YAW IDENTIFICATION FOR ELECTRIC DIRECT-DRIVE COAXIAL UNMANNED HELICOPTERS BASED ON AN IMPROVED Q-LEARNING

Lin-Jie Huang,∗ Zi-Huan Cheng,∗ Hai-Long Pei,∗ Ding Xu,∗ and Zhi-Hao Xu∗∗

References

  1. [1] H. Qi, G. Xu, C. Lu, and Y. Shi, A study of coaxial rotoraerodynamic interaction mechanism in hover with high-efficienttrim model, Aerospace Science and Technology, 84, 2019,1116–1130.
  2. [2] S. Darvishpoor, J. Roshanian, A. Raissi, and M. Hassana-lian, Configurations, flight mechanisms, and applications ofunmanned aerial systems: A review, Progress in AerospaceSciences, 121, 2020, 100694.
  3. [3] Y. Zhang, B. Xu, C. Xiang, L. Liu, and T. Ai, Modellingand controller design of a novel ducted fan unmanned aircraft,International Journal of Robotics and Automation, 36(6), 2021,448–461.
  4. [4] R. Celi, HeliUM 2 Flight dynamic simulation model: develop-ment, technical concepts, and applications, in Proceedings ofthe 71st Annual Forum of the American Helicopter Society,Virginia Beach, VA, 2015, 1.
  5. [5] R. Chen, Y. Yuan, and D. Thomson, A review of mathematicalmodelling techniques for advanced rotorcraft configurations,Progress in Aerospace Sciences, 120, 2021, 100681.
  6. [6] K. Li, B. Li, and D. Li, Numerical simulation of mechanicalperformances and out of control Fora rotor UAV, InternationalJournal of Robotics and Automation, 36, 2021, 462–470.
  7. [7] I. Aliskan, Optimized inverse nonlinear function-basedwiener model predictive control for nonlinear systems,Arabian Journal for Science and Engineering, 46(10), 2021,10217–10230.
  8. [8] A. Golabi, N. Meskin, and J. Mohammadpour, A Bayesianapproach for LPV model identification and its application tocomplex processes, IEEE Transactions on Control SystemsTechnology, 25(6), 2017, 1–8.
  9. [9] A. K. Samanta, A. R., Swanand, R. Khare, and A. Naha,Direct estimation of multiple time-varying frequencies of non-stationary signals, Signal Processing, 169, 2020, 107384.
  10. [10] N. Xu and F. Ding, Parameter estimation for a class oftime-varying systems with the invariant matrix, InternationalJournal of Robust and Nonlinear Control, 33(3), 2023,2163–2181.
  11. [11] X. Zhang, S. Gao, C. Chen, and J. Huang, Optimal controlalgorithm for stochastic systems with parameter drift, Sensors,23(12), 2023, 5743.
  12. [12] J. Teng, Y. An, and L. Wang, Time-optimal control problemfor a linear parameter varying system with nonlinear item,Journal of the Franklin Institute-Engineering and AppliedMathematical, 359(2), 2022, 859–869.
  13. [13] F. Song, L. Li, Y. Liu, and Y. Dong, Singular valuedecomposition based learning identification for linear time-varying systems: from recursion to iteration, InternationalJournal of Robust and Nonlinear Control, 33(12), 2023,6986–7003.
  14. [14] H. Zhou, G. Yan, and X. Sun, Adaptive incrementalKalman predictor with unknown time-varying parameters, inProceedings of 2016 International Conference on AdvancedRobotics and Mechatronics, Kunming, 2016, 4789–4794.
  15. [15] M. Gilson, What has instrumental variable method to offerfor system identification, IFAC Papers Online, 48(1), 2015,354–159.
  16. [16] H. Xia, S. Xu, X. Miao, and J. Cao, Instrumental variable-basedmulti-innovation gradient estimation for nonlinear systemswith scarce measurements, Optimal Control Applications andMethods, 44(1), 2023, 243–258.
  17. [17] J. M. Bravo, A. Suarez, M. Vasallo, and T. Alamo, Slidewindow bounded-error time-varying systems identification,IEEE Transactions on Automatic Control, 61(8), 2016,2282–2287.
  18. [18] L. Huang, H. Pei, and Z. Cheng, System identifica-tion and improved internal model control for yaw ofunmanned helicopter, Asian Journal of Control, 25(2), 2022,1619–1638.12
  19. [19] N. Xu,and F. Ding, Recursive estimation algorithms basedon the least squares and their convergence for a class oftime-varying systems, Nonlinear Dynamics, 111(19), 2023,18191–18213.
  20. [20] Z. Liu, H. Ji, H. Pei, and F.L. Lewis, A new information-weighted recursive algorithm for time-varying systems: Appli-cation to UAV system identification, International Journal ofSystems Science, 61(8), 2016, 2282–2287.
  21. [21] M.B. Tischler and R.K. Remple, Aircraft and rotorcraftsystem identification engineering methods with testexamples, Blacksburg Virginia: AIAA, 25(2), 2012,466–467.
  22. [22] Z. Wang, Vertical-yaw identification of a new configurationof variable-speed coaxial helicopter, Helicopter Technique, 3,2023, 9-15.
  23. [23] T Berger, E.L. Tobias, M.B. Tischler, and O. Juhasz, Advancesand modern applications of frequency-domain aircraft androtorcraft system identification, Journal of Aircraft, 60(5),2023, 1331–1353.
  24. [24] X. Chen, A. N. Iyer, Z. Wang, and A.H. Qureshi, Efficient Q-learning over visit frequency maps for multi-agent explorationof unknown environments, in Proceeding IEEE InternationalConference on Intelligent Robots and Systems, Detroit, MI,2023,1893–1900.
  25. [25] V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J.Veness, M.G. Bellemare, A. Graves, M. Riedmiller, A.K.Fidjeland, G. Ostrovski, S. Petersen , Human-level controlthrough deep reinforcement learning, Nature, 518 (7504),2015, 529–533.
  26. [26] J.W. MacArthur, A new approach for nonlinear processidentification using orthonormal bases and ordinalsplines, Journal of Process Control, 22(2), 2012,375–389.
  27. [27] L. Huang and H. Pei, Design of yaw controller for a smallunmanned helicopter based on improved ADRC, GuidanceNavigation and Control, 1(4), 2021, 7–25.
  28. [28] M. Yu, Y. Wang, W. Wang, and Y. Wei, Continuous-timesubspace identification with prior information using generalizedorthonormal basis functions,Mathematics, 11(23), 2023, 4765.
  29. [29] A. Pentaliotis and M. Wiering, Variation-resistant Q-learning:Controlling and utilizing estimation bias in reinforcementlearning for better performance, in Proceedings of 13thInternational Conference on Agents and Artificial Intelligence,Los Angeles, CA, 2021, 17–28.
  30. [30] M. Yu, G. Guo, J. Liu, and L. Shang, Closed-loop time-varying continuous-time recursive subspace-based predictionvia principal angles rotation, ISA Transactions, 122, 2022,135–145.

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