Aircraft ascending section trajectory optimization method based on neural network

Abstract

The invention provides an aircraft ascending section trajectory optimization method based on a neural network, which is used for solving the technical problems of poor real-time performance and adaptability in the prior art, and comprises the following steps: 1, establishing an aircraft ascending section continuous optimal control problem under a launching inertial system; 2, acquiring a boundary value problem of two continuous points of the vacuum flight section of the aircraft under the launching inertial coordinate system; 3, obtaining nominal parameters and non-nominal parameters of the aircraft; 4, performing off-line solving on the continuous optimal control problem of the rising section of the aircraft under the launching inertial coordinate system; 5, performing off-line solving on the boundary value problem of two continuous points of the vacuum flight section of the aircraft under the launching inertial coordinate system; 6, constructing a neural network and carrying out offline training on the neural network; and 7, obtaining a trajectory optimization result of the rising section of the aircraft on line.

Description

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AI Technical Summary

Benefits of technology

This patented technology uses an artificial intelligence system called Neural Network (NN) that helps optimize airplanes during their flying phase without affecting its overall performance or even causing damage from external factors like weather changes over time. It achieves this through learning patterns on data collected at various points along with parameters such as altitude, speed, fuel level, etc., while also optimizing for smaller variations within these ranges. By adjusting certain variables based upon specific needs, it becomes more accurate than previous methods.

Problems solved by technology

This patents describes different techniques related to improving the performance of an airborne mission (AS) projectile rocket). One technique called assisted particle transfer (APT), focuses on achieving higher levels of acceleration without compromising stability under certain conditions like altitude. Other techniques involve solving linear systems by combining various mathematical models together, including differential calculus, partial derivative analysis, and advanced stochastic approximation algorithms. These techniques aimed at reducing complexity while maintaining accurate tracking data.

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