Coordinated Control of Multiple Agents for Automatic Landing and Execution of Formation Flights using Fuzzy Control Allocation Approach

Document Type : Research Article

Authors

1 K. N. Toosi University of Technology

2 Department of Space Engineering, Faculty of Aerospace Engineering, K. N. Toosi University of Technology.

Abstract

In many next-generation platforms, the application of control allocation approaches is one of the most effective methods for performing coordinated aerial maneuvers. This approach minimizes the energy required for various operations, compensates for actuator faults, enhances reliability and dependability, and prevents actuator saturation. This paper addresses the coordinated control of multiple agents for autonomous landing and coordinated maneuvers using a fuzzy control allocation approach. In this framework, one of the platforms assumes the role of a leader agent, while the other two operate as follower agents. By employing a fuzzy controller, optimizing its parameters with a genetic algorithm, and allocating control signals among lift actuators and the thrust vector control system, agents can be guided with desired stability, sufficient accuracy, and minimal control effort to a specific altitude, followed by executing well-organized and synchronized maneuvers. The results demonstrate that the fuzzy control allocation method achieves high precision in controlling the landing of platforms to a designated altitude. Ultimately, the platforms execute coordinated autonomous landing maneuvers with favorable conditions, sufficient stability, high accuracy, relatively short execution time, and minimal control effort.

Keywords

Main Subjects


[1] S.A. Tohidi, Control of Fault Tolerance using Adaptive Control for Allocation of Virtual Inverse Manipulators along Null Space, K. N. Toosi University of Technology, 1391.
[2] C. Hao, W. Xiangke, S. Lincheng, C. Yirui, Formation flight of fixed-wing UAV swarms: A group-based hierarchical approach, Chinese Journal of Aeronautics, 34(2) (2021) 504-515.
[3] Y. Kartal, K. Subbarao, N.R. Gans, A. Dogan, F. Lewis, Distributed backstepping based control of multiple UAV formation flight subject to time delays, IET Control Theory & Applications, 14(12) (2020) 1628-1638.
[4] J. Wang, Z. Zhou, C. Wang, Z. Ding, Cascade structure predictive observer design for consensus control with applications to UAVs formation flying, Automatica, 121 (2020) 109200.
[5] Z. Yan, L. Han, X. Li, X. Dong, Q. Li, Z. Ren, Event-triggered formation control for time-delayed discrete-time multi-agent system applied to multi-UAV formation flying, Journal of the Franklin Institute, 360(5) (2023) 3677-3699.
[6] M. Kankashvar, H. Bolandi, N. Mozayani, Multi-agent Q-Learning control of spacecraft formation flying reconfiguration trajectories, Advances in Space Research, 71(3) (2023) 1627-1643.
[7] P. Artitthang, M. Xu, M. Lin, Y. He, Robust optimal sliding mode control for the deployment of Coulomb spacecraft formation flying, Advances in Space Research, 71(1) (2023) 439-455.
[8] J. Martinez-Ponce, B. Herkenhoff, A. Aboelezz, M. Hassanalian, Load Distribution on “V” and Echelon Formation Flight of Flapping-Wings, in:  AIAA SCITECH 2024 Forum, 2024, pp. 2337.
[9] A.A. Elgohary, B. Moidel, A Novel Use of Model Predictive Control with Extremum Seeking in Formation Flight, in:  AIAA SCITECH 2024 Forum, 2024, pp. 2750.
[10] Y. Wang, N. Li, B. Wang, X. He, Y. Zhu, M. Zhou, Local Pursuit Strategy-Inspired Cooperative Formation Flight and Collision Avoidance for UAV Cluster, in:  ASME International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, 2023, pp. V006T007A083.
[11] Q. Bian, B. Nener, X. Wang, An improved NSGA-II based control allocation optimisation for aircraft longitudinal automatic landing system, International Journal of Control, 92(4) (2019) 705-716.
[12] J. Cao, F. Garrett Jr, E. Hoffman, H. Stalford, Analytical aerodynamic model of a high alpha research vehicle wind-tunnel model, 1990.
[13] N. MR, Aircraft Dynamics. Hoboken, 2012.
[14] C.S. Buttrill, P.D. Arbuckle, K.D. Hoffler, Simulation model of a twin-tail, high performance airplane, 1992.
[15] A.R. Babaei, M. Mortazavi, M.H. Moradi, Classical and fuzzy-genetic autopilot design for unmanned aerial vehicles, Applied Soft Computing, 11(1) (2011) 365-372.