Volume – 1 Issue – 2 Article – 5

A Comparative Study on the Tuning of the PID Flight Controllers Using Swarm Intelligence

Aziz Kaba
1. azizkaba@eskisehir.edu.tr Faculty of Aeronautics and Astronautics
F IJAST 2020; 1 (2) : 80-91; 10.23890/IJAST.vm01is02.0205; Language: EN

QUAVs have some shortcomings in terms of nonlinearities, coupled dynamics, unstable open–loop characteristics, and they are prone to internal and external disturbances. Therefore, control problem of the QUAVs is still an open issue. Designed controllers based on the linear dynamics have limited operating ranges. Therefore, nonlinear dynamics of the QUAVs must be derived and used in the control problem. Although some advanced controllers are presented for QUAV control, PID controllers are the most employed, well–known controllers with the simple structure, ease of implementation, solid functionality and robustness amongst the variations up to a degree. In this paper, PID based controllers are proposed for the nonlinear attitude dynamics to overcome the control problem of the QUAVs. However, since optimality and tuning of the PID controllers are fuzzy because of trial and error approaches, swarm intelligence based meta-heuristic algorithms (ABC, ACO and PSO) are employed to optimize the PID coefficients. Results are compared in terms of transient analysis and MC analysis to cover the rise time, settling time, percentage overshoot, steady–state error for the former and stochastic fitness evaluation for the latter, respectively.

