[ Log In | Register]
! Skip Navigation Links
Skip Navigation Links
Image of Journal of Applied Nonlinear Dynamics
ISSN:2164-6457 (print)
ISSN:2164-6473 (online)
Journal of Applied Nonlinear Dynamics
Miguel A. F. Sanjuan (editor), Albert C.J. Luo (editor)
Miguel A. F. Sanjuan (editor)

Department of Physics, Universidad Rey Juan Carlos, 28933 Mostoles, Madrid, Spain

Email: miguel.sanjuan@urjc.es

Albert C.J. Luo (editor)

Department of Mechanical and Industrial Engineering, Southern Illinois University Ed-wardsville, IL 62026-1805, USA

Fax: +1 618 650 2555 Email: aluo@siue.edu

Optimal Attitude Synchronization for Full-State Constrained Quadrotor UAVs Based on Reinforcement Learning

Journal of Applied Nonlinear Dynamics 14(3) (2025) 523--533 | DOI:10.5890/JAND.2025.09.003

Youneng Li$^{1}$, Peng Jin$^{1}$, Guopeng Zhou$^{1,2}$, Yutao Zhu$^{1}$

$^{1}$ School of Electronics and Electrical Engineering, Wuhan Textile University, Wuhan, 430200, China

$^{2}$ Department of Research and Development, Hubei Xiangcheng Institute of Intelligent Mechatronics, Xianning, 437000, China

Download Full Text PDF

 

Abstract

This paper discusses the optimal attitude synchronization of quadrotor unmanned aerial vehicles with full-state constraints. First, a distributed observer is constructed to generate a reference trajectory for each QUAV. Then, a global unified barrier function is constructed to transform the constrained control problem into an unconstrained one. By ensuring that the states of the transformed system are bounded, the states trajectory of the QUAV are always within the constrained range. Considering the uncertainty of QUAVs model, a model-free reinforcement learning algorithm is designed to obtain the optimal attitude control law. Finally, the effectiveness of the proposed control strategy is verified by simulation experiments. The simulation results show that the proposed method can effectively realize the optimal formation without state transgression.

