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Stabilisation, tracking and disturbance rejection control design for the UAS-S45 Bálaam

Published online by Cambridge University Press:  10 March 2022

M.A.J. Kuitche ,
H. Yañez-Badillo ,
R.M. Botez Open the ORCID record for R.M. Botez [Opens in a new window]  and
S.M. Hashemi
M.A.J. Kuitche
Affiliation:
ETS, Laboratory of Active Controls, Avionics and AeroServoElasticity LARCASE, 1100 Notre Dame West, Montreal, QC, Canada, H3C-1K3
H. Yañez-Badillo
Affiliation:
ETS, Laboratory of Active Controls, Avionics and AeroServoElasticity LARCASE, 1100 Notre Dame West, Montreal, QC, Canada, H3C-1K3
R.M. Botez*
Affiliation:
ETS, Laboratory of Active Controls, Avionics and AeroServoElasticity LARCASE, 1100 Notre Dame West, Montreal, QC, Canada, H3C-1K3
S.M. Hashemi
Affiliation:
ETS, Laboratory of Active Controls, Avionics and AeroServoElasticity LARCASE, 1100 Notre Dame West, Montreal, QC, Canada, H3C-1K3
*
*Corresponding author. Email: [email protected]
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Abstract

The stabilisation and control mechanisms of an Unmanned Aerial System (UAS) must be properly designed to ensure acceptable flight performance. During their operation, these mechanisms are subjected to unknown and random environmental effects, making it imperative that all available information should be taken into consideration during the mechanisms’ design process (e.g. system dynamics, actuators, flight conditions and certain criteria requirements such as phugoid and short modes for longitudinal dynamics, and roll subsidence, spiral and Dutch-roll modes for lateral dynamics) in order to guarantee flight stability. Therefore, this paper introduces a novel methodology for the stabilisation and control of the UAS-S45 Bálaam, designed and manufactured by Hydra Technologies. This methodology uses composite controllers that combine feedback Linear Quadratic Regulators (LQR) and Proportional Integral Feed-Forward (PI-FF) compensation controller for stabilisation and tracking tasks, respectively. Furthermore, a Generalised Extended State Observer was implemented to provide robustness to the closed loop dynamics by introducing disturbance compensation. Furthermore, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was adopted to perform a gain scheduling by computing the gains of each composite controller for certain unknown trim conditions within a given flight domain. Finally, several numerical assessments were performed to highlight the efficiency of the proposed methodology.

Keywords

Robust controlsGeneralised Extended State ObserverOptimal controlUAS-S45Disturbance rejectionLQRPI-FFANFIS
Type
Research Article
Information
The Aeronautical Journal , Volume 126 , Issue 1303 , September 2022 , pp. 1474 - 1496
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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