Modeling and prediction of powered parafoil unmanned aerial vehicle throttle and servo controls through artificial neural networks

Author:

Kumar Prashant1ORCID,Choudhury Bisheswar2,Singh Amandeep2,Ramkumar Janakarajan2,Philip Deepu3,Ghosh Ajoy Kanti1

Affiliation:

1. Department of Aerospace Engineering, IIT Kanpur, Uttar Pradesh 208016, India

2. Department of Mechanical Engineering, IIT Kanpur, Uttar Pradesh 208016, India

3. Department of Industrial and Management Engineering, IIT Kanpur, Uttar Pradesh 208016, India

Abstract

This study proposes a framework for developing a realistic model for throttle and servo control algorithms for a powered parafoil unmanned aerial vehicle (PPUAV) using artificial neural networks (ANNs). Two servo motors on an L-shaped platform, control and steer the PPUAV. Six degrees of freedom mathematical model of a dynamic parafoil system is built to test the technique's efficacy using a simulation in which disturbances mimic actual flights. A guiding law is then established, including the cross-track error and the line-of-sight approach. Furthermore, a path-following controller is constructed using the proportional-integral derivative, and a simulation platform was created to evaluate numerical data illustrating the route's validity following the technique. PPUAV was developed, built, and instrumented to collect real-time flight data to test the controller. These dynamic characteristics were sent into the ANN for training. A diverging-converging design was identified to obtain the best consistency between predicted and observed throttle and servo control values. For a comparable flight route, the control signal of the simulated model is compared with those of the actual and ANN-predicted models. The comparative findings show that the ANN-predicted and actual control inputs were almost identical, with an 80%–99% match. However, the simulated response showed deviation from the actual control input, with an accuracy of 50%–80%.

Publisher

Canadian Science Publishing

Subject

Control and Optimization,Electrical and Electronic Engineering,Control and Systems Engineering,Automotive Engineering,Aerospace Engineering,Computer Science Applications

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