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Research paper

Stochastic Estimator Based Adaptive Sliding Mode Control for a Quadrotor in Rainy Flight Conditions

Guidance and Navigation Laboratory of IAST Research Unit, Ecole Militaire Polytechnique, 16111 Algiers, Algeria

This paper was recommended for publication in its revised form by editorial board member, Haibin Duan.

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Abstract

Novel modeling of the raindrops impact forces on a quadrotor has been developed and implemented in this work. These forces are meant to generate dispersed displacement disturbances in the vertical axis ZE and dispersed rotation disturbances around roll and pitch axis. The proposed model is derived using the stochastic characteristics of the raindrops combined with classical quadrotor dynamics to address the issue of the adopted sliding mode controller, which shows lack of robustness while tracking the desired path, resulting in sensitive fluctuations. These fluctuations are increasingly visible for disturbances generated by raindrops with an average diameter greater than 2 mm. To deal with these types of disturbances, a novel control law based on an adaptive sliding mode approach and the estimated parameters of the stochastic disturbances (average and variance) was employed. This control law has been synthesized based on the sliding mode approach and Lyapunov principle to ensure quadrotor stability and robustness purposes. The adopted estimation of the average and variance of the disturbances was carried out by filtering the difference between the system state and a reference model state. The adopted control law has been implemented on MATLAB/SIMULINK, yielding satisfactory results and minimizing the fluctuations.

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Unmanned Systems
Pages 133-148

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Cite this article:
Hamdadou N, Bouadi H, Hebablia N, et al. Stochastic Estimator Based Adaptive Sliding Mode Control for a Quadrotor in Rainy Flight Conditions. Unmanned Systems, 2024, 12(1): 133-148. https://doi.org/10.1142/S2301385024500092

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Received: 09 March 2022
Revised: 26 November 2022
Accepted: 26 November 2022
Published: 28 January 2023
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