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Control Tuning for the Quadcopter Unmanned Aerial Vehicle Based on Genetic Evolutionary Algorithm


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DOI: https://doi.org/10.15866/iremos.v15i6.22708

Abstract


The quadcopter is an unmanned aerial vehicle that has been used in civil and military applications because of its relatively low cost, simple design, and ease of maintenance in addition to its unique flying characteristics and vast potential. Six Proportional-Integral-Derivative (PID) controllers are usually built for the position and attitude control of the quadcopter. However, tuning the controllers’ parameters poses a real challenge, as the system is highly coupled and contains two cascaded control loops while being underactuated (only four control inputs for the six degrees of freedom). In this study, a quadcopter model has been implemented by using MATLAB / SIMULINK where the model has been constructed to be integrated with the evolutionary genetic algorithm in addition to a third-party toolbox that is used to simulate fractional order calculus. The gains of the traditional and the fractional-order proportional-integral-derivative controllers have been tuned by using the genetic algorithm against different path shapes. The performance of the manually and the genetic algorithm tuned proportional-integral-derivative controllers have been compared with the genetic algorithm tuned fractional-order proportional-integral-derivative controllers. The genetic algorithm tuned fractional-order proportional-integral-derivative controllers have been superior and more robust in all cases with a lower fitness function value, much smother responses, and higher trajectory tracking ability.
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Keywords


GA-Optimized FOPID; GA-Optimized PID; Multirotor; Quadcopter; UAV

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References


J. Tisdale, Z. Kim, and J. Hedrick, Autonomous UAV path planning and estimation, IEEE Robotics & Automation Magazine, Vol. 16(Issue 2), pp. 35-42, 2009.
https://doi.org/10.1109/MRA.2009.932529

H. Saha, S. Basu, S. Auddy, R. Dey, A. Nandy, D. Pal, N. Roy, S. Jasu, A. Saha, S. Chattopadhyay, and M. Tamanna, A low cost fully autonomous GPS (Global Positioning System) based quad copter for disaster management, IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pp. 654-660, Las Vegas, Nevada, USA, 2018.
https://doi.org/10.1109/CCWC.2018.8301782

J. Navia, I. Mondragon, D. Patino, and J. Colorado, Multispectral mapping in agriculture: Terrain mosaic using an autonomous quadcopter UAV, International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1351-1358, Arlington, VA, United States, 2016.
https://doi.org/10.1109/ICUAS.2016.7502606

U.F. Ukaegbu, L.K. Tartibu, M.O. Okwu, and I.O. Olayode, Development of a light-weight unmanned aerial vehicle for precision agriculture, Sensors, Vol. 21(Issue 13), p. 4417, 2021.
https://doi.org/10.3390/s21134417

S. Radiansyah, M.D. Kusrini, L.B. Prasetyo, Quadcopter applications for wildlife monitoring, IOP Conference Series: Earth and environmental science, Vol. 54, p. 012066, Sanya, China, 2017.
https://doi.org/10.1088/1755-1315/54/1/012066

J.H. Gillula, H. Huang, M.P. Vitus, and C.J. Tomlin, Design of guaranteed safe maneuvers using reachable sets: Autonomous quadrotor aerobatics in theory and practice, IEEE International Conference on Robotics and Automation, pp. 1649-1654, Anchorage, Alaska, 2010.
https://doi.org/10.1109/ROBOT.2010.5509627

S. Lupashin, A. Schöllig, M. Sherback, and R. D'Andrea, A simple learning strategy for high-speed quadcopter multi-flips, IEEE international conference on robotics and automation, pp. 1642-1648, Anchorage, Alaska, 2010.
https://doi.org/10.1109/ROBOT.2010.5509452

D. Mellinger, N. Michael, and V. Kumar, Trajectory generation and control for precise aggressive maneuvers with quadrotors, The International Journal of Robotics Research, Vol. 31(Issue 5), pp. 664-674, 2012.
https://doi.org/10.1177/0278364911434236

B. Njinwoua, and A.V. Wouwer, Cascade attitude control of a quadcopter in presence of motor asymmetry, IFAC-PapersOnLine, Vol. 51(Issue 4), pp. 113-118, February 2018.
https://doi.org/10.1016/j.ifacol.2018.06.055

C.S. Subudhi, and D. Ezhilarasi, Modeling and trajectory tracking with cascaded PD controller for quadrotor, Procedia computer science, Vol. 133, pp. 952-959, 2018.
https://doi.org/10.1016/j.procs.2018.07.082

S. Abdelhay, and A. Zakriti, Modeling of a quadcopter trajectory tracking system using pid controller, Procedia Manufacturing, Vol. 32, pp. 564-571, 2019.
https://doi.org/10.1016/j.promfg.2019.02.253

Housny, H., Chater, E., El Fadil, H., Observer-Based Enhanced ANFIS Control for a Quadrotor UAV, (2021) International Review on Modelling and Simulations (IREMOS), 14 (1), pp. 55-69.
https://doi.org/10.15866/iremos.v14i1.18991

