Analysis of Nonlinear Control Strategies for Quadrotor Stability and Trajectory Tracking
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Abstract
The quadrotor control as nonlinear and underactuated systems is a huge challenge in getting a fine stabilization and trajectory tracking. The present work seeks to introduce a novel hybrid control structure composed of an Internal Model Control-based Proportional-Integral (IMC-PI) controller, with the integration of a Sliding Mode Controller (SMC) and an Extended State Observer (ESO). The main purpose is to achieve improvement of stability and tracking performance under dynamic flight operations. A six degrees of freedom (6-DoF) dynamic model is developed under rigid body structure and proportional thrust-drag assumptions. An IMC-PI controller is initially employed to stabilize the Euler angles and vertical position but is not able to control the lateral positions (x and y). For this, a hybrid SMC-ESO method is incorporated for enhanced robustness along with precise path tracking. Simulation results reveal that while the stand-alone IMC-PI controller can stabilize altitude and orientation within 3 seconds, it cannot converge to the complete position. Nevertheless, the hybrid controller achieves very precise moving in complex trajectories (helical and figure-eight) within 25 seconds, which outperforms the stand-alone solution. The results confirm the viability of the hybrid controller in autonomous UAV operations where stability and precision are of particular importance.
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References
Elnady, A., Osama, J., Osama, I., & Khalil, A. (2019). Design and manufacturing of quadcopter. • The 4th International under-Graduate Research Conference, IUGRC 2019, July 29th -August 1st, 2019 At: Military Technical College, Cairo, Egypt.
Pappalardo, C. M., Del Giudice, M., Oliva, E. B., Stieven, L., & Naddeo, A. (2023). Computer-aided design, multibody dynamic modeling, and motion control analysis of a quadcopter system for delivery applications. Machines, 11(4), 464. https://doi.org/10.3390/machines11040464
Abdulkareem, A., Oguntosin, V., Popoola, O. M., & Idowu, A. A. (2022). Modeling and nonlinear control of a quadcopter for stabilization and trajectory tracking. Journal of Engineering, 2022, 1–19. https://doi.org/10.1155/2022/2449901
Sampaio, D. D., & Silva, W. L. S. (2021). Sistema nebuloso para navegação autônoma de veículo aéreo não tripulado / Fuzzy system for autonomous unmanned aerial vehicle navigation. Brazilian Journal of Development, 7(4), 34520–34536. https://doi.org/10.34117/bjdv7n4-086
Muhamad, A., Panjaitan, S. D., & Yacoub, R. R. (2024). Design and development of flight controller for quadcopter drone control. Telecommunications Computers and Electricals Engineering Journal, 1(3), 279. https://doi.org/10.26418/telectrical.v1i3.73681
Telli, K., et al. (2023). A comprehensive review of recent research trends on unmanned aerial vehicles (UAVs). Systems, 11(8), 400. https://doi.org/10.3390/systems11080400
Polvara, R., Sharma, S., Wan, J., Manning, A., & Sutton, R. (2018). Vision-based autonomous landing of a quadrotor on the perturbed deck of an unmanned surface vehicle. Drones, 2(2), 15. https://doi.org/10.3390/drones2020015
How, J. P., Frazzoli, E., & Chowdhary, G. V. (2014). Linear flight control techniques for unmanned aerial vehicles. In Springer eBooks (pp. 529–576). https://doi.org/10.1007/978-90-481-9707-1_49
Elkholy, H., & Habib, M. K. (2015). Dynamic modeling and control techniques for a quadrotor. In Advances in Computational Intelligence and Robotics Book Series (pp. 408–454). https://doi.org/10.4018/978-1-4666-7387-8.ch014
Thanh, H. L. N. N., et al. (2022). Quadcopter UAVs extended states/disturbance observer-based nonlinear robust backstepping control. Sensors, 22(14), 5082. https://doi.org/10.3390/s22145082
Bouabdallah, S., Murrieri, P., & Siegwart, R. (2004). Design and control of an indoor micro quadrotor. In IEEE International Conference on Robotics and Automation (ICRA '04) (Vol. 5, pp. 4393–4398). https://doi.org/10.1109/ROBOT.2004.1302409
Sahrir, N. H., & Basri, M. A. M. (2022). Modelling and manual tuning PID control of quadcopter. In Lecture Notes in Electrical Engineering (pp. 346–357). https://doi.org/10.1007/978-981-19-3923-5_30
Maaruf, M., Abubakar, A. N., & Gulzar, M. M. (2024). Adaptive backstepping and sliding mode control of a quadrotor. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(11). https://doi.org/10.1007/s40430-024-05188-z
Alexis, K., Tzes, A., & Nikolakopoulos, G. (2012). Model predictive quadrotor control: Attitude, altitude and position experimental studies. IET Control Theory and Applications, 6(12), 1812–1827. https://doi.org/10.1049/iet-cta.2011.0348
Lim, Y., Ramasamy, S., Gardi, A., Kistan, T., & Sabatini, R. (2017). Cognitive human-machine interfaces and interactions for unmanned aircraft. Journal of Intelligent & Robotic Systems, 91(3–4), 755–774. https://doi.org/10.1007/s10846-017-0648-9
Gopalakrishnan, E. (2017). Quadcopter flight mechanics model and control algorithms (Master’s thesis, Czech Technical University in Prague).