References

  1. [1]  Sun, Y., Xia, K., Zou, Y., Fu, Q., and He, X. (2021), Distributed output-feedback formation tracking control for clustered quadrotors, IEEE Transactions on Aerospace and Electronic Systems, 58(3), 1894-1905.
  2. [2] Peng, Z., Yang, S., Wen, G., Rahmani, A., and Yu, Y. (2016), Adaptive distributed formation control for multiple nonholonomic wheeled mobile robots, Neurocomputing, 173, 1485-1494.
  3. [3] Jin, P., Ma, Q., and Xu, S. (2023), Dynamic Event-Triggered Robust Optimal Attitude Control of QUAV Using Reinforcement Learning, IEEE Transactions on Aerospace and Electronic Systems, 59(6), 7798-7807.
  4. [4] Jiang, H., Ma, Q., and Guo, J. (2023), Fuzzy-based fixed-time attitude control of QUAV with full-state constraints: Theory and experiments, IEEE Transactions on Fuzzy Systems, 32(3), 1108-1115.
  5. [5] Liu, H., Tian, Y., Lewis, F.L., Wan, Y., and Valavanis, K.P. (2019), Robust formation tracking control for multiple quadrotors under aggressive maneuvers, Automatica, 105, 179-185.
  6. [6] Du, H., Zhu, W., Wen, G., Duan, Z., and Lü, J. (2017), Distributed formation control of multiple quadrotor aircraft based on nonsmooth consensus algorithms, IEEE Transactions on Cybernetics, 49(1), 342-353.
  7. [7] Yang, H., Cocquempot, V., and Jiang, B. (2009), Optimal fault-tolerant path-tracking control for 4WS4WD electric vehicles, IEEE Transactions on Intelligent Transportation Systems, 11(1), 237-243.
  8. [8]  Liu, H., Zhao, W., Lewis, F.L., Jiang, Z.P., and Modares, H. (2019), Attitude synchronization for multiple quadrotors using reinforcement learning, In 2019 Chinese Control Conference (CCC) (pp. 2480-2483). IEEE.
  9. [9]  Hamayun, M.T., Edwards, C., and Alwi, H. (2011), Design and analysis of an integral sliding mode fault-tolerant control scheme, IEEE Transactions on Automatic Control, 57(7), 1783-1789.
  10. [10]  Liu, D., Liu, H., Lewis, F.L., and Wan, Y. (2019). Robust fault-tolerant formation control for tail-sitters in aggressive flight mode transitions, IEEE Transactions on Industrial Informatics, 16(1), 299-308.
  11. [11] Hua, Y., Dong, X., Li, Q., and Ren, Z. (2017), Distributed fault-tolerant time-varying formation control for second-order multi-agent systems with actuator failures and directed topologies, IEEE Transactions on Circuits and Systems II: Express Briefs, 65(6), 774-778.
  12. [12] Ma, H.J., Xu, L.X., and Yang, G.H. (2019), Multiple environment integral reinforcement learning-based fault-tolerant control for affine nonlinear systems, IEEE Transactions on Cybernetics, 51(4), 1913-1928.
  13. [13] Faust, A., Palunko, I., Cruz, P., Fierro, R., and Tapia, L. (2017), Automated aerial suspended cargo delivery through reinforcement learning, Artificial Intelligence, 247, 381-398.
  14. [14]  dos Santos, S.R.B., Givigi, S.N., and Nascimento, C.L. (2015), Autonomous construction of multiple structures using learning automata: Description and experimental validation, IEEE Systems Journal, 9(4), 1376-1387.
  15. [15] Liu, Y., Zhang, H., Sun, J., and Wang, Y. (2020), Adaptive fuzzy containment control for multiagent systems with state constraints using unified transformation functions, IEEE Transactions on Fuzzy Systems, 30(1), 162-174.
  16. [16]  Cao, Y., Shen, Z., Cao, J., Li, D., and Song, Y. (2023), Prescribed time recovery from state constraint violation via approximation-free control approach, IEEE Transactions on Circuits and Systems I: Regular Papers, 70(7), 2957-2967.
  17. [17] Tee, K.P., Ge, S.S., and Tay, E.H. (2009), Barrier Lyapunov functions for the control of output-constrained nonlinear systems, Automatica, 45(4), 918-927.
  18. [18] Guo, X., Zhang, H., Sun, J., and Liu, X. (2023), Cooperative control for stochastic multiagent systems with deferred dynamic constraints via a novel universal barrier function approach, IEEE Transactions on Cybernetics, 54(3), 1806-1815.
  19. [19] Liu, Y.J., Lu, S., Tong, S., Chen, X., Chen, C.P., and Li, D.J. (2018), Adaptive control-based barrier Lyapunov functions for a class of stochastic nonlinear systems with full state constraints, Automatica, 87, 83-93.
  20. [20] Xu, J., Wang, L., Liu, Y., and Xue, H. (2022), Event-triggered optimal containment control for multi-agent systems subject to state constraints via reinforcement learning, Nonlinear Dynamics, 109(3), 1651-1670.
  21. [21] He, W., Chen, Y., and Yin, Z. (2015), Adaptive neural network control of an uncertain robot with full-state constraints, IEEE Transactions on Cybernetics, 46(3), 620-629.
  22. [22] Liu, Y.J., Li, J., Tong, S., and Chen, C.P. (2016), Neural network control-based adaptive learning design for nonlinear systems with full-state constraints, IEEE Transactions on Neural Networks and Learning Systems, 27(7), 1562-1571.
  23. [23] Lewis, F.L., Vrabie, D., and Syrmos, V.L. (2012), Optimal Control, John Wiley and Sons.
  24. [24] Zhao, W., Liu, H., and Lewis, F.L. (2020), Robust formation control for cooperative underactuated quadrotors via reinforcement learning, IEEE Transactions on Neural Networks and Learning Systems, 32(10), 4577-4587.
  25. [25]  Jiang, Y. and Jiang, Z.P. (2017), Robust Adaptive Dynamic Programming, John Wiley and Sons.

Copyright © 2011-2024 L & H Scientific Publishing. All rights reserved. Wildcard SSL Certificates