UAV.Quadrotor.PID Control

  1. K. H. Ang, G. Chong and Y. Li, “PID Control System Analysis, Design, and Technology,” IEEE Transactions on Control Systems Technology, vol. 13, no. 4, pp. 559-576, 2005.
  2. G.-Q. Zeng, X.-Q. Xie, M.-R. Chen and J. Weng, “Adaptive population extremal optimization-based PID neural network for multivariable nonlinear control systems,” Swarm and Evolutionary Computation, vol. 44, pp. 320 – 334, 2019.
  3. E. Çelik and N.Öztürk, “A hybrid symbiotic organisms search and simulated annealing technique applied to efficient design of PID controller for automatic voltage regulator,” Soft Computing, vol. 22, pp. 8011 – 8024, 2018.
  4. S. Panda, B. Sahu and P. Mohanty, “Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization,” Journal of Franklin Institute, vol. 349, pp. 2609 – 2625, 2012.
  5. T. Hägglund, “Signal filtering in PID control,” IFAC Proceedings, vol. 45, no. 3, pp. 1 – 10, 2012.
  6. A. Tepljakov, E. Gonzalez, E. Petlenkov, J. Belikov, C. Monje and I. Petráš, “Incorporation of fractional-order dynamics into an existing PI/PID DC motor control loop,” ISA Transactions, vol. 60, pp. 262 – 273, 2016.
  7. A. Masoumian, P. Kazemi, M. C. Montazer, H. A. Rashwan and D. P. Valls, “Designing and Analyzing the PID and Fuzzy Control System for an Inverted Pendulum,” in 6th International Conference on Mechatronics and Robotics Engineering (ICMRE), Barcelona, Spain, 2020.
  8. X. Wu, X. Wang and G. He, “A Fuzzy Self-tuning Temperature PID Control Algorithms for 3D Bio-printing Temperature Control System,” in Chinese Control And Decision Conference (CCDC), Hefei, China, 2020.
  9. Z. Ge, F. Liu and L. Meng, “Adaptive PID Control for Second Order Nonlinear Systems,” in Chinese Control And Decision Conference (CCDC), Hefei, China, 2020.
  10. S. Zhang, Z. Dan, Q. Qian, Y. Guo and J. Zhang, “Nonlinear PID Pressure Control Based on Extremum Seeking,” in 39th Chinese Control Conference (CCC), Shenyang, China, 2020.
  11. S. Lihan, M. Jie and Y. Baoqing, “Fuzzy PID Design of Vehicle Attitude Control Systems,” in Chinese Control And Decision Conference (CCDC), Hefei, China, 2020.
  12. M. Mahmud, S. M. A. Motakabber, A. H. M. Z. Alam and A. N. Nordin, “Adaptive PID Controller Using for Speed Control of the BLDC Motor,” in IEEE International Conference on Semiconductor Electronics (ICSE), Kuala Lumpur, Malaysia, 2020.
  13. S. Mu, S. Shibata, T. Yamamoto, K. Tanaka, S. Nakashima and T. Liu, “Experimental Study on Speed Control of Ultrasonic Motor using Intelligent IMC-PID Control,” in International Conference on Technologies and Applications of Artificial Intelligence (TAAI), Kaohsiung, Taiwan, 2019.
  14. S. Wei, G. Chen, H. Zhang, H. Lu and S. Wu, “Design of Z-Axis Position Servo Control System for Laser Processing Based on Fuzzy PID Control,” in 12th International Conference on Intelligent Computation Technology and Automation (ICICTA), Xiangtan, China, 2019.
  15. A. A. A. Razak, A. N. K. Nasir, M. F. M. Jusof, S. Mohammad and N. A. M. Rizal, “Opposition based Spiral Dynamic Algorithm with an Application to a PID Control of a Flexible Manipulator,” in 9th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia, 2019.
  16. H. Zhang and W. Assawinchaichote, “PID Control Based On Double Fuzzy RBF Neural Network For 7-DOF Manipulator,” in 8th International Electrical Engineering Congress (iEECON), Chiang Mai, Thailand, 2020.
  17. E. A. Yfantis and W. Culbreth, “A Data Driven PID Control System,” in 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2020.
  18. L. Bauersfeld and G. Ducard, “Fused-PID Control for Tilt-Rotor VTOL Aircraft,” in 28th Mediterranean Conference on Control and Automation (MED), Saint-Raphaël, France, 2020.
  19. H. Matsuki, T. Nishiyama, Y. Omori and S. Suzuki, “Flight test of fault-tolerant flight control system using simple adaptive control with PID controller,” Aircraft Engineering and Aerospace Technology, vol. 90, no. 1, pp. 210 – 218, 2018.
  20. I. Yazar, E. Kıyak, F. Çalışkan and T. H. Karakoç, “Simulation-based dynamic model and speed controller design of a small-scale turbojet engine,” Aircraft Engineering and Aerospace Technology, vol. 90, no. 2, pp. 351-358, 2018.
  21. A. Ermeydan and E. Kıyak, “Fault tolerant control against actuator faults based on enhanced PID controller for a quadrotor,” Aircraft Engineering and Aerospace Technology, vol. 89, no. 3, pp. 468-476, 2017.
  22. A. Kaba, A. Ermeydan and E. Kıyak, “Model derivation, attitude control and Kalman filter estimation of a quadcopter,” in 4th. International Conference on Electrical and Electronic Engineering, IEEE, Ankara, 2017.
  23. Z. Long, Z. Jiang, C. Wang, Y. Jin, Z. Cao and Y. Li, “A Novel Approach to Control of Piezo-Transducer in Microelectronics Packaging: PSO-PID and Editing Trajectory Optimization,” IEEE Transactions On Components, Packaging And Manufacturing Technology, vol. 10, no. 5, pp. 795 – 805, 2020.
  24. E. Kıyak, “Tuning of controller for an aircraft flight control system based on particle swarm optimization,” Aircraft Engineering and Aerospace Engineering, vol. 88, no. 6, pp. 799-809, 2016.
  25. J. Connor, M. Seyedmahmoudian and B. Horan, “Using particle swarm optimization for PID optimization for altitude control on a quadrotor,” in Australasian Universities Power Engineering Conference, 2017.
  26. Y. Chen, S. Liu, C. Xiong, Y. Zhu and Jiaheng Wang, “Research on UAV Flight Tracking Control Based on Genetic Algorithm Optimization and Improved bp Neural Network pid Control,” in Chinese Automation Congress (CAC), Hangzhou, China, 2019.
  27. J. O. Pedro, M. Dangor and P. J. Kala, “Differential Evolution-Based PID Control of a Quadrotor System for Hovering Application,” in IEEE Congress on Evolutionary Computation, 2016.
  28. E. Bonabeau, M. Dorigo and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford,England: Oxford University Press, 1999.
  29. D. Karaboga, “An Idea Based on Honey Bee Swarm for Numerical Optimization,” Technical Report -TR06, Erciyes University, Kayseri, 2005.
  30. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 – International Conference on Neural Networks, Perth, WA, Australia, 1995.
  31. D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, pp. 459-471, 2007.
  32. M. Dorigo, V. Maniezzo and A. Colorni, “Ant System: Optimization by a colony of cooperating agents,” IEEE Trans Syst Man Cybernetics – Part B, vol. 26, pp. 29 – 41, 1996.
  33. V. Tereshko, “Reaction-diffusion model of a honeybee colony’s foraging behaviour,” in Parallel Problem Solving from Nature VI, Berlin, Springer-Verlag, 2000, pp. 807 – 816.
  34. J. Christensen and C. Bastien, “Heuristic and Meta-Heuristic Optimization Algorithms,” in Nonlinear Optimization of Vehicle Safety Structures, Butterworth-Heinemann, 2016, pp. 277 – 314.
  35. M. Dorigo, M. Birattari and T. Stützle, “Ant Colony Optimization,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28 – 39, 2006.
  36. M. K.Jha, “Metaheuristic Applications in Highway and Rail Infrastructure Planning and Design: Implications to Energy and Environmental Sustainability,” in Metaheuristics in Water, Geotechnical and Transport Engineering, Elsevier, 2013, pp. 365 – 384.
  37. S. Talatahari, V. P. Singh and Y. Hassanzadeh, “Ant Colony Optimization for Estimating Parameters of Flood Frequency Distributions,” in Metaheuristics in Water, Geotechnical and Transport Engineering, Elsevier, 2013, pp. 121 – 146.
  38. C. M. Martínez and D. Cao, “Integrated energy management for electrified vehicles,” in Ihorizon-Enabled Energy Management for Electrified Vehicles, Butterworth-Heinemann, 2019, pp. 15 – 75.
  39. A. Kaba and E. Kıyak, “Artificial bee colony–based Kalman filter hybridization for three–dimensional position estimation of a quadrotor,” Aircraft Engineering and Aerospace Technology, vol. 92, no. 10, pp. 1523-1532, 2020.
  40. X. Li and G. Yang, “Artificial bee colony algorithm with memory,” Applied Soft Computing, vol. 41, pp. 362 – 372, 2016.
  41. Å. KJ and H. T, “The future of PID control,” Control Engineering Practice, vol. 9, no. 11, pp. 1163 – 1175, 2001.
  42. R. P. Borase, D. K. Maghade, S. Y. Sondkar and S. N. Pawar, “A review of PID control, tuning methods and applications,” International Journal of Dynamics and Control, 2020.
  43. I. D. Díaz-Rodríguez, H. Sangjin and S. P. Bhattacharyya, Analytical design of PID controllers, Berlin: Springer, 2019.
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