyapunov-Krasovskii functional is constructed, and Wirtinger-based inequalities are employed to handle the time-delay-dependent integral terms. Based on this, a co-design strategy is also developed to jointly solve both the filter and the event-triggering mechanism. Simulation results demonstrate that the proposed method achieves accurate state estimation for networked mass-switching AMVs while reducing the transmission rate by 75%, significantly outperforming conventional logarithmic quantizers in suppressing chattering.