Distributed State Estimation for Flapping-Wing Micro Air Vehicles with Information Fusion Correction

TL;DR

This paper proposes a new method for estimating the state of flapping-wing micro air vehicles using a networked system with improved accuracy.

Contribution

The novel contribution is an information-fusion-based estimator model that improves estimation accuracy and topological information completeness in FMAVs.

Findings

An information fusion function was introduced to enhance the FMAV state estimation model.

A Lyapunov–Krasovskii functional and refined inequalities were used to derive less conservative estimator design conditions.

Simulation results confirmed the effectiveness and superiority of the proposed estimator.

Abstract

In this paper, we explore a nonlinear interactive network system comprising nodalized flapping-wing micro air vehicles (FMAVs) to address the distributed H∞ state estimation problem associated with FMAVs. We enhance the model by introducing an information fusion function, leading to an information-fusionized estimator model. This model ensures both estimation accuracy and the completeness of FMAV topological information within a unified framework. To facilitate the analysis, each FMAV’s received signal is individually sampled using independent and time-varying samplers. Transforming the received signals into equivalent bounded time-varying delays through the input delay method yields a more manageable and analyzable time-varying nonlinear network error system. Subsequently, we construct a Lyapunov–Krasovskii functional (LKF) and integrate it with the refined Wirtinger and relaxed integral inequalities to derive design conditions for the FMAVs’ distributed H∞ state estimator, minimizing conservatism. Finally, we validate the effectiveness and superiority of the designed estimator through simulations.

Figures