Online Continual Physics-Informed Learning for Quadrotor State Estimation Under Wind-Induced Disturbances

Liu, Yanhui; Wang, Shuopeng; Shi, Junhua; Hao, Lina

doi:10.3390/aerospace12080704

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Open AccessArticle

Online Continual Physics-Informed Learning for Quadrotor State Estimation Under Wind-Induced Disturbances

by

Yanhui Liu

,

Shuopeng Wang

,

Junhua Shi

and

Lina Hao

^*

School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China

^*

Author to whom correspondence should be addressed.

Aerospace 2025, 12(8), 704; https://doi.org/10.3390/aerospace12080704

Submission received: 26 June 2025 / Revised: 30 July 2025 / Accepted: 6 August 2025 / Published: 8 August 2025

(This article belongs to the Special Issue UAV System Modelling Design and Simulation)

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Abstract

Accurate state estimation for quadrotors under wind-induced disturbances remains a critical challenge in dynamic outdoor environments. Existing model-based and data-driven approaches often struggle with real-time adaptation and catastrophic forgetting when faced with continuous wind disturbances. This paper proposes an online continual physics-informed learning framework that integrates physics-informed neural networks with continual backpropagation to address these limitations. The physics-informed neural networks architecture embeds quadrotor dynamics into the neural network training process, ensuring physical consistency, while continual backpropagation enables continual learning from real-time streaming data without compromising previously acquired knowledge. Experimental validation on a simulation platform demonstrates the accuracy and robustness of the framework in ideal and wind-disturbed scenarios.

Keywords: quadrotor state estimation; wind-induced disturbances; continual learning; physics-informed neural networks; continual backpropagation

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MDPI and ACS Style

Liu, Y.; Wang, S.; Shi, J.; Hao, L. Online Continual Physics-Informed Learning for Quadrotor State Estimation Under Wind-Induced Disturbances. Aerospace 2025, 12, 704. https://doi.org/10.3390/aerospace12080704

AMA Style

Liu Y, Wang S, Shi J, Hao L. Online Continual Physics-Informed Learning for Quadrotor State Estimation Under Wind-Induced Disturbances. Aerospace. 2025; 12(8):704. https://doi.org/10.3390/aerospace12080704

Chicago/Turabian Style

Liu, Yanhui, Shuopeng Wang, Junhua Shi, and Lina Hao. 2025. "Online Continual Physics-Informed Learning for Quadrotor State Estimation Under Wind-Induced Disturbances" Aerospace 12, no. 8: 704. https://doi.org/10.3390/aerospace12080704

APA Style

Liu, Y., Wang, S., Shi, J., & Hao, L. (2025). Online Continual Physics-Informed Learning for Quadrotor State Estimation Under Wind-Induced Disturbances. Aerospace, 12(8), 704. https://doi.org/10.3390/aerospace12080704

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Online Continual Physics-Informed Learning for Quadrotor State Estimation Under Wind-Induced Disturbances

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