Designing a Multidimensional Sliding Mode Control System for Quadcopters Under Fault Conditions: Enhanced Fault Tolerance and Robustness
محورهای موضوعی : مهندسی هوشمند برق
Mohammad Amin Ahmadi
1
,
Mehdi Siahi
2
,
Ali Moarefianpour
3
,
Soodabeh Soleymani Morcheh Khorti
4
1 - Department of Electrical Engineering, Science and Research Branch, Islamic Azad university, Tehran, Iran
2 - Department of Electrical Engineering, Science and Research Branch, Islamic Azad university, Tehran, Iran
3 - Department of Electrical Engineering, Science and Research Branch, Islamic Azad university, Tehran, Iran
4 - Department of Electrical Engineering, Science and Research Branch, Islamic Azad university, Tehran, Iran
کلید واژه: Quadcopter, Multidimensional Sliding Mode Control, Faults, Dynamic Control.,
چکیده مقاله :
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.
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.
