Visual Journal of Technical and Vocational Education

Visual Journal of Technical and Vocational Education

Adaptive Precision in UAV Flight Dynamics: A Hybrid Recursive Least Squares and Digital LQR Framework for Real-Time Deflection Control

Document Type : Original Article

Authors
Department of Electrical Engineering, Hamedan University of Technology, Hamedan, Iran.
Abstract
This paper presents the design and analysis of an advanced autopilot system for controlling the lateral and longitudinal dynamics of a custom-built unmanned aerial vehicle (UAV). The system regulates roll, pitch, and yaw angles through a unified control strategy, leveraging recursive least squares (RLS) estimation to model the UAV’s dynamic behavior with high precision. A key innovation lies in applying RLS to extract both lateral and longitudinal transfer functions, offering a level of adaptability rarely seen in comparable studies. To optimize multi-axis control, a digital linear quadratic regulator (DLQR) is developed, enabling simultaneous tuning of all three rotational degrees of freedom. This integrated approach marks a departure from conventional proportional, proportional-integral- derivative or fuzzy logic controllers, which often treat these axes independently. The simulation framework incorporates pseudo-random binary sequence (PRBS) inputs and white Gaussian noise (WGN), ensuring robustness under realistic operating conditions. The UAV platform is grounded in validated aerodynamic data, with stability derivatives obtained from DATCOM and JSBSIM, effectively bridging theoretical modeling with real-world flight dynamics. Simulation results reveal substantial improvements in rise time, settling time, and overshoot across all axes, demonstrating the DLQR controller’s effectiveness in achieving fast, stable, and responsive flight behavior.
Keywords
Subjects

[1] Akyazı, Ö., Usta, M., & Akpinar, A. S. (2013). A Self-Tuning Fuzzy Logic Controller for Aircraft Roll Control System. International Journal of Control Science and Engineering, 2. https://doi.org/10.5923/j.control.20120206.06
[2] Hou, Q., & Dong, J. (2023). Robust adaptive event-triggered fault-tolerant consensus control of multiagent systems with a positive minimum interevent time. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(7), 4003–4014. https://doi.org/10.1109/TSMC.2023.3238709
[3] Wang, H., & Li, M. (2024). Adaptive fuzzy nonsingular fixed-time safety flight containment control for multi-UAVs. IEEE Transactions on Aerospace and Electronic Systems, 61(1), 802–812. https://doi.org/10.1109/TAES.2024.3448398
[4] Ji, R., & Ge, S. S. (2023). Event-triggered tunnel prescribed control for nonlinear systems. IEEE Transactions on Fuzzy Systems, 32(1), 90–101. https://doi.org/10.1109/TFUZZ.2023.3290934
[5] Wu, Y., Chen, M., Li, H., & Chadli, M. (2023). Event-triggered-based adaptive NN cooperative control of six-rotor UAVs with finite-time prescribed performance. IEEE Transactions on Automation Science and Engineering, 21(2), 1867–1877. https://doi.org/10.1109/TASE.2023.3241182
[6] Wahid, N., Hassan, N., Rahmat, M. F., & Mansor, S. (2011). Application of intelligent controller in feedback control loop for aircraft pitch control. Australian Journal of Basic and Applied Sciences, 5(12), 1065–1074. https://www.ajbasweb.com/old/ajbas/2011/December-2011/1065-1074.pdf
[7] Bossert, D., & Cohen, K. (2003). Fuzzy logic non-minimum phase autopilot design 2003 AIAA Guidance, Navigation, and Control Conference and Exhibit, 5550,  https://doi.org/10.2514/6.2003-5550
[8] Li, T.-H. S., & Shieh, M.-Y. (2000). Design of a GA-based fuzzy PID controller for non-minimum phase systems. Fuzzy Sets and Systems, 111(2), 183–197. https://doi.org/10.1016/S0165-0114(97)00404-1
[9] Omar, H. (2007). Genetic-based fuzzy logic controller for satellites stabilized by reaction wheels and gravity gradient AIAA Guidance, Navigation and Control Conference and Exhibit, 6445,  https://doi.org/10.2514/6.2007-6445
[10] Babaei, A. R., Mortazavi, M., & Moradi, M. H. (2013). Fuzzy sliding mode autopilot design for nonminimum phase and nonlinear UAV. Journal of Intelligent & Fuzzy Systems, 24(3), 499–509. https://doi.org/10.3233/IFS-2012-0515
[11] Babaei, A. R., Mortazavi, M., & Moradi, M. H. (2011). Classical and fuzzy-genetic autopilot design for unmanned aerial vehicles. Applied Soft Computing, 11(1), 365–372. https://doi.org/10.1016/j.asoc.2009.11.027
[12] Nair, V. G. (2012). Aircraft yaw control system using LQR and fuzzy logic controller. International Journal of Computer Applications, 45(9), 25–30. https://doi.org/10.5120/6808-9150
[13] Usta, M. A., Akyazi, Ö., & Akpinar, A. S. (2011). Aircraft roll control system using LQR and fuzzy logic controller 2011 International symposium on innovations in intelligent systems and applications,  https://doi.org/10.1109/INISTA.2011.5946069
[14] Nair, V. G., George, V., & Prahlad, K. (2012). Lateral control of aircraft, considering the cross-coupling effect of yaw and roll on each-other using fuzzy logic. Sensors & Transducers, 142(7), 105. https://researcher.manipal.edu/en/publications/lateral-control-of-aircraft-considering-the-cross-coupling-effect/
[15] Akoushideh, A., Sadat, S. S., & Shahbahrami, A. (2024). Detecting, identifying, and counting vehicles based on deep learning algorithms in video surveillance systems. Visual Journal of Technical and Vocational Education, 1(2), 79–90. https://doi.org/10.48301/jear.2024.459050.1027
[16] Taghandiki, K., Dallakehnejad, M., & Rahimi Asiabaraki, H. (2024). Reducing Air Pressure System Repair Costs in Scania Trucks through Deep Learning. Visual Journal of Technical and Vocational Education, 1(1), 183–196. https://doi.org/10.48301/jear.2024.447671.1022
[17] Taghandiki, K. (2024). Quantum machine learning unveiled: a comprehensive review. Visual Journal of Technical and Vocational Education, 1(2), 29–48. https://doi.org/10.48301/jear.2024.446673.1021
[18] Khan, A. I., & Al-Mulla, Y. (2019). Unmanned aerial vehicle in the machine learning environment. Procedia computer science, 160, 46–53. https://doi.org/10.1016/j.procs.2019.09.442
[19] Bobál, V., Böhm, J., Fessl, J., & Macháček, J. (2005). Digital self-tuning controllers: algorithms, implementation and applications. Springer. https://doi.org/10.1007/1-84628-041-9_4
[20] Nelles, O. (2020). Nonlinear dynamic system identification. In Nonlinear system identification: from classical approaches to neural networks, fuzzy models, and gaussian processes (pp. 831–891). Springer. https://doi.org/10.1007/978-3-030-47439-3_19
[21] Van Schuppen, J. H. (2021). Control and system theory of discrete-time stochastic systems. Springer. https://doi.org/10.1007/978-3-030-66952-2
[22] Sundararajan, D. (2022). State-Space Analysis of Control Systems, Control Systems, 233-271. Springer. https://doi.org/10.1007/978-3-030-98445-8

  • Receive Date 10 June 2025
  • Revise Date 26 August 2025
  • Accept Date 15 October 2025