Simulation and evaluation of lateral/directional dynamics in an aircraft autopilot control system
Abstract
The objective of the research was to design and simulate the lateral/directional dynamics control of an aircraft’s autopilot system to automate the landing approach execution, complying with the requirements of the Instrument Landing System (CAT III C). The design methodology involved integrating a Linear Quadratic Regulator (LQR) with Affine Parameterization techniques to create a robust control system. The prototype was developed using Matlab and simulated in Simulink. Through various simulations, adjustments were made to the Q and R matrices of the LQR controller based on Bryson’s rule, allowing the system to adapt to the nonlinearities and dynamic constraints of the aircraft model. These adjustments included modifying the lateral attitude control parameters to achieve the desired damping factors and time constants, ensuring flight quality standards according to MIL-8785C. Validation under real conditions through a flight simulator confirmed the control system’s effectiveness under various operational conditions. The controllers are able to maintain the aircraft’s alignment with the runway centerline, even in the presence of external disturbances, thus demonstrating the system’s robustness and reliability. The methodologies and results provide a solid foundation for future improvements and comparative analyses of autopilot systems within CAT III C requirements.
Keyword : lateral/directional dynamics, autopilot system, instrument landing system (ILS), linear quadratic regulator (LQR), affine parameterization, flight simulator validation
How to Cite
Sánchez, C., Cajas, M., Calvopiña, P., & Ortega, A. (2024). Simulation and evaluation of lateral/directional dynamics in an aircraft autopilot control system. Aviation, 28(4), 206–214. https://doi.org/10.3846/aviation.2024.22577
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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American Institute of Aeronautics and Astronautics. (2000). Flight control systems. AIAA. https://doi.org/10.2514/4.866555
Azeez, M. I., Abdelhaleem, A. M. M., Elnaggar, S., Moustafa, K. A. F., & Atia, K. R. (2023). Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithm. Scientific Reports, 13, Article 11164. https://doi.org/10.1038/s41598-023-37895-3
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Cavanini, L., Ippoliti, G., & Camacho, E. F. (2021). Model predictive control for a linear parameter varying model of an UAV. Journal of Intelligent and Robotic Systems: Theory and Applications, 101(3), 1–18. https://doi.org/10.1007/s10846-021-01337-x
Coello, L. A., Jácome, F. A., Zurita, J. R., Casa, C. W., & Vélez, J. S. (2023). Design and simulation of an aircraft autopilot control system: Longitudinal dynamics. In Lecture notes in networks and systems. Trends in Artificial intelligence and computer engineering (LNNS, Vol. 619, pp. 388–402). Springer. https://doi.org/10.1007/978-3-031-25942-5_31
Collinson, R. P. G. (2023). Autopilots and flight management systems. In Introduction to avionics systems (pp. 259–284). Springer. https://doi.org/10.1007/978-3-031-29215-6_8
Cook, M. V. (2013). Flight dynamics principles: A linear systems approach to aircraft stability and control. In Flight dynamics principles: A linear systems approach to aircraft stability and control (pp. 1–575). Elsevier. https://doi.org/10.1016/C2010-0-65889-5
Cortez, B., Peter, F., Lausenhammer, T., Oliveira, P., Author, F., & Lisboa, T. (2020). Reinforcement learning for robust missile autopilot design (arxiv). Cornell University. https://arxiv.org/abs/2011.12956v2
Dudek, E., & Kozłowski, M. (2018). The concept of the instrument landing system – ILS continuity risk analysis method. Communications in Computer and Information Science, 897, 305–319. https://doi.org/10.1007/978-3-319-97955-7_21
