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Boosting a Reference Model-Based Controller Using Active Disturbance Rejection Principle for 3D Trajectory Tracking of Quadrotors: Experimental Validation

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Abstract

It is relevant to develop an adequate control algorithm for quadrotors that guarantees a good compromise robustness/ performance. This compromise should be ensured with or without external disturbances. In this paper, we investigate and apply a revisited formulation of a reference model-based control strategy by introducing a boosting mechanism. This mechanism uses an Extended State-based Observer (ESO) to estimate the uncertainties and variety of disturbances. The estimation is continually updated and rejected from the main control loop. The reinforcement principle is inspired from the popular Active Disturbance Rejection Control (ADRC) technique in order to enhance the robustness ability of a nonlinear reference model-based control strategy (i.e. Interconnection and Damping Assignment-Passivity Based Control (IDA-PBC)). The obtained controller is augmented by an additional input, which is derived via sliding modes framework to handle the estimation errors and ensure asymptotic stability. This combination leads to promising results by improving the nominal control technique. The primary results are shown through numerical simulations and are confirmed, experimentally, with several scenarios.

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Authors and Affiliations

  1. CSCS Laboratory, Ecole Militaire Polytechnique, Bordj-el-Bahri, Algiers, Algeria

    Yasser Bouzid & Mohamed Guiatni

  2. EPESA Laboratory, USTO-MB, Oran and the University of Maskara, Mascara, Algeria

    Mehdi Zareb

  3. L2S, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, 91190, France

    Houria Siguerdidjane

Authors
  1. Yasser Bouzid
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  2. Mehdi Zareb
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  3. Houria Siguerdidjane
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  4. Mohamed Guiatni
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Correspondence to Houria Siguerdidjane.

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Bouzid, Y., Zareb, M., Siguerdidjane, H. et al. Boosting a Reference Model-Based Controller Using Active Disturbance Rejection Principle for 3D Trajectory Tracking of Quadrotors: Experimental Validation. J Intell Robot Syst 100, 597–614 (2020). https://doi.org/10.1007/s10846-020-01182-4

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