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Nejabat, Erfan, Nikoofard, Amirhosein. (1401). Hybrid Robust Model Predictive Based Controller for a Class of Multi-Agent Aerial dynamic Systems. نشریه مهندسی عمران امیرکبیر, 54(2), 209-224. doi: 10.22060/eej.2022.21055.5452
Erfan Nejabat; Amirhosein Nikoofard. "Hybrid Robust Model Predictive Based Controller for a Class of Multi-Agent Aerial dynamic Systems". نشریه مهندسی عمران امیرکبیر, 54, 2, 1401, 209-224. doi: 10.22060/eej.2022.21055.5452
Nejabat, Erfan, Nikoofard, Amirhosein. (1401). 'Hybrid Robust Model Predictive Based Controller for a Class of Multi-Agent Aerial dynamic Systems', نشریه مهندسی عمران امیرکبیر, 54(2), pp. 209-224. doi: 10.22060/eej.2022.21055.5452
Nejabat, Erfan, Nikoofard, Amirhosein. Hybrid Robust Model Predictive Based Controller for a Class of Multi-Agent Aerial dynamic Systems. نشریه مهندسی عمران امیرکبیر, 1401; 54(2): 209-224. doi: 10.22060/eej.2022.21055.5452

Hybrid Robust Model Predictive Based Controller for a Class of Multi-Agent Aerial dynamic Systems

AUT Journal of Electrical Engineering
مقاله 5، دوره 54، شماره 2، اسفند 2022، صفحه 209-224    اصل مقاله (1.73 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/eej.2022.21055.5452
نویسندگان
Erfan Nejabat1؛ Amirhosein Nikoofard* 2
1Mechanical Engineering Department, K.N.Toosi University of Technology, Tehran, Iran
2Electrical Engineering Department, K.N.Toosi University of Technology, Tehran, Iran
چکیده
The decentralized control of a multi-agent system with leader-follower consensus is investigated. The system is formulated in graph theory, and a general configuration for L-F formation is proposed. The goal for the formation is defined to track the predefined trajectory in the presence of high-frequency noise. The controller for the system is proposed on the basis of a model predictive-based controller. Different scenarios for a multi-agent system are considered, which lead to the linearization of the plant. Meanwhile, external structured disturbances are considered in the system. The novelty of the present paper addresses the gap between optimal controllers and robust controllers. The robustness of optimal controllers is not verified in the optimality of MPC controllers. Thus, a tube MPC theory is proposed to increase the robustness of the interacting noise system. Consequently, the optimal controller maintains robust throughout the existence of external disturbances and high frequency noises. Meanwhile, the closed-loop multi-agent response is investigated in the presence of external bounded disturbances. Next, The hybrid controller is designed for the formation. The switches take place between MPC and Tube-MPC controllers for each agent. On the other hand, hard constraints on control input and its variations and soft constraint on graph structures and topology of the multi-agent system are submitted. At length, the stability proof is considered for the closed loop multi-agent system. Finally, the simulation results demonstrate the formation results and the proposed controller can also satisfactorily deal with the high-frequency noise with hard and soft constraints.
کلیدواژه‌ها
Decentralized control؛ Formation؛ Multi-Agent system؛ Model Predictive Control؛ Hybrid system؛ Tube-MPC
مراجع
  1. Kabzan, L. Hewing, A. Liniger and M. Zeilinger. “Learning based model predictive control for autonomous racing” in Proceedings of the IEEE Trans, Robotic and Automation Letters, Vol.04, Issue:4. AIAA-2006-6200, 2019.
  2. Kabzan, L. Hewing, A. Liniger and M. Zeilinger. “Learning based model predictive control for autonomous racing” in Proceedings of the IEEE Trans, Robotic and Automation Letters, Vol.04, Issue:4. AIAA-2006-6200, 2019.
  3. Berberich, M. Koehler and J. Waslander. “Data driven model predictive control with stability and robustness guarantee” IEEE Transaction On Automatic Control, Navigation, and Control Conference, AIAA 2007-6461,2020.
  4. Castillo, R. Lozano and A. Dzul. “Model predictive control of three-axis gimbal system mounted on UAV for real time target tracking under external disturbances” Mechanical system and signal processing journal., Vol. 3, No. 6, pp. 45–55, 2020.
  5. Dai., Q. Cao, Y. Xia and Y. Gao “Distributed MPC for formation of multi-agent system with collision avoidance and obstacle avoidance” Engineering and Applied Mathematics, Journal of the Franklin Institude,2016.
  6. Nejabat, E. Azadi Yazdi and M. Mahzoon. “Hybrid formation control of a UAV multi-agent system ” The Annual 28th International Conference of Iranian Society of Mechanical Engineering-ISME, 2020.
  7. Sun, L. Dai, Y. Xia and K, H. Johansson “Robust MPC for tracking constrained unicycle robots with additive disturbance” Automatica, Vol. 90, 2018.
