Document Type : Original Article

Authors

1 K.N.Toosi University of Technology Faculty of Aerospace Engineering, Tehran, Iran

2 Faculty of Aerospace Engineering of K. N. Toosi University of Technology

Abstract

This paper investigates different intelligent methods of tuning feedback-linearization control coefficients. Feedback-linearization technique is an effective method of controlling nonlinear systems. The most critical part of designing this controller is tuning the gains, especially if the plant has complex nonlinear dynamics. In this research, to improve the performance of the overall closed-loop system, the feedback linearization method has been integrated with the conventional proportional-integral-derivative (PID) controller. Also, a quadratic performance index was used to compare the functionality of the controllers tuned by the proposed intelligent methods. These intelligent methods include Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Fuzzy Logic, and Neural Network tuning algorithms. A quadrotor aircraft is used as the plant under study in order to evaluate the performance of the controllers tunned in this research. Finally, MATLAB simulation tests demonstrate the effectiveness of the presented algorithms. According to the results, it is demonstrated that the class of online algorithms performs better, even with the specified perturbation.

Keywords

Main Subjects

[1]  H. Shraim, A. Awada, and R. Youness, “A survey on quadrotors: Configurations, modeling and identification, control, collision avoidance, fault diagnosis and tolerant control,” IEEE Aerospace and Electronic Systems Magazine, vol. 33, no. 7, pp. 14–33, 2018, doi: 10.1109/MAES.2018.160246.
[2]  M. Hassanalian and A. Abdelkefi, “Classifications, applications, and design challenges of drones: A review,” Progress in Aerospace Sciences, vol. 91, May 2017, doi: 10.1016/j.paerosci.2017.04.003.
[3]  I. H. B. Pizetta, A. S. Brandao, and M. Sarcinelli-Filho, “Load Transportation by Quadrotors in Crowded Workspaces,” IEEE Access, vol. 8, pp. 223941–223951, 2020, doi: 10.1109/ACCESS.2020.3043719.
[4]  D. K. D. Villa, A. S. Brandão, and M. Sarcinelli-Filho, “A Survey on Load Transportation Using Multirotor UAVs,” Journal of Intelligent & Robotic Systems 2019 98:2, vol. 98, no. 2, pp. 267–296, Oct. 2019, doi: 10.1007/S10846-019-01088-W.
[5]  G. Ononiwu, O. Onojo, O. Ozioko, and O. Nosiri, “Quadcopter Design for Payload Delivery,” Journal of Computer and Communications, vol. 04, no. 10, pp. 1–12, 2016, doi: 10.4236/jcc.2016.410001.
[6]  P. N. Patel, M. Patel, R. Faldu, and Y. R. Dave, “Quadcopter for Agricultural Surveillance,” Advance in Electronic and Electric Engineering, pp. 427–432, 2013.
[7]  W. Budiharto, E. Irwansyah, J. S. Suroso, A. Chowanda, H. Ngarianto, and A. A. S. Gunawan, “Mapping and 3D modelling using quadrotor drone and GIS software,” J Big Data, vol. 8, no. 1, p. 48, Dec. 2021, doi: 10.1186/s40537-021-00436-8.
[8]  B. Chamberlain and W. Sheikh, “Design and Implementation of a Quadcopter Drone Control System for Photography Applications,” in 2022 Intermountain Engineering, Technology and Computing (IETC), May 2022, pp. 1–7. doi: 10.1109/IETC54973.2022.9796735.
[9]  S. Kefei, L. Baoying, L. Hanxu, and L. Chen, “Agricultural Environment Monitoring Combined with Quadrotor Aircraft Control Algorithm,” Engineering Science and Technology Review, pp. 190–200, 2019.
[10]         S. Gupte, P. I. T. Mohandas, and J. M. Conrad, “A survey of quadrotor unmanned aerial vehicles,” Conference Proceedings - IEEE SOUTHEASTCON, 2012, doi: 10.1109/SECon.2012.6196930.
