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July 6, 2026Transactions of the Institute of Measurement and Control

Neural adaptive sliding mode control for quadrotor position tracking under unknown mass and external disturbances

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Authors

ASAzmat SaeedAMAhmad MahmoodHDHazry Desa

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Overview

Randomized trial demonstrates improved tracking performance in quadrotors, suggesting enhanced control under disturbances.

Key Points

  • This research aims to improve trajectory tracking control of quadrotors facing unknown mass and external disturbances.
  • Implemented adaptive sliding mode control integrated with a radial basis function neural network.
  • Utilized real-time adaptive laws based on RBFNN to estimate and counter model uncertainties.
  • Established stability using Lyapunov theory.
  • Significant reductions in root mean square error (RMSE) observed with RBFNN-ASMC compared to SMC.
  • Improved on-track percentage (OTP) demonstrated under varying scenarios.
  • Enhanced performance validated through numerical simulations against traditional methods.

Cite This Study

Saeed et al. (2026) studied this question.

synapsesocial.com/papers/6a4b45b2997070ff83b5b573https://doi.org/10.1177/01423312261460944
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