F. Ahmad, P. Kumar, A. Bhandari, and P.P. Patil, Simulation of the Quadcopter Dynamics with LQR based Control, Materials Today: Proceedings, Vol. 24, pp. 326-332, 2020.
https://doi.org/10.1016/j.matpr.2020.04.282

E. Kuantama, T. Vesselenyi, S. Dzitac, and R. Tarca, PID and Fuzzy-PID control model for quadcopter attitude with disturbance parameter, International journal of computers communications & control, Vol. 12(Issue 1), pp. 519-532, 2017.
https://doi.org/10.15837/ijccc.2017.4.2962

V.K. Tripathi, L. Behera, and N. Verma, Design of sliding mode and backstepping controllers for a quadcopter, 39th national systems conference (NSC), pp. 1-6, Greater Noida, India, 2015.
https://doi.org/10.1109/NATSYS.2015.7489097

H. Razmi, and S. Afshinfar, Neural network-based adaptive sliding mode control design for position and attitude control of a quadrotor UAV, Aerospace Science and technology, Vol. 91, pp. 12-27, 2019.
https://doi.org/10.1016/j.ast.2019.04.055

J. Hwangbo, I. Sa, R. Siegwart, and M. Hutter, Control of a quadrotor with reinforcement learning, IEEE Robotics and Automation Letters, Vol. 2(Issue 4), pp. 2096-2103, 2017.
https://doi.org/10.1109/LRA.2017.2720851

Izidi, L., Houalef, M., Implementation of Hybrid Solar/Li-ion Energy System for Quadcopter, (2022) International Review of Mechanical Engineering (IREME), 16 (7), pp. 329-335.
https://doi.org/10.15866/ireme.v16i7.22174

Roa, C., Amaya, D., Ramos, O., Control and Path Planning for Quadrotor Oriented to Agricultural Support in Annona Muricata Crops, (2022) International Review of Automatic Control (IREACO), 15 (5), pp. 222-232.
https://doi.org/10.15866/ireaco.v15i5.21301

Loubar, H., Boushaki, R., Aouati, A., Bouanzoul, M., Sliding Mode Controller for Linear and Nonlinear Trajectory Tracking of a Quadrotor, (2020) International Review of Automatic Control (IREACO), 13 (3), pp. 128-138.
https://doi.org/10.15866/ireaco.v13i3.18522

R. Ayad, W. Nouibat, M. Zareb, and Y.B. Sebanne, Full control of quadrotor aerial robot using fractional-order FOPID, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, Vol. 43(Issue 1), pp. 349-360, 2019.
https://doi.org/10.1007/s40998-018-0155-4

R. Cajo, C. Copot, C.M. Ionescu, R. De Keyser, and D. Plaza, Fractional order PD path-following control of an AR. Drone quadrotor, IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI), pp. 000291-000296, Timisoara, Romania, 2018.
https://doi.org/10.1109/SACI.2018.8440944

Bani Younes, A., Batayneh, W., Khamis, A., Cooperative Aerial-Ground Robotic System Using Genetic Algorithm Auto-Tuned Fractional Order PID Control, (2021) International Review of Automatic Control (IREACO), 14 (6), pp. 348-359.
https://doi.org/10.15866/ireaco.v14i6.21365

W. Dong, G. Gu, X. Zhu, and H. Ding, Modeling and control of a quadrotor UAV with aerodynamic concepts, International Journal of Aerospace and Mechanical Engineering, Vol. 7(Issue 5), pp. 901-906, 2013.

Q. Quan, Introduction to Multicopter Design and Control (Springer Nature, 2017, pp. 251-274).
https://doi.org/10.1007/978-981-10-3382-7_11

Y. Chen, I. Petras, and D. Xue, Fractional order control-a tutorial, American Control Conference, pp. 1397-1411, Saint Louis, MO, United State, 2009.
https://doi.org/10.1109/ACC.2009.5160719

C.R. Houck, J. Joines, and M.G. Kay, A genetic algorithm for function optimization: a Matlab implementation, Ncsu-ie tr, Vol. 95(Issue 09), pp. 1-10, 1995.

M. Deepyaman, A. Ayan, C. Mithun, K. Amit, and J. Ramdoss, Tuning PID and PIλ Dμ controllers using the integral time absolute error criteria, 4th International Conference on Information and Automation for Sustainability ICIAFS, pp. 457-462, Colombo, Sri Lanka, 2008.

Batayneh, W., Aburmaileh, Y., Adeeb, M., Al-Karasneh, A., Smooth 2D Navigation in Hazardous Areas Utilizing a GA-PID Controlled Omnidirectional Mobile Robot with Kinematic Constraint Consideration, (2021) International Review on Modelling and Simulations (IREMOS), 14 (3), pp. 213-220.
https://doi.org/10.15866/iremos.v14i3.20237

Gorial, I., A Novel Numerical Method for Variable Fractional Order Cable Model, (2020) International Review on Modelling and Simulations (IREMOS), 13 (5), pp. 313-318.
https://doi.org/10.15866/iremos.v13i5.19342


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