Abstract
The flapping wing micro-air vehicle (FWMAV) is a new type of aerial vehicle which uses mechanical structure to simulate bird flight. The FWMAV possesses the characteristics of small size, light weight, high flight efficiency and no occupation of runway. Firstly, we calculate the aerodynamic lift and drag on the wing surface. Then, through the analysis and summary of the existing FWMAV control technology, the attitude control model of the aerial vehicle is obtained. Besides, the control system based on STM32F103 is designed, which is equipped with a communication module and thus controls the position and flight attitude through the control signals received wirelessly. Finally, the feasibility of the system is verified by MATLAB and Simulink simulation.
L. Jin—This work was supported by the National Natural Science Foundation of China (No. 61703189), by CAS Light of West China Program, by the Team Project of Natural Science Foundation of Qinghai Province, China (No. 2020-ZJ-903), by the National Key Research and Development Program of China (No. 2017YFE0118900), in part by the Sichuan Science and Technology Program (No. 19YYJC1656), in part by the Fundamental Research Funds for the Central Universities (No. lzujbky-2019-89).
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References
Mackenzie, D.: A flapping of wings. Science 335(6075), 1430–1433 (2012)
Deng, X., Schenato, L., Wu, W., Sastry, S.S.: Flapping flight for biomimetic robotic insects: part I-system modeling. IEEE Trans. Robot. 22(4), 776–788 (2006)
Lee, J., Choi, H., Kim, Y.: A scaling law for the lift of hovering insects. J. Fluid Mech. 782(10), 479–490 (2015)
Gerdes, J., Gupta, S., Wilkerson, S.: A review of bird-inspired flapping wing miniature air vehicle designs. J. Mech. Robot. 4(2), 103–114 (2012)
Ellington, C.P.: The novel aerodynamics of insect flight: applications to micro-air vehicles. J. Exp. Biol. 202(23), 3439–3448 (1999)
He, W., Yan, Z., Sun, C., Chen, Y.: Adaptive neural network control of a flapping wing micro aerial vehicle with disturbance observer. IEEE Trans. Cybern. 47(10), 3452–3465 (2017)
He, W., Meng, T., He, X.: Iterative learning control for a flapping wing micro aerial vehicle under distributed disturbances. IEEE Trans. Cybern. 49(4), 1524–1535 (2019)
Li, S., Zhang, Y., Jin, L.: Kinematic control of redundant manipulators using neural networks. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2243–2254 (2017)
Jin, L., Li, S., Yu, J., He, J.: Robot manipulator control using neural networks: a survey. Neurocomputing 285(12), 23–34 (2018)
Li, D., Chen, X., Xu, Z.: Gain adaptive sliding mode controller for flight attitude control of MAV. Opt. Precis. Eng. 21(5), 1183–1191 (2013)
Jin, L., Zhang, Y., Qiao, T., Tan, M., Zhang, Y.: Tracking control of modified Lorenz nonlinear system using ZG neural dynamics with additive input or mixed inputs. Neurocomputing 196, 82–94 (2016)
Jin, L., Yan, J., Du, X., Xiao, X., Fu, D.: RNN for solving time-variant generalized sylvester equation with applications to robots and acoustic source localization. IEEE Trans. Industr. Inform. 16(99), 6359–6369 (2020)
Zhang, J., Jin, L., Cheng, L.: RNN for perturbed manipulability optimization of manipulators based on a distributed scheme: a game-theoretic perspective. IEEE Trans. Neural Netw. 1–11 (2020)
Luo, X., Sun, J., Wang, Z., Li, S., Shang, M.: Symmetric and non-negative latent factor models for undirected, high dimensional and sparse networks in industrial applications. IEEE Trans. Industr. Inform. 13, 3098–3107 (2017)
Luo, X., et al.: Incorporation of efficient second-order solvers into latent factor models for accurate prediction of missing QoS data. IEEE Trans. Cybern. 48(4), 1216–1228 (2018)
Wu, J., Sun, M.: Unsteady aerodynamic forces of a flapping wing. J. Exp. Biol. 207(7), 1137–1150 (2004)
Zou, S., Gao, A., Shi, Y., Wu, J.: Causal mechanism behind the stall delay by airfoils pitching-up motion. Theor. Appl. Mech. Lett. 7(5), 311–315 (2017)
Carr, Z., DeVoria, A., Ringuette, M.: Aspect-ratio effects on rotating wings: circulation and forces. J. Fluid Mech. 767(10), 497–525 (2015)
Schenato, L.: Analysis and control of flapping flight: from biological to robotic insects. Ph.D. dissertation, UC Berkeley (2003)
He, W., et al.: Development of an autonomous flapping-wing aerial vehicle. Sci. China Inf. Sci. 60(6), 1–8 (2017). https://doi.org/10.1007/s11432-017-9077-1
Kwan, C., Xu, H., Lewi, F.: Robust spacecraft attitude control using adaptive fuzzy logic. Int. J. Syst. Sci. 31(10), 1217–1225 (2000)
Li, H., Tong, S.: A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems. IEEE Trans. Fuzzy Syst. 11(1), 24–34 (2003)
Yu, W.: Adaptive fuzzy PID control for nonlinear systems with H\(\infty \) tracking performance. IEEE International Conference on Fuzzy System, BC, Canada, pp. 1010–1015 (2006)
Duan, H.: Flight attitude control of MAV. Ph.D. dissertation, pp. 1–134 (2007)
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Ma, D., Jin, L., Fu, D., Xiao, X., Liu, M. (2020). On Position and Attitude Control of Flapping Wing Micro-aerial Vehicle. In: Han, M., Qin, S., Zhang, N. (eds) Advances in Neural Networks – ISNN 2020. ISNN 2020. Lecture Notes in Computer Science(), vol 12557. Springer, Cham. https://doi.org/10.1007/978-3-030-64221-1_18
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