  8. Chevet, C. Stoica Maniu, C. Vlad, Y. Zhang and E. Camacho “Chance-Constrained MPC for Voronoi-based Multi-agent System Developmet” IFAC- Vol. 53, Issue 2, pp. 6969-6974 2020.
  9. Nejabat, and A. Nikoofard. “Switched Robust Model Predictive Based Controller for UAV Swarm System”, IEEE, 29th Iranian Conference on Electrical Engineering - (ICEE), 2021.
  10. Song., K. Zhu., G. Guoliang. and J. Wang “Distributed MPC-Based adaptive control for linear systems with unknown parameters” Engineering and Applied Mathematics, Journal of the Franklin Institude, 2019.
  11. Oliveri, C, Gianoglio, E, Ragusa. and M., Storace. “Low-complexity digital architecture fpr solving the point location problem in explicit model predictive control” Engineering and Applied Mathematics, Journal of the Franklin Institude, 2015.
  12. Emami, and A. Banazadeh., “Nonlinear fault-tolerant trajectory tracking control of a quadrotor UAV” International Journal of Modeling and Optimization, Vol. 9, No.5, 2019.
  13. Emami, and A. Banazadeh., “Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances” International Journal of Robust and Nonlinear Control, Vol. 20, No.6, pp. 2259-2264, 2019.
  14. P and Mayne. D., “Getting robustness against unstructured uncertainty: A tube based MPC approach” IEEE Transaction On Automatic Control, Vol.59, No.5., 2014.
  15. Y., Wang. M, and Libin. M., “Stabilization of a class of nonlinear parametrized systems by a flexible switching adaptive backstepping” Journal of The Franklin Institute, Vol.356, pp: 1-17, 2019.
  16. A. and Arefi. M.M. “Robust finite-time stabilizers for five-degree-of-freedom active magnetic bearing system” Journal of The Franklin Institute, Vol.356, pp:80-102, 2019
  17. Chamseddine, , Li, T., Zhang, Y., Rabbath, C., and Theilliol, D. “Flatness- based trajectory planning/replanning for a quadrotor unmanned aerial vehicle.” IEEE Transaction on Aerospace and Electronic Systems, pp. 2832-2848, 2012.
  18. Xu, Y., Yang, H., and Jiang, B. “Fault tolerant time optimization of switched systems with application to multi-agent flight control” International Journal of Control, Automation and Systems, pp. 1-11, 2019.
  19. Ni, W., and Cheng, D. “Leader-following consensus of multi-agent system under fixed and switching topologies” System and Control Letters, pp.209- 217, 2010.
  20. Z., Zhang, Y., Qu, Y., Su, C., Ma, Y., and Jiang, b. “Decenteralized adaptive fault tracking cooperative control of multi uavs under actuator fault and directed communicated topology” 12th Asian Control Conference, ASCC, pp. 1334-1339, 2019.
  21. Dames, P., and Kumar, V. “ Autonomous localization of an unknown number of targets without data associasion” IEEE Transactions on Automation Science, Vol. 3, 2015.
  22. Zhao, T. Hiong Go “Quadcopter formation flight control combining MPC and Robust feedback linearization” Engineering and Applied Mathematics, Journal of the Franklin Institude, 2013.
  23. Biggs “Algebraic Graph Theory”, Cambridge University Press, Second edition, 1996.
  24. Nocedal, Stephen. J. Wright, “Numerical optimization”, Springer, Mathematics subject classification, Second edition, 2000.
  25. Lin., M. Wang., X. Zhou., D. Guoru., S. Mao. “Dynamic spectrum interaction of UAV flight formation communication with priority: A deep reinforcement learning approach” IEEE Transaction on Cognitive Communication and Networking, vol. 6, issue. 3, 2020.
  26. Zhang., J. Yan., P. Zhang. “Multi-UAV formation control based on a novel back-stepping approach”, IEEE Transaction on Vehicular Technology, Vol. 69, Issue. 3, 2020.
  27. Dong., Y. Li., C. Lu., G. Hu., Q. Li. “Time-Varying formation tracking for UAV swarm system with switching directed topologies” IEEE on Neural Networks and Learning Systems, Vol.30, Issue. 12, 2019.
  28. Wang, L. Han., X. Dong., Q. Li., Z. Ren., “Distributed sliding mode control for time-varying formation tracking of multi-UAV system with a dynamic leader”, Aerospace Science and Technology, Vol. 111, pp. 106-158, 2021.
  29. He., P. Bai., X. Liang., J. Zhang., W. Wang. “Feedback formation control of UAV swarm with multiple implicit leaders” Aerospace Science and Technology, Vol. 72, pp. 327-334, 2018.
  30. Fu., J. Pan., H. Wang., X. Gao., “A formation maintenance and reconstruction method of UAV swarm based on distributed control”, Aerospace Science and Technology, Vol. 104, 2020.
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