Autopilot design for tilt-rotor unmanned aerial vehicle with nacelle mounted wing extension using single hidden layer perceptron neural network. (September 2017)

• Record Type: Journal Article
• Authors: Kang, Youngshin; Kim, Nakwan; Kim, Byoung-Soo; Tahk, Min-Jea
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 231:Number 11(2017:Nov.)
• Page Start: 1979
• Page End: 1992
• Publication Date: 2017-09
• Subjects: Single hidden layer -- perceptron -- neural network -- back-propagation -- pseudo-control hedging -- dynamic inversion -- autonomous -- waypoint -- tilt-rotor -- wing extension -- nacelle fixed auxiliary wing; Aeronautics -- Periodicals; Astronautics -- Periodicals; Airplanes -- Design and construction -- Periodicals; Aerospace industries -- Periodicals; 629.1
• DOI: 10.1177/0954410016664926
• Languages: English
• ISSNs: 0954-4100

Abstract

Single hidden layer perceptron neural network controllers combined with dynamic inversion are applied to the tilt-rotor unmanned aerial vehicle and its variant model with the nacelle mounted wing extension. The bandwidths of the inner loop and outer loop of the controller are designed using the timescale separation approach, which uses the combined analysis of the two loops. The bandwidth of each loop is selected to be close to each other using a combination of the pseudo-control-hedging and the pole-placement method. Similar to the previous studies on sigma-pi neural network, the dynamic inversion at hover conditions of the original tilt-rotor model is used as a baseline for both aircraft, and the compatible solution to the Lyapunov equation is suggested. The single hidden layer perceptron neural network minimizes the error of the inversion model through the back-propagation adaptation. The waypoint guidance is applied to the outermost loop of the neural network controller for autonomous flight which includes vertical take-off and landing as well as nacelle conversion. The simulation results under the two wind conditions for the tilt-rotor aircraft and its variant are presented. The south and north-west wind directions are simulated in order to compare with the results from the existing sigma-pi neural network, and the estimation results of the wind are presented.