Surrogate-based Optimal Multidisciplinary Takeoff Trajectory Design for Electric Drones

Abstract

Multi-Disciplinary Analysis and Optimization (MDAO) plays an important role in the future of aviation, especially relating to Urban Air Mobility (UAM). MDAO for UAM effectively quantifies and maximizes system performance and safety while minimizing cost or consumption. Electric drone takeoff trajectory design aims to minimize energy consumption while fulfilling acceleration and other constraints to keep passengers comfortable. Conventional simulation-based MDAO is computationally expensive due to repeatedly evaluating physics-based models while naïvely constructing machine learning predictive models, also known as surrogate models, suffers from the “curse of dimensionality”. A twin-generator generative adversarial networks (Twin-GAN) model is proposed to generate realistic electric drone takeoff trajectories for the first time. In this work, 1,000 optimal electric drone takeoff trajectories were generated as training data for the Twin-GAN model. The generator of GAN is composed of two networks of the same architecture, called twin generators, to predict power and wing angle profiles separately. Results revealed that the proposed Twin-GAN reduced the design space from 43 dimensions to 11 dimensions while maintaining ~99% fitting accuracy towards 100 arbitrary real optimal designs. In the meantime, the Twin-GAN model automatically reduces the design space by filtering out unrealistic takeoff trajectories. A deep neural network surrogate was constructed with the Twin-GAN variables as input and achieved >95% relative accuracy using 2,385 training samples. Surrogate modeling without using GAN could hardly collect training samples due to convergence difficulty on unrealistic takeoff trajectories. Eventually, surrogate-based optimal takeoff trajectory design achieved ~90% accuracy on the objective function (energy consumption) compared to the reference optimal trajectory.