Carrier rocket load shedding control method based on inverse reinforcement learning

A launch vehicle and reinforcement learning technology, applied in the aerospace field, can solve the problems of inability to guarantee the guidance accuracy, relying on accurate wind field information, etc., and achieve good load shedding control effect, broad popularization and application value, and good manufacturability.

Pending Publication Date: 2021-10-15
BEIHANG UNIV
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the above problems, and propose a load reduction control method based on inverse reinforcement learning, that is, a load reduction control method for the ascent of a launch vehicle, through inverse reinforcement learning load reduction index deduction and load reduction control stra

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Carrier rocket load shedding control method based on inverse reinforcement learning
  • Carrier rocket load shedding control method based on inverse reinforcement learning
  • Carrier rocket load shedding control method based on inverse reinforcement learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0035] The present invention will be described in further detail below in conjunction with accompanying drawing and embodiment example;

[0036] The present invention is a carrier rocket load reduction control method based on inverse reinforcement learning, that is, an aircraft path point tracking and guidance method, and its flow chart is as follows figure 1 As shown, it includes the following steps:

[0037] Step 1. Model establishment;

[0038] According to the assumption of a flat earth, combined with the relevant coordinate system, the in-plane dynamics model of the launch vehicle is established according to the geometric and mechanical relations between the state quantities, and the expression is as follows:

[0039]

[0040] Where r is the position vector from the launch point to the rocket center of mass, is the pitch angle of the launch vehicle, m is the mass of the launch vehicle, and J is the pitch axis inertia of the launch vehicle; F ae , F prop , F g , M...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a carrier rocket load shedding control method based on inverse reinforcement learning. The carrier rocket load shedding control method comprises the following specific steps: 1, establishing a carrier rocket dynamic model considering a wind field condition; 2, generating a passive load shedding expert demonstration; 3, training an inverse reinforcement learning load shedding control strategy; and 4, migrating the load shedding controller, that is, load shedding control strategy network parameters obtained through training are solidified, a closed loop is achieved through input and output interfaces of carrier rocket dynamics, and the load shedding controller serves as the load shedding controller. Through the above steps, the method can achieve the load shedding control of the carrier rocket, solves a problem that the existing method depends on precise wind field information and cannot guarantee the guidance precision, and achieves better stability and universality. The guidance control method is scientific and good in manufacturability and has wide application and popularization value.

Description

technical field [0001] The invention provides a carrier rocket load reduction control method based on inverse reinforcement learning, which is a guidance control method for autonomously adjusting the attitude of the carrier rocket ascending section in a dense atmosphere to reduce the aerodynamic load, applicable to general carrier rockets, and belongs to aviation Aerospace; Guidance, navigation and control technology; Reinforcement learning control field; Background technique [0002] During the ascent flight of the launch vehicle, the rocket body flying at high speed interacts with the airflow, causing the rocket body to be subjected to aerodynamic force and aerodynamic moment, which is called aerodynamic load. Moment balance, thereby generating internal force bending moment in the rocket body; due to the high slenderness ratio of the launch vehicle, the above internal force bending moment is likely to cause instability or even destruction of the launch vehicle structure; ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 李惠峰何林坤张冉
Owner BEIHANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products