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

Xianglin Zhang1, Mingqiang Luo1, Simeng Guo2

  • 1School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China.

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|March 27, 2024
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Summary

This study introduces an advanced distributed H∞ state estimation method for flapping-wing micro air vehicles (FMAVs). The novel approach enhances accuracy and topological completeness in networked FMAV systems.

Keywords:
Lyapunov–Krasovskii functionaldistributed state estimationflapping-wing micro air vehiclesinformation fusion correctionnon-uniform sampling

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Area of Science:

  • Robotics and Control Systems
  • Networked Systems Analysis
  • Aerospace Engineering

Background:

  • Flapping-wing micro air vehicles (FMAVs) present unique challenges for state estimation due to their complex dynamics and networked interactions.
  • Distributed state estimation is crucial for coordinating multiple FMAVs in complex missions.
  • Existing methods often struggle with ensuring both estimation accuracy and comprehensive topological awareness in such systems.

Purpose of the Study:

  • To develop a distributed H∞ state estimation framework for nodalized FMAV networks.
  • To enhance estimation accuracy and ensure the completeness of FMAV topological information within a unified model.
  • To minimize estimation conservatism in the designed FMAV state estimator.

Main Methods:

  • Introduction of an information fusion function to create an information-fusionized estimator model.
  • Modeling received signals with independent, time-varying samplers and converting them to equivalent bounded time-varying delays.
  • Construction of a Lyapunov-Krasovskii functional (LKF) combined with refined Wirtinger and relaxed integral inequalities.

Main Results:

  • A novel distributed H∞ state estimator for FMAVs was designed, ensuring estimation accuracy and topological information completeness.
  • The methodology effectively transforms complex nonlinear network systems into more manageable time-varying nonlinear error systems.
  • The derived design conditions for the estimator were shown to minimize conservatism.

Conclusions:

  • The proposed information-fusionized distributed H∞ state estimator is effective for FMAV networks.
  • The method provides a robust framework for accurate state estimation and topological awareness in FMAV systems.
  • Simulation results validate the superior performance of the designed estimator compared to existing approaches.