Journals / European Mechanical Science / 2023 / Cilt: 7 Sayı: 4

Metaheuristic algorithm-based cascade PID controller design for fixed wing unmanned aerial vehicle

Pages
230–237
Publication Date
2023-12-20
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Controller performances were tested in simulation environment for unit step input responses., Considering the longitudinal dynamics of the aircraft, among the ITAE, ISE, MSE, and IAE fitness functions, IAE can be shown as the optimum result for altitude control.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, the nonlinear model of the longitudinal motion and altitude of a fixed-wing unmanned aerial vehicle with assured geometrical features and aerodynamic parameters is linearized under certain conditions.
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Key Findings & Data:A cascade Proportional Integral Differential Controller is designed on the linear model.
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Conclusion & Impact:Controller performances were tested in simulation environment for unit step input responses., Considering the longitudinal dynamics of the aircraft, among the ITAE, ISE, MSE, and IAE fitness functions, IAE can be shown as the optimum result for altitude control.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Cascade PID · UAV · ABC Algorithm

Abstract

In this study, the nonlinear model of the longitudinal motion and altitude of a fixed-wing unmanned aerial vehicle with assured geometrical features and aerodynamic parameters is linearized under certain conditions. A cascade Proportional Integral Differential Controller is designed on the linear model. The controller coefficients that applied to the model of the UAV were optimized with an artificial intelligence technique, which is based on a metaheuristic search algorithm. The four different controller gains in the system are optimized using four different objective functions. Controller performances were tested in simulation environment for unit step input responses., Considering the longitudinal dynamics of the aircraft, among the ITAE, ISE, MSE, and IAE fitness functions, IAE can be shown as the optimum result for altitude control.

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Cite This Article

DURMAZ, M., CİCİ, K., SARIKAYA, M., BİLİCİ, M., BİLGİÇ, H. H. (2023). Metaheuristic algorithm-based cascade PID controller design for fixed wing unmanned aerial vehicle. European Mechanical Science, Cilt: 7 Sayı: 4, 230–237. https://ajindex.com/articles/1022515

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References (11)

  1. Han, T., Xiao, B., Zhan, X. S., Wu, J., & Gao, H. (2019). Time-optimal control of multiple unmanned aerial vehicles with human control input. International Journal of Intelligent Computing and Cybernetics, 12(1), 138-152.
  2. Alladi, T., Chamola, V., & Kumar, N. (2020). PARTH: A two-stage lightweight mutual authentication protocol for UAV surveillance networks. Computer Communications, 160, 81-90.
  3. Xia, C., Yongtai, L., Liyuan, Y., & Lijie, Q. (2020). Cooperative task assignment and track planning for multi-UAV attack mobile targets. Journal of Intelligent & Robotic Systems, 100(3), 1383-1400.
  4. Bilici, M., Karılı, M., (2022) Modeling and Control of a Fixed-Wing High-Speed UAV. International Journal of Aviation Science and Technology, 3(01), 35-44.
  5. Altan, A. (2020, October). Performance of metaheuristic optimization algorithms based on swarm intelligence in attitude and altitude control of unmanned aerial vehicle for path following. In 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-6).
  6. IEEE. Ilango, H. S., & Ramanathan, R. (2020). A Performance Study of Bio-Inspired Algorithms in Autonomous Landing of Unmanned Aerial Vehicle. Procedia Computer Science, 171, 1449-1458.
  7. Mobarez, E. N., Sarhan, A., & Ashry, M. M. (2019, December). Robust PID Flight Controller for Ultrastick-25e UAV. In 2019 15th International Computer Engineering Conference (ICENCO) (pp. 150-156).
  8. IEEE. Kaba, A. (2021). Improved PID rate control of a quadrotor with a convexity-based surrogated model. Aircraft Engineering and Aerospace Technology. 93, 1287–1301.
  9. Amarat, S. B., & Zong, P. (2019). 3D path planning, routing algorithms and routing protocols for unmanned air vehicles: a review. Aircraft engineering and aerospace technology. Hervas, J. R., Reyhanoglu, M., Tang, H., & Kayacan, E. (2016). Nonlinear control of fixed-wing UAVs in presence of stochastic winds. Communications in Nonlinear Science and Numerical Simulation, 33, 57-69.
  10. Stastny, T., & Siegwart, R. (2018, June). Nonlinear model predictive guidance for fixed-wing UAVs using identified control augmented dynamics. In 2018 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 432-442).
  11. IEEE. Yan, J., Yu, Y., & Wang, X. (2022). Distance-based formation control for fixed-wing UAVs with input constraints: A low gain method. Drones, 6(7), 159. Zhen, Y., Hao, M., & Sun, W. (2020, November). Deep reinforcement learning attitude control of fixed-wing UAVs. In 2020 3rd Inter