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

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.

Keywords

• Cascade PID
• UAV
• ABC Algorithm
• Optimisation

References

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). IEEE.
6. 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). IEEE.
8. 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.
10. 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.
11. 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). IEEE.
12. 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.
13. Zhen, Y., Hao, M., & Sun, W. (2020, November). Deep reinforcement learning attitude control of fixed-wing UAVs. In 2020 3rd International Conference on Unmanned Systems (ICUS) (pp. 239-244). IEEE.
14. Poksawat, P., Wang, L., & Mohamed, A. (2017). Gain scheduled attitude control of fixed-wing UAV with automatic controller tuning. IEEE Transactions on Control Systems Technology, 26(4), 1192-1203.
15. Mammarella, M., Capello, E., Dabbene, F., & Guglieri, G. (2018). Sample-based SMPC for tracking control of fixed-wing UAV. IEEE control systems letters, 2(4), 611-616.
16. Abachizadeh, M., Yazdi, M. R. H., & Yousefi-Koma, A. (2010, July). Optimal tuning of PID controllers using artificial bee colony algorithm. In 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (pp. 379-384). IEEE.
17. Coban, S., Bilgic, H.H., Akan, E. (2020). Improving autonomous performance of a passive morphing fixed wing UAV. Information Technology and Control. 49 (1), pp. 28-35.
18. McCafferty, J. P., Woodward, D. A., Ray, G., Bachelani, A., & Kim, B. (2014). Investigation of an autonomous landing sensor for unmanned aerial systems. In AIAA Guidance, Navigation, and Control Conference (p. 0979).
19. Bray, R. M. (1991). A wind tunnel study of the Pioneer remotely piloted vehicle. NAVAL POSTGRADUATE SCHOOL, MONTEREY CA.
20. Etkin, B., & Reid, L. D. (1995). Dynamics of flight: stability and control. John Wiley & Sons.
21. Johnson, M. A., & Moradi, M. H. (2005). PID control. London, UK: Springer-Verlag London Limited.
22. Kada, B., & Ghazzawi, Y. (2011, October). Robust PID controller design for an UAV flight control system. In Proceedings of the World congress on Engineering and Computer Science (Vol. 2, No. 1-6, pp. 1-6).
23. Andrade, F. A., Guedes, I. P., Carvalho, G. F., Zachi, A. R., Haddad, D. B., Almeida, L. F., ... & Pinto, M. F. (2021). Unmanned Aerial Vehicles Motion Control with Fuzzy Tuning of Cascaded-PID Gains. Machines, 10(1), 12.
24. Bilgiç, H. H., Tutumlu, M. S., & Conker, Ç. (2021). Top ve çubuk sistemi için kaskad denetleyici parametrelerinin meta-sezgisel algoritmalarla optimizasyonu. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 23(67), 157-167.
25. Bilgic, H. H., Sen, M. A., Yapici, A., Yavuz, H., & Kalyoncu, M. (2021). Meta-heuristic tuning of the LQR weighting matrices using various objective functions on an experimental flexible arm under the effects of disturbance. Arabian Journal for Science and Engineering, 46(8), 7323-7336.
26. Guvenc, M. A., Bilgic, H. H., Cakir, M., & Mistikoglu, S. (2022). The prediction of surface roughness and tool vibration by using metaheuristic-based ANFIS during dry turning of Al alloy (AA6013). Journal of the Brazilian Society of Mechanical Sciences and Engineering, 44(10), 1-14.
27. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.
28. Akay, B., & Karaboga, D. (2012). Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of intelligent manufacturing, 23(4), 1001-1014.
29. Hekimoğlu, B. (2019). Optimal tuning of fractional order PID controller for DC motor speed control via chaotic atom search optimization algorithm. IEEE Access, 7, 38100-38114.
30. Shagor, M. R. K., Nishat, M. M., Faisal, F., Mithun, M. H., & Khan, M. A. (2021, December). Firefly algorithm based optimized PID controller for stability analysis of DC-DC SEPIC converter. In 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 0957-0963). IEEE.
31. Maghfiroh, H., Saputro, J. S., Hermanu, C., Ibrahim, M. H., & Sujono, A. (2021, March). Performance Evaluation of Different Objective Function in PID Tuned by PSO in DC-Motor Speed Control. In IOP Conference Series: Materials Science and Engineering (Vol. 1096, No. 1, p. 012061). IOP Publishing.