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

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 shoul...

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Bibliographic Details
Published in:Aeronautical journal 2022-09, Vol.126 (1303), p.1474-1496
Main Authors: Kuitche, M.A.J., Yañez-Badillo, H., Botez, R.M., Hashemi, S.M.
Format: Article
Language:English
Subjects:
Actuators
Adaptive systems
Aircraft
Algorithms
Artificial neural networks
Closed loops
Compensation
Control theory
Controllers
Design
Dynamic stability
Environmental effects
Feedforward control
Flight characteristics
Flight conditions
Fuzzy logic
Gain scheduling
Lateral stability
Linear quadratic regulator
Methodology
Methods
Neural networks
Proportional integral
Robust control
Robustness (mathematics)
State observers
System dynamics
Tracking control
Unmanned aerial vehicles
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Summary: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.
ISSN:0001-9240
2059-6464
DOI:10.1017/aer.2022.22