Synthesis and Testing of an Algorithm for Autonomous Landing of a UAV under Turbulence, Wind Disturbance and Sensor Noise †
Abstract
:1. Introduction
2. Materials and Methods
- The critical angle of attack (αcrit.): At this angle, the aircraft stays in the air with the minimum possible speed that is useful for landing;
- An economical angle of attack (the most advantageous) (αecon.) where the drag coefficient is minimal but different from zero. At this angle of attack, the inductive resistance is half of the resistance of the aircraft, and the UAV can fly with maximum speed, so the diagram of the aerodynamic quality of the UAV is used;
- The most favorable α. At this angle, the quality of the UAV is at a maximum, and then only at this angle of attack for a certain height can it fly the farthest (no wind) because the lifting force is K times greater than the drag, and for 1 m height the plane will fly K [m] (if the air is still).
3. Synthesizing an LQR Regulator with Integral Action
- Bryson’s Rule: The square of the maximum acceptable error is taken into account. The method is suitable as a starting point for tuning LQR, and it is possible to carry out an iterative process to fine-tune Q and R;
- Genetic Algorithm (GA): Used for optimal setting of Linear Quadratic Regulator (LQR) parameters through the principles of genetics and natural selection;
- Particle Swarm Optimization (PSO): Finds an optimal value of the parameters that satisfy the system while minimizing the loss function.
4. Setting the Disturbance and Noise Effects on the System
5. Synthesizing a State Estimator in the Longitudinal Channel of the UAV
6. Flight Planning on Landing
7. Simulation of the Developed Algorithms in the Landing Stage
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Eigennumber λ | Eigenvector 1 | Eigenvector 2 | Eigenvector 3 | Eigenvector 4 |
---|---|---|---|---|
−3.5817 + 9.1151i | 1 + 0i | 48.13 + 3.87i | 2.328 − 16.54i | 1.485 + 0.8389i |
−3.5817 + 9.1151i | 1 + 0i | 48.13 − 3.87i | 2.328 +16.54i | 1.485 − 0.8389i |
−0.0061 + 0.4856i | 1 + 0i | −0.0106 + 0.003213i | 0.02409 + 0.0008569i | 0.002392 + 0.04958i |
−0.0061 + 0.4856i | 1 + 0i | −0.0106 − 0.003213i | 0.02409 − 0.0008569i | −0.002392 − 0.04958i |
Flight Plan 1 April 2024 (for example) | |||||
---|---|---|---|---|---|
Waypoints | H (m) | S (m) | u (m/s) | B (°) | L (°) |
SRP | 240 | 0 | 15 | 43.4500 | 24.5130 |
IRP-1 | 200 | 100 | 10 | 43.4502 | 24.5111 |
IRP-2 | 140 | 160 | 10 | 43.4511 | 24.5066 |
IRP-3 | 100 | 240 | 10 | 43.4515 | 24.5044 |
IRP-4 | 60 | 373.3 | 10 | 43.4518 | 24.5027 |
IRP-5 | 20 | 553.3 | 15 | 43.4519 | 24.5018 |
IRP-6 | 0 | 928 | 2 | 43.4520 | 24.5011 |
ERP | 0 | 1018 | 0 | 43.4522 | 24.4999 |
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Biliderov, S.; Kamenov, K.; Calovska, R.; Georgiev, G. Synthesis and Testing of an Algorithm for Autonomous Landing of a UAV under Turbulence, Wind Disturbance and Sensor Noise. Eng. Proc. 2024, 70, 41. https://doi.org/10.3390/engproc2024070041
Biliderov S, Kamenov K, Calovska R, Georgiev G. Synthesis and Testing of an Algorithm for Autonomous Landing of a UAV under Turbulence, Wind Disturbance and Sensor Noise. Engineering Proceedings. 2024; 70(1):41. https://doi.org/10.3390/engproc2024070041
Chicago/Turabian StyleBiliderov, Stefan, Krasimir Kamenov, Radostina Calovska, and Georgi Georgiev. 2024. "Synthesis and Testing of an Algorithm for Autonomous Landing of a UAV under Turbulence, Wind Disturbance and Sensor Noise" Engineering Proceedings 70, no. 1: 41. https://doi.org/10.3390/engproc2024070041