A hydrogen-powered unmanned aerial vehicle control management system and method based on deep learning

The hydrogen-powered drone control and management system, powered by deep learning, can perceive changes in hydrogen storage status in real time and dynamically optimize rotor load distribution. This solves the problem of unbalanced rotor load caused by changes in hydrogen storage status, improves the drone's energy utilization efficiency and control precision, and extends component lifespan.

CN122449901APending Publication Date: 2026-07-24HYDROPENG TECHNOLOGY (BEIJING) CO LTD
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HYDROPENG TECHNOLOGY (BEIJING) CO LTD
Filing Date
2026-04-16
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing hydrogen-powered drones lack a dynamic allocation mechanism when the hydrogen storage status changes, resulting in unbalanced rotor load, reduced control precision, and energy waste, making it difficult to operate stably for long periods of time.

Method used

By using a deep learning-based control and management system, real-time data from the hydrogen storage system is acquired, a centroid offset matrix is ​​constructed, the control efficiency matrix is ​​dynamically corrected, and the rotor load is optimized through a thrust power joint allocation network. Iterative judgment and weighted generalized inverse matrix algorithms are used to ensure load balance.

Benefits of technology

It improves the energy utilization efficiency and control precision of hydrogen-powered drones throughout their entire life cycle, extends the life of key components, and enhances flight stability and reliability.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122449901A_ABST
    Figure CN122449901A_ABST
Patent Text Reader

Abstract

The application relates to the technical field of hydrogen-powered unmanned aerial vehicle management, and particularly discloses a hydrogen-powered unmanned aerial vehicle control management system and method based on deep learning, which obtains hydrogen storage system pressure and temperature data, converts the hydrogen storage system pressure and temperature data into residual hydrogen mass based on a gas state equation, fuses pre-stored installation center coordinates to generate a center of mass offset matrix to correct an initial control efficiency matrix, and constructs a dynamic efficiency matrix. The expected instruction and the dynamic efficiency input thrust power are jointly distributed in a network, and thrust distribution weights and fuel cell power are extracted. If the expected load standard deviation exceeds a threshold value and does not reach an iteration upper limit, the hidden layer balance weight is adjusted, and then remapping is performed; otherwise, a weighted generalized inverse is called to calculate a temporary thrust distribution. Finally, rotation speed and voltage instructions are converted and issued, so that precise and robust center of mass adaptive thrust balance control is realized.
Need to check novelty before this filing date? Find Prior Art