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UAV Model-based Flight Control with Artificial Neural Networks: A Survey

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Abstract

Model-Based Control (MBC) techniques have dominated flight controller designs for Unmanned Aerial Vehicles (UAVs). Despite their success, MBC-based designs rely heavily on the accuracy of the mathematical model of the real plant and they suffer from the explosion of complexity problem. These two challenges may be mitigated by Artificial Neural Networks (ANNs) that have been widely studied due to their unique features and advantages in system identification and controller design. Viewed from this perspective, this survey provides a comprehensive literature review on combined MBC-ANN techniques that are suitable for UAV flight control, i.e., low-level control. The objective is to pave the way and establish a foundation for efficient controller designs with performance guarantees. A reference template is used throughout the survey as a common basis for comparative studies to fairly determine capabilities and limitations of existing research. The end-result offers supported information for advantages, disadvantages and applicability of a family of relevant controllers to UAV prototypes.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, DU Unmanned Systems Research Institute (DU2SRI), Daniel Felix Ritchie School of Engineering and Computer Science, University of Denver, 2155 E Wesley Ave, Denver, CO, 80210, USA

    Weibin Gu, Kimon P. Valavanis & Matthew J. Rutherford

  2. Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Torino, TO, Italy

    Alessandro Rizzo

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  1. Weibin Gu
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  2. Kimon P. Valavanis
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  4. Alessandro Rizzo
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Correspondence to Weibin Gu.

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This work is partially supported by an NSF Grant, CMMI-DCSD-1728454, Compagnia di San Paolo, and by an Amazon Research Award granted to Dr. A. Rizzo.

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Gu, W., Valavanis, K.P., Rutherford, M.J. et al. UAV Model-based Flight Control with Artificial Neural Networks: A Survey. J Intell Robot Syst 100, 1469–1491 (2020). https://doi.org/10.1007/s10846-020-01227-8

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