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Autonomous predictive maintenance of quadrotor UAV with multi-actuator degradation

Published online by Cambridge University Press:  08 February 2024

F.-y. Shen Open the ORCID record for F.-y. Shen [Opens in a new window] ,
W. Li ,
D.-n. Jiang  and
H.-j. Mao
F.-y. Shen
Affiliation:
College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China
W. Li*
Affiliation:
College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China
D.-n. Jiang
Affiliation:
College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China
H.-j. Mao
Affiliation:
College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China
*
Corresponding author: W. Li; Email: liwei@lut.edu.cn
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Abstract

With the wide application of quadrotor unmanned aerial vehicles (UAVs), the requirements for their safety and reliability are becoming increasingly stringent. In this paper, based on the feedback of airframe performance health perception information and the predictive function control strategy, the autonomous maintenance of a quadrotor UAV with multi-actuator degradation is realised. Autonomous maintenance architecture is constructed by the predictive maintenance (PdM) idea and the Laguerre function model predictive pontrol (LF-MPC) strategy. Using the two-stage Kalman filter (TSKF) method, based on the established UAV degradation model, the aircraft state and actuator degradation state are predicted simultaneously. For the predictive perception of system health, on the one hand, the system health degree (HD) based on Mahalanobis distance is defined by the degree of airframe state deviation from the expected state, and then the failure threshold of the UAV is obtained. On the other hand, according to the degradation state of each actuator, a comprehensive degradation variable fused with different weight coefficients of multiple actuators degradation is used to obtain the probability density function (PDF) of remaining useful life (RUL) prediction. For the autonomous maintenance of system health, the LF-MPC weight matrixes are adjusted adaptively in real-time based on the HD evaluation, to achieve a compromise balance between UAV performance and control effect, and greatly extend the working time of UAV. Simulation results verified the effectiveness of the proposed method.

Keywords

multi-actuator degradationremaining useful lifeLaguerre function-based model predictive controlpredictive maintenance
Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 25
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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