Elbatal, A., Youssef, A. M., & Elkhatib, M. M. (2021). Smart aerosonde UAV longitudinal flight control system based on genetic algorithm. Bulletin of Electrical Engineering and Informatics, 10(5), 2433–2441. https://doi.org/10.11591/eei.v10i5.2342
Elsisi, M., Altius, M., Su, S. F., & Su, C. L. (2023). Robust Kalman filter for position estimation of automated guided vehicles under cyberattacks. IEEE Transactions on Instrumentation and Measurement, 72. https://doi.org/10.1109/TIM.2023.3250285
Elsisi, M., Zaini, H. G., Mahmoud, K., Bergies, S., & Ghoneim, S. S. M. (2021). Improvement of trajectory tracking by robot manipulator based on a new co-operative optimization algorithm. Mathematics, 9(24), Article 3231. https://doi.org/10.3390/math9243231
Fu, J., Huang, J., Song, L., & Yang, D. (2020). Experimental study of aircraft achieving dutch roll mode stability without weathercock stability. International Journal of Aerospace Engineering, 2020. https://doi.org/10.1155/2020/8971275
Guardeño, R., López, M. J., & Sánchez, V. M. (2019). MIMO PID controller tuning method for quadrotor based on LQR/LQG theory. Robotics, 8(2), Article 36. https://doi.org/10.3390/robotics8020036
Kashyap, V., & Vepa, R. (2023). Reinforcement learning based linear quadratic regulator for the control of a quadcopter. In AIAA 2023-0014 Session: International Student Conference – Masters Category. AIAA. https://doi.org/10.2514/6.2023-0014
Liu, X., & Qin, X. (2020). A probability-based core dandelion guided dandelion algorithm and application to traffic flow prediction. Engineering Applications of Artificial Intelligence, 96, Article 103922. https://doi.org/10.1016/j.engappai.2020.103922
Mo, H., & Farid, G. (2019). Nonlinear and adaptive intelligent control techniques for quadrotor UAV – A survey. Asian Journal of Control, 21(2), 989–1008. https://doi.org/10.1002/asjc.1758
Okyere, E., Bousbaine, A., Poyi, G. T., Joseph, A. K., & Andrade, J. M. (2019). LQR controller design for quad-rotor helicopters. The Journal of Engineering, 2019(17), 4003–4007. https://doi.org/10.1049/joe.2018.8126
Qiao, W., & Wu, S. (2023). The modeling and simulation of turbulence for civil aircraft compliance verification test. Journal of Physics: Conference Series, 2658(1), Article 012056. https://doi.org/10.1088/1742-6596/2658/1/012056
Reis, W. P. N. dos, Couto, G. E., & Junior, O. M. (2023). Automated guided vehicles position control: A systematic literature review. Journal of Intelligent Manufacturing, 34(4), 1483–1545. https://doi.org/10.1007/s10845-021-01893-x
Rodrigues, L. (2021). Affine quadratic optimal control and aerospace applications. IEEE Transactions on Aerospace and Electronic Systems, 57(2), 795–805. https://doi.org/10.1109/TAES.2020.3029625
Sariyildiz, E., & Ohnishi, K. (2015). An adaptive reaction force observer design. IEEE/ASME Transactions on Mechatronics, 20(2), 750–760. https://doi.org/10.1109/TMECH.2014.2321014
Simões, A. M., & Cavalcanti, V. M. G. B. (2023). Missile autopilot design via structured robust linear parameter-varying synthesis. Journal of Guidanace, Control, and Dynamics, 46(8), 1649–1656. https://doi.org/10.2514/1.G007580
Sir, A., & Naci, S. (2022). Robust LQR and LQR-PI control strategies based on adaptive weighting matrix selection for a UAV position and attitude tracking control. Alexandria Engineering Journal, 61(8), 6275–6292. https://doi.org/10.1016/j.aej.2021.11.057
Sun, C. T., & Adnan, A. (2021). Mechanics of aircraft structures. John Wiley & Sons.
Zhao, S., Zhang, T., Ma, S., & Chen, M. (2022). Dandelion optimizer: A nature-inspired metaheuristic algorithm for engineering applications. Engineering Applications of Artificial Intelligence, 114, Article 105075. https://doi.org/10.1016/j.engappai.2022.105075
Zuo, Z., Liu, C., Han, Q. L., & Song, J. (2022). Unmanned aerial vehicles: Control methods and future challenges. IEEE/CAA Journal of Automatica Sinica, 9(4), 601–614. https://doi.org/10.1109/JAS.2022.105410