Abstract
Inspired by the exceptional flight ability of birds and insects, a bio-inspired neural adaptive flight control structure of a small unmanned aerial vehicle was presented. Eight pressure sensors were elaborately installed in the leading-edge area of the forward wing. A back propagation neural network was trained to predict the aerodynamic moment based on pressure measurements. The network model was trained, validated, and tested. An adaptive controller was designed based on a radial basis function neural network. The new adaptive laws guaranteed the boundedness of the adaptive parameters. The closed-loop stability was analyzed via Lyapunov theory. The simulation results demonstrated the robustness of the bio-inspired flight control system when subjected to measurement noise, parametric uncertainties, and external disturbance.
Highlights
With the development of science and technology, unmanned aerial vehicles (UAVs) are becoming increasingly popular in business and daily life
Severe turbulence can degrade the flight safety of small unmanned aerial vehicles (SUAVs) in complex urban environments, which leads to their limited application
SUAVs, birds, and insects largely fly at low Reynolds number, where nonlinearity and separation occur [10]
Summary
With the development of science and technology, unmanned aerial vehicles (UAVs) are becoming increasingly popular in business and daily life. Severe turbulence can degrade the flight safety of SUAVs in complex urban environments, which leads to their limited application. SUAVs, birds, and insects largely fly at low Reynolds number, where nonlinearity and separation occur [10]. In order to enhance the flight stability of flying vehicles under parametric uncertainties and external disturbances, scholars and researchers have proposed numerous model-based control strategies [19,20]. A bio-inspired flight control framework is studied, where pressure sensors are integrated into the framework. The rest of the paper is organized as follows: in Section 2, the configuration of the pressure sensors of the test-model SUAV is presented, a back propagation neural network (BP NN) model is trained, validated, and tested, and a modified control-oriented model is proposed.
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