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Model identification for a flapping-wing robot based on improved squirrel search algorithm.

Xiaoyang Wu1, Wei He2, Tingting Meng1

  • 1Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, School of Intelligent Science and Technology, University of Science and Technology Beijing, Beijing 100083, China.

ISA Transactions
|October 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new system identification method for flapping-wing robots (FWRs), enhancing dynamic models. An Improved Squirrel Search Algorithm accurately identifies parameters, improving flight characteristic prediction for FWR control.

Keywords:
Flapping-wing robot (FWR)Squirrel search algorithmSystem identificationValidation

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Aerospace Engineering

Background:

  • Flapping-wing robots (FWRs) present complex dynamic modeling challenges.
  • Accurate dynamic models are crucial for FWR control and performance optimization.
  • Existing methods may not fully capture the intricacies of flapping-wing motion.

Purpose of the Study:

  • To develop a novel system identification method for creating an authentic dynamic model of a flapping-wing robot (FWR).
  • To address and resolve model uncertainties stemming from flapping-wing kinematics.
  • To validate the identified dynamic model's effectiveness in representing FWR flight characteristics.

Main Methods:

  • Constructing an independent frame for each flapping wing to enhance model structural authenticity.
  • Employing an Improved Squirrel Search Algorithm for parameter identification of the dynamic model.
  • Validating the identified model through experimental testing across three distinct maneuvers.

Main Results:

  • The proposed method successfully identifies unknown parameters in the FWR dynamic model.
  • Experimental comparisons confirm the efficiency of the Improved Squirrel Search Algorithm in parameter identification.
  • The validated dynamic model accurately reflects the flight characteristics of the underactuated FWR during various maneuvers.

Conclusions:

  • The novel system identification method provides an authentic dynamic model for FWRs.
  • The Improved Squirrel Search Algorithm is effective for parameter identification in complex robotic systems.
  • This research lays a foundation for advanced control strategies in flapping-wing robot applications.