期刊
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
卷 8, 期 1, 页码 148-156出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2020.1003417
关键词
Flapping-wing micro aerial vehicles (FWMAVs); modeling; neural networks; trajectory tracking
资金
- National Natural Science Foundation of China [61933001, 62061160371]
- Ministry of Education of China [6141A02033 339]
- Beijing Top Discipline for Artificial Intelligent Science and Engineering, University of Science and Technology Beijing
- Joint Funds of Equipment Pre-Research of China [6141A02033 339]
This paper studies the trajectory tracking problem of flapping-wing micro aerial vehicles in the longitudinal plane and proposes an adaptive control scheme to achieve autonomous flight. By designing position and attitude controllers, the effectiveness of the control scheme is discussed and verified through simulations.
This paper studies the trajectory tracking problem of flapping-wing micro aerial vehicles (FWMAVs) in the longitudinal plane. First of all, the kinematics and dynamics of the FWMAV are established, wherein the aerodynamic force and torque generated by flapping wings and the tail wing are explicitly formulated with respect to the flapping frequency of the wings and the degree of tail wing inclination. To achieve autonomous tracking, an adaptive control scheme is proposed under the hierarchical framework. Specifically, a bounded position controller with hyperbolic tangent functions is designed to produce the desired aerodynamic force, and a pitch command is extracted from the designed position controller. Next, an adaptive attitude controller is designed to track the extracted pitch command, where a radial basis function neural network is introduced to approximate the unknown aerodynamic perturbation torque. Finally, the flapping frequency of the wings and the degree of tail wing inclination are calculated from the designed position and attitude controllers, respectively. In terms of Lyapunov's direct method, it is shown that the tracking errors are bounded and ultimately converge to a small neighborhood around the origin. Simulations are carried out to verify the effectiveness of the proposed control scheme.
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