Iranian Journal of Mechanical Engineering Transactions of the ISME

Iranian Journal of Mechanical Engineering Transactions of the ISME

Coordinated Landing Control of Multiple Vehicles using Rough Neural Network and Sliding Mode Methods

Document Type : Research Paper

Authors
Saba Nikseresht 1
Mahdi Jafari-Nadoushan 2
1 Ph.D. Candidate, Faculty of Aerospace Engineering, K.N. Toosi University of Technology, Tehran, Iran
2 Assistant Professor, Faculty of Aerospace Engineering, K.N. Toosi University of Technology, Tehran, Iran
10.30506/jmee.2025.2038103.1355
Abstract
This paper investigates and compares the coordinated landing of multiple vehicles using the Rough Mimetic Neural Controller (R-MNC) and Sliding Mode Controller. Coordinated landing scenarios, critical for advanced aerospace operations, require robust control strategies to handle nonlinear dynamics and ensure safe, precise landings. In the simulations, nonlinear dynamic equations of the agents are used, and control signals are allocated among system actuators based on inputs such as gamma angle, angle of attack, and altitude rate. The NSGA-II optimization algorithm tunes controller parameters to enhance performance and reduce control effort. Results demonstrate that both controllers effectively stabilize the vehicle and achieve desired outcomes, but R-MNC shows superior adaptability in dynamic environments, particularly under varying conditions. This study examines the trade-offs and complementary advantages of both methods, offering insights for designing reliable coordinated landing strategies in complex aerospace missions.
Keywords
Control allocation
Fuzzy system
Rough Neural Network
Sliding mode controller
Multi agent systems

Subjects

[1] Á. Soriano, E. J. Bernabeu, Á. Valera, and M. Vallés, "Multi-agent Systems for Evasive Maneuvers of Mobile Robots through Agreements," in International Conference on Informatics in Control, Automation and Robotics, 2013, Vol. 2: Scitepress, pp. 140-147, https://doi.org/10.5220/0004430101400147.
 
[2] J. Wang, X. Ding, C. Wang, L. Liang, and H. Hu, "Affine Formation Control for Multi-agent Systems with Prescribed Convergence Time," Journal of the Franklin Institute, Vol. 358, No. 14, pp. 7055-7072, 2021, https://doi.org/10.1016/j.jfranklin.2021.07.019.
 
[3] W. Du, T. Guo, J. Chen, B. Li, G. Zhu, and X. Cao, "Cooperative Pursuit of Unauthorized UAVs in Urban Airspace via Multi-agent Reinforcement Learning," Transportation Research Part C: Emerging Technologies, Vol. 128, pp. 103122, 2021, https://doi.org/10.1016/j.trc.2021.103122.
 
[4] R. Wang, "Adaptive Output-feedback Time-varying Formation Tracking Control for Multi-agent Systems with Switching Directed Networks," Journal of the Franklin Institute, Vol. 357, No. 1, pp. 551-568, 2020, https://doi.org/10.1016/j.jfranklin.2019.11.077.
 
[5] A. A. Elgohary, and B. Moidel, "A Novel Use of Model Predictive Control with Extremum Seeking in Formation Flight," in AIAA SCITECH 2024 Forum, 2024, pp. 2750, https://doi.org/10.2514/6.2024-2750.
 
[6] Y. Wang, N. Li, B. Wang, X. He, Y. Zhu, and M. Zhou, "Local Pursuit Strategy-inspired Cooperative Formation Flight and Collision Avoidance for UAV Cluster," in ASME International Mechanical Engineering Congress and Exposition, 2023, Vol. 87639: American Society of Mechanical Engineers, pp. V006T07A083, https://doi.org/10.1115/IMECE2023-113399.
 
[7] J. Martinez-Ponce, B. Herkenhoff, A. Aboelezz, and M. Hassanalian, "Load Distribution on “V” and Echelon Formation Flight of Flapping-wings," in AIAA SCITECH 2024 Forum, 2024, pp. 2337, https://doi.org/10.2514/6.2024-2337.
 
[8] A. Tabassum and H. Bai, "Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid," Aerospace Science and Technology, Vol. 121, pp. 107323, 2022, https://doi.org/10.1016/j.ast.2021.107323.
 
[9] L. Cao, G.-P. Liu, and D.-W. Zhang, "Cloud-based Predictive Formation Control of Networked Multi-agent System and Its Application to Air Bearing Spacecraft Simulators," ISA Transactions, Vol. 138, pp. 696-704, 2023, https://doi.org/10.1016/j.isatra.2023.03.013.
 
[10] C. Kang, J. Xu, and Y. Bian, "Affine Formation Maneuver Control for Multi-agent Based on Optimal Flight System," Applied Sciences, Vol. 14, No. 6, p. 2292, 2024, https://doi.org/10.3390/app14062292.
 
[11] R.C. Nelson, "Flight Stability and Automatic Control", New York: WCB/McGraw Hill, Vol. 2, 1998.
 
[12] Q. Bian, B. Nener, and X. Wang, "An Improved NSGA-II Based Control Allocation Optimisation for Aircraft Longitudinal Automatic Landing System," International Journal of Control, Vol. 92, No. 4, pp. 705-716, 2019, https://doi.org/10.1080/00207179.2018.1473643.
 
[13] M. Teshnehlab, and K. Watanabe, "Flexible Neural Networks. In Intelligent Control Based on Flexible Neural Networks", Dordrecht: Springer Netherlands, pp. 61-84. 1999.
 
[14] D. V. Rao and T. H. Go, "Automatic Landing System Design using Sliding Mode Control," Aerospace Science and Technology, Vol. 32, No. 1, pp. 180-187, 2014, https://doi.org/10.1016/j.ast.2013.10.001.
 
Iranian Journal of Mechanical Engineering Transactions of the ISME
Volume 26, Issue 2 - Serial Number 45
September 2025
Pages 93-110