Lee, D., Kim, H. J., & Sastry, S. (2009). Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter. International Journal of Control Automation and Systems, 7(3), 419–428. https://doi.org/10.1007/s12555-009-0311-8
Uzair, M., Sipra, K., Waqas, M., & Tu, S. (2020, October 16). Applications of MyO armband using EMG and IMU signals. In 2020 IEEE 3rd International Conference on Mechatronics, Robotics and Automation (ICMRA). https://doi.org/10.1109/ICMRA51221.2020.9398375
Gao, Y., Lin, J., Yang, L., & Zhu, J. (2016). Development and calibration of an accurate 6-degree-of-freedom measurement system with total station. Measurement Science and Technology, 27(12), 125103. https://doi.org/10.1088/0957-0233/27/12/125103
Lee, D., Burg, T., Dawson, D., Shu, D., Xian, B., & Tatlicioglu, E. (2009). Robust tracking control of an underactuated quadrotor aerial-robot based on a parametric uncertain model. In 2009 IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 3187–3192).
Sabatino, F. (2015). Quadcopter control: Modeling, nonlinear control design, and simulation.
Amer, T. S., & Abady, I. M. (2017). Solutions of Euler’s dynamic equations for the motion of a rigid body. Journal of Aerospace Engineering, 30(4). https://doi.org/10.1061/(asce)as.1943-5525.0000736
H. P. R., M. S. Y., & Lakshmanaprabu, S. K. (2019). Internal model controller based PID with fractional filter design for a nonlinear process. International Journal of Electrical and Computer Engineering (IJECE), 10(1), 243. https://doi.org/10.11591/ijece.v10i1.pp243-254
Saxena, S., & Hote, Y. (2012). Advances in internal model control technique: A review and future prospects. IETE Technical Review, 29(6), 461. https://doi.org/10.4103/0256-4602.105001
Bucak, İ. Ö. (2020). An in-depth analysis of sliding mode control and its application to robotics. In IntechOpen eBooks. https://doi.org/10.5772/intechopen.93027
Yang, X., & Huang, Y. (2009). Capabilities of extended state observer for estimating uncertainties. In American Control Conference (pp. 3700–3705). https://doi.org/10.1109/acc.2009.5160642
Li, Y., Zhu, Q., & Elahi, A. (2024). Quadcopter trajectory tracking based on model predictive path integral control and neural network. Drones, 9(1), 9. https://doi.org/10.3390/drones9010009
Yan, D., Zhang, W., Chen, H., & Shi, J. (2022). Robust control strategy for multi-UAVs system using MPC combined with Kalman-consensus filter and disturbance observer. ISA Transactions, 135, 35–51. https://doi.org/10.1016/j.isatra.2022.09.021
Li, B., Liu, H., Ahn, C. K., Wang, C., & Zhu, X. (2024). Fixed-Time tracking control of wheel mobile robot in slipping and skidding conditions. IEEE/ASME Transactions on Mechatronics, 1–11. https://doi.org/10.1109/tmech.2024.3401069