Design of fractional order PID controller based on Genetic Algorithm Optimization for Vertical Take-off and Landing Platforms

Year 2023, Volume: 2 Issue: 2, 263 - 269, 27.12.2023

Ali Mahmood , Mohammed Almaged Abdulla Abdulla

Abstract

This research studies the procedure of analyzing and designing of a fractional order Proportional Integral Derivative (FOPID) with the genetic algorithm (GA) as optimization method in order to control the pitch angle of vertical take-off and landing system (VTOL). The VTOL system of unmanned aerial vehicles (UAVs) has been extensively manipulated in many areas because they have stable flight and simple necessities for the space needed for the take-off and landing. The VTOL system has been modeled firstly by taking the action of the torque of the rigid body then finding the equations of the angular motions. Finally, the transfer function of the current to the position dynamics of the VTOL (pitch angle variable) has been found. The fractional order PID controller is considered as a modified type of the PID controller because it has fractional orders for the integral and derivative sections instead of being integers. This results in five variables in the controller that must be optimally chosen. So, the GA optimization method will be used in order to find the optimal values for the parameters of the controller, while three fitness functions will be used, including mean square error (MSE), integral time square error (ITSE) and integral square error (ISE). The performances of the controllers with the three-fitness functions have been compared relating to the maximum overshoot, rise time, settling time, and steady-state error. The results show that the ISE gives better behavior in terms of the transient and the steady state response specifications.

Keywords

PID Controllers, FOPID Controllers, Genetic Algorithms, Unmanned aerial vehicles, VTOL platforms.

References

• AIbrahim, M., Kh Mahmood, A., Saleh Sultan, N., & Sultan, N. S. (2019). Optimal PID controller of a brushless DC motor using genetic algorithm. International Journal of Power Electronics and Drive System (IJPEDS), 10(2), 822–830. https://doi.org/10.11591/ijpeds.v10.i2.pp822-830
• Bauersfeld, L., & Ducard, G. (2020). Fused-PID control for tilt-rotor VTOL aircraft. 2020 28th Mediterranean Conference on Control and Automation, MED 2020, 703–708. https://doi.org/10.1109/MED48518.2020.9183031
• Becker, M., & Sheffler, D. (2016). Designing a high speed, stealthy, and payload-focused VTOL UAV. 2016 IEEE Systems and Information Engineering Design Symposium, SIEDS 2016, 176–180. https://doi.org/10.1109/SIEDS.2016.7489294
• Cao, J. Y., Liang, J., & Cao, B. G. (2005). Optimization of Fractional Order PID controllers based on genetic algorithms. 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005, 5686–5689. https://doi.org/10.1109/ICMLC.2005.1527950
• Gani, M. M., Islam, M. S., & Ullah, M. A. (2019). Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm. SN Applied Sciences, 1(8), 1–8. https://doi.org/10.1007/S42452-019-0929-Y/FIGURES/12 Ibrahim Khather, S., Almaged, M., & Abdullah, A. I. (2018). Fractional order based on genetic algorithm PID controller for controlling the speed of DC motors. International Journal of Engineering & Technology, 7(4), 5386–5392. https://doi.org/10.14419/ijet.v7i4.25601
• Lin, K., Qi, J., Wu, C., Wang, M., & Zhu, G. (2020). Control System Design of A Vertical Take-off and Landing Unmanned Aerial Vehicle. Chinese Control Conference, CCC, 2020-July, 6750–6755. https://doi.org/10.23919/CCC50068.2020.9188609
• Mahmood, A., Almaged, M., & Abdulla, A. I. (2021). Antenna Azimuth Position Control Using Fractional Order PID Controller Based on Genetic Algorithm. IOP Conference Series: Materials Science and Engineering, 1152(1), 012016. https://doi.org/10.1088/1757-899X/1152/1/012016
• S Dawood, Y., K Mahmood, A., & A Ibrahim, M. (2018). Comparison of PID, GA and Fuzzy Logic Controllers for Cruise Control System. International Journal of Computing and Digital Systems, 7(05), 311–319. http://dx.doi.org/10.12785/ijcds/070505
• Shah, P., & Agashe, S. (2016). Review of fractional PID controller. Mechatronics, 38, 29–41. https://doi.org/10.1016/J.MECHATRONICS.2016.06.005
• Taşören, A. E., Gökçen, A., Soydemir, M. U., & Şahin, S. (2020). Artificial Neural Network-Based Adaptive PID Controller Design for Vertical Takeoff and Landing Model. European Journal of Science and Technology Special Issue, 87–93. https://doi.org/10.31590/ejosat.779085
• Zhang, B., Liu, W., Mao, Z., Liu, J., & Shen, L. (2014). Cooperative and Geometric Learning Algorithm (CGLA) for path planning of UAVs with limited information. Automatica, 50(3), 809–820. https://doi.org/10.1016/J.AUTOMATICA.2013.12.035