Designing a Multidimensional Sliding Mode Control System for Quadcopters Under Fault Conditions: Enhanced Fault Tolerance and Robustness
Subject Areas : International Journal of Smart Electrical Engineering
Mohammad Amin Ahmadi
1
,
Mehdi Siahi
2
,
Ali Moarefianpour
3
,
Soodabeh Soleymani Morcheh Khorti
4
1 -
2 -
3 -
4 -
Keywords: Quadcopter, Multidimensional Sliding Mode Control, Faults, Dynamic Control.,
Abstract :
Unmanned aerial vehicles, particularly quadcopters, have gained widespread popularity across various applications. However, their operation is susceptible to faults, which can compromise stability and performance. This paper introduces a novel Multidimensional Sliding Mode Control (MSMC) strategy for quadcopters, designed to enhance fault tolerance and overall system robustness. The proposed approach incorporates advanced fault detection and isolation algorithms, enabling real-time identification and mitigation of diverse fault scenarios. Extensive simulations and experimental evaluations demonstrate the MSMC strategy's superiority over several existing fault-tolerant control techniques, achieving at least 18.47% higher accuracy in fault damping. Additionally, the sliding mode control system exhibits improved stability characteristics, with a response time reduction of at least 6.45% compared to conventional methods. The robustness and adaptability of the MSMC make it a promising solution for ensuring safe and reliable quadcopter operations under various fault conditions, paving the way for enhanced performance and increased operational safety across a wide range of applications.
1- Shamsabadi, M., & Kardehi Moghaddam, R. (2021). Underwater robot trajectory control using nonsingular terminal sliding mode controller. International Journal of Smart Electrical Engineering, 10(03), 117-125.
2- Nguyen, N. P., Mung, N. X., Thanh, H. L. N. N., Huynh, T. T., Lam, N. T., & Hong, S. K. (2021). Adaptive sliding mode control for attitude and altitude system of a quadcopter UAV via neural network. IEEE Access, 9, 40076-40085.
3- Motaei, M., Edrisi, M., & Shahgholian, G. (2023). Controller Design and Simulation High-Gain Nonlinear Observer for Three-Joint PUMA Robot Regards to Adaptive Fuzzy Sliding Mode Controller with Uncertainly Condition. International Journal of Smart Electrical Engineering, 12(03), 209-220.
4- Ebrahimi, M. H. E., Menhaj, M. B., Nazari Monfared, M., & Fakharian, A. (2021). Design of a Free Model Adaptive-Neural Controller for Level and Temperature Control of Liquid Storage Tanks. International Journal of Smart Electrical Engineering, 10(03), 105-116.
5- Said, M., Larabi, M. S., & Kherief, N. M. (2023). Robust control of a quadcopter using PID and H∞ controller. Turkish Journal of Electromechanics and Energy, 8(1), 3-11.
6- Merzban, M., Khalaf, A. A., & Hamed, H. F. A. (2023). Comparison of various control techniques applied to a quadcopter. Journal of Advanced Engineering Trends, 42(2), 233-244.
7- Şahin, İ., & Ulu, C. (2023). Altitude control of a quadcopter using interval type-2 fuzzy controller with dynamic footprint of uncertainty. ISA transactions, 134, 86-94.
8- Cai, B., & Pei, D. (2024). Bumpless‐transfer‐based fault tolerant control for the quadcopter: Piecewise homogeneous emission probability approach. International Journal of Robust and Nonlinear Control, 34(1), 341-358.
9- Praveen, V., & Pillai, S. (2016). Modeling and simulation of quadcopter using PID controller. International Journal of Control Theory and Applications, 9(15), 7151-7158.
10- Ahmad, F., Kumar, P., & Patil, P. P. (2018). Modeling and simulation of a quadcopter with altitude and attitude control. Nonlinear Studies, 25(2).
11- Cheng, X., Liu, Z. W., Hou, H., & Guan, Z. H. (2022). Disturbance observer-based nonsingular fixed-time sliding mode tracking control for a quadcopter. Science China Information Sciences, 65(9), 192202.
12- Rinaldi, M., Primatesta, S., & Guglieri, G. (2023). A comparative study for control of quadrotor UAVs. Applied Sciences, 13(6), 3464.
13- Ferede, R., de Croon, G., De Wagter, C., & Izzo, D. (2024). End-to-end neural network based optimal quadcopter control. Robotics and Autonomous Systems, 172, 104588.
14- Cedro, L., Wieczorkowski, K., & Szcześniak, A. (2024). An Adaptive PID Control System for the Attitude and Altitude Control of a Quadcopter. acta mechanica et automatica, 18(1), 29-39.
15- Bucki, N., Tang, J., & Mueller, M. W. (2022). Design and control of a midair-reconfigurable quadcopter using unactuated hinges. IEEE Transactions on Robotics, 39(1), 539-557.
16- Elagib, R., & Karaarslan, A. (2023). Sliding mode control-based modeling and simulation of a quadcopter. J. Eng. Res. Rep, 24(3), 32-41.
17- Asadi, D., Ahmadi, K., & Nabavi, S. Y. (2022). Fault-tolerant trajectory tracking control of a quadcopter in presence of a motor fault. International Journal of Aeronautical and Space Sciences, 23(1), 129-142.
18- Thanh, H. L. N. N., & Hong, S. K. (2018). Quadcopter robust adaptive second order sliding mode control based on PID sliding surface. IEEE Access, 6, 66850-66860.
19- Kurak, S., & Hodzic, M. (2018). Control and estimation of a quadcopter dynamical model. Periodicals of Engineering and Natural Sciences, 6(1), 63-75.
20- Islam, M., Okasha, M., & Idres, M. M. (2017, December). Dynamics and control of quadcopter using linear model predictive control approach. In IOP conference series: materials science and engineering (Vol. 270, No. 1, p. 012007). IOP Publishing.
21- Belmouhoub, A., Bouzid, Y., Medjmadj, S., Derrouaoui, S. H., Siguerdidjane, H., & Guiatni, M. (2023). Fast terminal synergetic control for morphing quadcopter with time-varying parameters. Aerospace Science and Technology, 141, 108540.
22- Lee, J. W., Xuan-Mung, N., Nguyen, N. P., & Hong, S. K. (2023). Adaptive altitude flight control of quadcopter under ground effect and time-varying load: Theory and experiments. Journal of Vibration and Control, 29(3-4), 571-581.