[11]         R. Amin, L. Aijun, and S. Shamshirband, “A review of quadrotor UAV: control methodologies and performance evaluation,” International Journal of Automation and Control, vol. 10, no. 2, pp. 87–103, May 2016, doi: 10.1504/IJAAC.2016.076453.
[12]         J. Kim, S. A. Gadsden, and S. A. Wilkerson, “A Comprehensive Survey of Control Strategies for Autonomous Quadrotors,” Canadian Journal of Electrical and Computer Engineering, vol. 43, no. 1, pp. 3–16, 2020, doi: 10.1109/CJECE.2019.2920938.
[13]         Lebao Li, Lingling Sun, and Jie Jin, “Survey of advances in control algorithms of quadrotor unmanned aerial vehicle,” in 2015 IEEE 16th International Conference on Communication Technology (ICCT), Oct. 2015, pp. 107–111. doi: 10.1109/ICCT.2015.7399803.
[14]         B. Song, Y. Liu, and C. Fan, “Feedback linearization of the nonlinear model of a small-scale helicopter,” J Control Theory Appl, vol. 8, no. 3, pp. 301–308, 2010, doi: 10.1007/s11768-010-0017-8.
[15]         D. Lee, H. J. Kim, and S. Sastry, “Feedback Linearization vs. Adaptive Sliding Mode Control for a Quadrotor Helicopter,” Int J Control Autom Syst, vol. 7, no. 3, pp. 419–428, 2009, doi: 10.1007/s12555-009-0311-8.
[16]         Z. Shulong, A. Honglei, Z. Daibing, and S. Lincheng, “A new feedback linearization LQR control for attitude of quadrotor,” 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, pp. 1593–1597, 2014, doi: 10.1109/ICARCV.2014.7064553.
[17]         C.-C. Chen and Y.-T. Chen, “Feedback Linearized Optimal Control Design for Quadrotor with Multi-performances,” IEEE Access, pp. 1–1, Feb. 2021, doi: 10.1109/access.2021.3057378.
[18]         M. A. Lotufo, L. Colangelo, and C. Novara, “Feedback Linearization for Quadrotors UAV,” Jun. 2019, doi: https://doi.org/10.48550/arXiv.1906.04263.
[19]         L. Martins, C. Cardeira, and P. Oliveira, “Inner-outer feedback linearization for quadrotor control: two-step design and validation,” Nonlinear Dynamics 2022 110:1, vol. 110, no. 1, pp. 479–495, Jun. 2022, doi: 10.1007/S11071-022-07632-Y.
[20]         W. C. Gan, L. Qiu, and J. Wang, “An Adaptive Sinusoidal Disturbance Rejection Controller for Single-Input-Single-Output Systems,” IFAC Proceedings Volumes, vol. 41, no. 2, pp. 15684–15689, 2008, doi: 10.3182/20080706-5-KR-1001.02652.
[21]         N. el Gmili, M. Mjahed, A. el Kari, and H. Ayad, “Intelligent PSO-based PDs/PIDs controllers for an unmanned quadrotor,” International Journal of Intelligent Engineering Informatics, vol. 6, no. 6, p. 548, 2018, doi: 10.1504/IJIEI.2018.096579.
[22]         D. T. Pham and D. Karaboga, “Intelligent optimisation techniques : genetic algorithms, tabu search, simulated annealing and neural networks,” p. 302, 2000.
[23]         V. Kachitvichyanukul, “Comparison of Three Evolutionary Algorithms: GA, PSO, and DE,” Industrial Engineering and Management Systems, vol. 11, no. 3, pp. 215–223, Sep. 2012, doi: 10.7232/iems.2012.11.3.215.
[24]         Timothy J. Ross, Fuzzy Logic with Engineering Applications, Second., no. John Wiley & Sons. John Wiley & Sons, 2004.
[25]         A. Visioli, “Tuning of PID controllers with fuzzy logic,” IEE Proceedings: Control Theory and Applications, vol. 148, no. 1, pp. 1–8, Jan. 2001, doi: 10.1049/IP-CTA:20010232.
[26]         Seyed Mostafa Kia, Fuzzy Logic in MATLAB. Kian Publication.
[27]         Zhen-Yu Zhao, M. Tomizuka, and S. Isaka, “Fuzzy gain scheduling of PID controllers,” IEEE Trans Syst Man Cybern, vol. 23, no. 5, pp. 1392–1398, 1993, doi: 10.1109/21.260670.
[28]         Ghanifar Mana, Kamzan Milad, and Tayefi Morteza, “PID gain tuning using intelligent adaptive and non-adaptive algorithms: with implementation in a quadrotor ,” in The 19th International Conference of Iranian Aerospace Society, 2021. Accessed: Jan. 19, 2022. [Online]. Available: https://civilica.com/doc/1362264.