A Real-Time Reentry Guidance Method for Hypersonic Vehicles Based on a Time2vec and Transformer Network

Abstract

In this paper, a real-time reentry guidance law for hypersonic vehicles is presented to accomplish rapid, high-precision, robust, and reliable reentry flights by leveraging the Time to Vector (Time2vec) and transformer networks. First, referring to the traditional predictor–corrector algorithm and quasi-equilibrium glide condition (QEGC), the reentry guidance issue is described as a univariate root-finding problem based on bank angle. Second, considering that reentry guidance is a sequential decision-making process, and its data has inherent characteristics in time series, so the Time2vec and transformer networks are trained to obtain the mapping relation between the flight states and bank angles, and the inputs and outputs are specially designed to guarantee that the constraints can be well satisfied. Based on the Time2vec and transformer-based bank angle predictor, an efficient and precise reentry guidance approach is proposed to realize on-line trajectory planning. Simulations and analysis are carried out through comparison with the traditional predictor-corrector algorithm, and the results manifest that the developed Time2vec and transformer-based reentry guidance algorithm has remarkable improvements in accuracy and efficiency under initial state errors and aerodynamic parameter perturbations.

Reentry trajectory design of a hypersonic vehicle based on reinforcement learning

“…Under the simplifying assumption of a point symmetric spherical Earth rotating with a constant angular velocity, the reentry dynamics equations of motion for a point mass vehicle are [ where, 𝐶 𝐿 = 𝐶 𝐿0 + 𝐶 𝐿1 𝛼 + 𝐶 𝐿2 𝑒 𝐶 𝐿3 𝑀 and 𝐶 𝐷 = 𝐶 𝐷0 + 𝐶 𝐷1 𝛼 2 + 𝐶 𝐷2 𝑒 𝐶 𝐷3 𝑀 are lift and drag coefficients, and the coefficients 𝐶 𝐿0 , 𝐶 𝐿1 , 𝐶 𝐿2 , 𝐶 𝐿3 , 𝐶 𝐷0 , 𝐶 𝐷1 , 𝐶 𝐷2 , and 𝐶 𝐷3 are adopted following the vehicle model of a special class of high performance common aero vehicle (CAV-H) developed by Lockheed-Martin [18]. 𝑀 is the Mach value of the vehicle.…”

Deep Learning-Based Time Series Forecasting Models Evaluation for the Forecast of Chlorophyll a and Dissolved Oxygen in the Mar Menor

“…This architecture is changing the paradigm of artificial intelligence. • Time2Vec: Its authors acknowledge that the ultimate goal was to develop a generalpurpose model-agnostic representation for time [48], which can potentially be used in any architecture [49]. It is not created as a new model for time series analyses but rather aims to provide a representation of time in the form of a vector embedded in order to automate the feature engineering process and model time in a better way.…”