Optimization of a Runge-Kutta 4th Order Method-based Airbrake Control System for High-Speed Vehicles Using Neural Networks

TL;DR

This paper introduces a neural network-enhanced RK4-based airbrake control system for high-speed vehicles, significantly reducing computational time while maintaining or improving control performance.

Contribution

It presents a novel integration of neural networks with RK4 methods to optimize real-time airbrake control in high-speed vehicles, addressing computational efficiency and adaptability.

Findings

Reduced computational time compared to traditional RK4 methods

Achieved comparable or better control performance

Enhanced adaptability to changing flow conditions

Abstract

The Runge-Kutta 4th Order (RK4) technique is extensively employed in the numerical solution of differential equations for airbrake control system design. However, its computational efficacy may encounter restrictions when dealing with high-speed vehicles that experience intricate aerodynamic forces. Using a Neural Network, a unique technique to improving the RK4-based airbrakes code is provided. The Neural Network is trained on numerous aspects of the high-speed vehicle as well as the current status of the airbrakes. This data was generated through the traditional RK4-based simulations and can predict the state of the airbrakes for any given state of the rocket in real-time. The proposed approach is demonstrated on a high-speed airbrakes control system, achieving comparable or better performance than the traditional RK4-based system while significantly reducing computational time by reducing the number of mathematical operations. The proposed method can adapt to changes in flow conditions and optimize the airbrakes system in real-time.