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Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter.

Xuegang Song1, Yuexin Zhang2, Dakai Liang3

  • 1State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China. sxg37068219890209@126.com.

Materials (Basel, Switzerland)
|October 11, 2017
PubMed
Summary

A new inverse algorithm estimates time-varying forces in nonlinear beam systems using dynamic responses for structural health monitoring. This method accurately determines force magnitude and location in real-time.

Keywords:
input forces estimationnonlinear algorithmnonlinear estimatorsquare-root cubature Kalman filter

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Area of Science:

  • Mechanical Engineering
  • Structural Dynamics
  • Signal Processing

Background:

  • Accurate estimation of time-varying input forces is crucial for understanding nonlinear beam system behavior.
  • Existing methods may struggle with real-time estimation and noise suppression in complex dynamic systems.

Purpose of the Study:

  • To develop and validate a novel inverse algorithm for real-time estimation of time-varying input forces in nonlinear beam systems.
  • To assess the algorithm's effectiveness for structural health monitoring applications.

Main Methods:

  • Discretization of state equations using the Runge-Kutta fourth-order algorithm.
  • Application of a square-root cubature Kalman filter (SRCKF) for white noise suppression.
  • Utilizing SRCKF outputs with a nonlinear least squares estimator to determine force magnitude and location.

Main Results:

  • The developed nonlinear algorithm demonstrated accurate estimation of input forces in both numerical simulations and experimental tests.
  • Successful real-time force estimation was achieved from dynamic responses of nonlinear beam systems.

Conclusions:

  • The proposed inverse algorithm offers a robust and accurate solution for identifying time-varying forces in nonlinear beam systems.
  • This technique holds significant potential for advancing structural health monitoring capabilities.