Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Intelligent control method for vertical recovery of carrier rockets based on deep reinforcement learning

A launch vehicle, reinforcement learning technology, applied in attitude control, adaptive control, general control system and other directions, can solve the problems of launch vehicle orbit planning and control dependence, loss and so on

Active Publication Date: 2019-02-15
BEIJING AEROSPACE AUTOMATIC CONTROL RES INST +1
View PDF6 Cites 32 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Under such a major premise, the trajectory planning and control of the launch vehicle is very dependent on manpower, and any human error may cause huge losses

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
  • Intelligent control method for vertical recovery of carrier rockets based on deep reinforcement learning
  • Intelligent control method for vertical recovery of carrier rockets based on deep reinforcement learning
  • Intelligent control method for vertical recovery of carrier rockets based on deep reinforcement learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0110] The invention proposes an intelligent control method for vertical recovery of a launch vehicle based on deep reinforcement learning, and studies a method for realizing autonomous intelligent control of a launch vehicle. The main research is to solve the problem of vertical recovery attitude control and trajectory planning of launch vehicle by using intelligent control.

[0111] The present invention first establishes the vertical recovery simulation model of the launch vehicle, and establishes the corresponding Markov decision-making process, including the state space, action space, state transition equation, and reward function, and adopts a deep reinforcement learning algorithm based on the strategy gradient, and at the same time learns from Alphago's " Decision-making network + valuation network" design idea, design the decision-making network of spacecraft and the valuation network for evaluating decision-making behavior. The decision-making network guides the space...

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

An intelligent control method for vertical recovery of carrier rockets based on deep reinforcement learning is disclosed. A method of autonomous intelligent control for carrier rockets is studied. Theinvention mainly studies how to realize attitude control and path planning for vertical recovery of carrier rockets by using intelligent control. For the aerospace industry, the autonomous intellectualization of spacecrafts is undoubtedly of great significance whether in the saving of labor cost or in the reduction of human errors. A carrier rocket vertical recovery simulation model is established, and a corresponding Markov decision-making process, including a state space, an action space, a state transition equation and a return function, is established. The mapping relationship between environment and agent behavior is fitted by using a neural network, and the neural network is trained so that a carrier rocket can be recovered autonomously and controllably by using the trained neural network. The project not only can provide technical support for the spacecraft orbit intelligent planning technology, but also can provide a simulation and verification platform for attack-defense confrontation between spacecrafts based on deep reinforcement learning.

Description

technical field [0001] The invention relates to a vertical recovery control method of a carrier rocket based on deep reinforcement learning, which is applicable to the field of carrier rocket guidance and control. Background technique [0002] The standard reinforcement learning framework is that an agent continuously interacts with its environment in discrete time. It mainly consists of four elements: reward and punishment feedback function, value function, strategy selection, and interactive environment. Such as figure 2 shown. [0003] The process of the agent interacting with the environment is as follows: (1) The agent (Agent) perceives the current state of the environment (state); (2) According to the current state and the reward value (reward), the agent selects an action (action) and executes it. The action; (3) When the action selected by the agent acts on the environment, the environment is transferred to a new state and a new reward is given; (4) The agent calcu...

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): G05B13/02G05B13/04G05D1/08G05D1/10
CPCG05B13/027G05B13/042G05D1/10
Inventor 郜诗佳谭浪王德意柳嘉润李博睿巩庆海杨业姬晓琴翟雯婧
Owner BEIJING AEROSPACE AUTOMATIC CONTROL RES INST
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products