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

Unmanned aerial surveying and mapping vehicle posture control method based on RBF neural network

A neural network and control method technology, applied in the field of attitude control of surveying and mapping unmanned aerial vehicles based on RBF neural network, can solve the problem that fixed parameter control cannot meet design requirements, control accuracy and rapidity are difficult to meet, environmental adaptability and anti-interference ability. limited and other problems, to achieve good control effect, strong system tracking ability and anti-interference ability, and the effect of highlighting substantive characteristics

Inactive Publication Date: 2018-01-19
QILU UNIV OF TECH
View PDF5 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the attitude control system of the surveying and mapping UAV is a multivariable, nonlinear and time-varying complex system, which makes the conventional fixed parameter control unable to meet the design requirements, and the traditional PID control method has limited environmental adaptability and anti-interference ability, and the control accuracy is limited. Indicators such as speed and speed are difficult to meet the growing demand for control

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
  • Unmanned aerial surveying and mapping vehicle posture control method based on RBF neural network
  • Unmanned aerial surveying and mapping vehicle posture control method based on RBF neural network
  • Unmanned aerial surveying and mapping vehicle posture control method based on RBF neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] The present invention will be described in detail below in conjunction with the accompanying drawings and through specific implementation. The following examples are explanations of the present invention, but the present invention is not limited to the following embodiments.

[0046] Such as figure 1 and 2 As shown, a kind of surveying and mapping unmanned aerial vehicle attitude control method based on RBF neural network provided by the present invention is characterized in that, comprises the following steps:

[0047] S1: Establish a nonlinear dynamic model of the controlled object aircraft;

[0048] S2: Design a PID controller for surveying and mapping drones for nonlinear models;

[0049] S3: Using the nonlinear mapping ability of the neural network to obtain the controller parameter adjustment variation;

[0050] S4: The RBF neural network method is combined with the PID control method to obtain an adaptive PID control method based on the RBF neural network, whi...

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 relates to an unmanned aerial surveying and mapping vehicle posture control method based on an RBF neural network. The control method is characterized by comprising the following steps of creating a non-linear dynamic model of an aerial vehicle serving as a controlled object; designing an unmanned aerial surveying and mapping vehicle PID controller according to the non-linear model;utilizing the non-linear mapping capability of the neural network to obtain the parameter adjustment variable quantity of the controller; combining an RBF neural network method with a PID control method to obtain a self-adapting PID control method based on the RBF neural network, and conducting simulation on the non-linear model of the unmanned aerial surveying and mapping vehicle.

Description

technical field [0001] The invention belongs to the field of surveying and mapping unmanned aerial vehicle control, in particular to an RBF neural network-based attitude control method for surveying and mapping unmanned aerial vehicles. Background technique [0002] With the continuous development of aerospace technology, space vehicles have been more and more widely used in many fields such as communication, disaster monitoring, resource exploration, navigation and positioning, scientific research, and military affairs. The attitude control system of the aircraft is an important part of the control of the aircraft. It is related to whether the aircraft after entering orbit can maintain a predetermined orientation or orientation with a certain accuracy with respect to the gravitational center body, inertial system, and other reference systems. Therefore, it is of great practical significance to control the attitude of the aircraft correctly and in real time. [0003] UAV is...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
Inventor 胡长琪朱树云马凤英魏同发付承彩
Owner QILU UNIV OF TECH
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