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

Identification method of robot motor system based on Kalman filtering through calibration of quasi model

A technology of Kalman filtering and robot movement, applied in general control systems, control/adjustment systems, instruments, etc., can solve the problems of insufficient fitting and linearization, achieve convenient parameter adjustment, wide range of Q value selection, and identification good effect

Active Publication Date: 2018-02-16
SHANGHAI UNIVERSITY OF ELECTRIC POWER
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] There are many traditional model identification methods. Common motor model identification methods include least squares method, Kalman filter algorithm, maximum likelihood method, model reference adaptive method and artificial neural network method, etc., but traditional Kalman filter It has deficiencies in fit and linearization

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
  • Identification method of robot motor system based on Kalman filtering through calibration of quasi model
  • Identification method of robot motor system based on Kalman filtering through calibration of quasi model
  • Identification method of robot motor system based on Kalman filtering through calibration of quasi model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0047] figure 1 Shown here is a schematic diagram of the identification platform structure of the inspection robot motion system of this embodiment. The inspection robot control platform consists of a remote controller with STM32F407ARM as the main control chip, a PID main controller with STM32F103ARM as the core, a 2.4G wireless communication module, and a motor driver Module, Hall speed sensor, composite filter, etc.

[0048] During normal operation, the robot is in automatic inspection mode without manual intervention. The handheld remote controller is used to manually remotely control the robot to perform corresponding actions when an abnormal situation occurs. The main controller is used to receive instructions from the remote control, and obtain speed information by decoding the instructions from the remote control. And the given speed information is calculated by PID algorithm to obtain the PWM wave signal of the corresponding duty cycle, and the PWM wave signal is outpu...

Embodiment 2

[0092] This embodiment compares the identification effects of Kalman filtering with quasi-model calibration and non-quasi-model calibration at the same set wheel speed.

[0093] Using the same set of robot wheel speed sampling data, the kalman filter with quasi-model calibration and the Kalman filter with inaccurate model are used to identify the motion system. The identification effect is compared and verified by the identification curve graph and identification related parameters, and finally the comparison effect in the two cases is obtained. Since the quasi-model is integrated in the Kalman filter algorithm, the Q value of the system noise parameter can be appropriately reduced, and the value of Q is 0.015 in this embodiment. When the robot wheel speed is set to 60r / min, a set of wheel speed adjustment data is collected, and the quasi-model of the least square fitting is substituted into the Kalman filter to draw a fitting curve, such as image 3 As shown, "*90ms" in parenthe...

Embodiment 3

[0103] This embodiment compares the robustness of Kalman filter identification with quasi-model calibration and non-quasi-model calibration, uses different Q values ​​for identification, and creates an identification curve and a key parameter quantitative analysis table.

[0104] Take a sample of the robot wheel speed with a rotation speed of 60r / min, Figure 7-9 Shown are the identification curves of using the quasi-model calibration Kalman filter to identify the set of samples when Q takes different values. Table 2 shows the corresponding identification key quantities. Such as Figure 10-12 As shown, when Q takes different values, the non-standard model calibration Kalman filter identifies the identification curve of the set of samples. Table 3 is a list of the corresponding identification key quantities.

[0105] Since there is no accurate model of Kalman filtering, a large number of sample points are needed for calibration. In order to improve the calibration efficiency, a large...

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 present invention relates to an identification method of a robot motor system based on Kalman filtering through calibration of a quasi model. The method comprises the following steps of: S1, performing identification through the least square method to obtain a system transfer function quasi model by taking a robot wheel speed collection data as an input and taking a robot wheel speed set valueas an output; S2, converting the system transfer function quasi model to a system state equation; and S3, obtaining an identification model comprising a Kalman filtering state equation and a measurement equation of the system through discretization. Compared to an identification method of a Kalman filtering system without a quasi model, the identification method provided by the invention is easyto select parameters, good in degree of fitting and high in robustness, etc.

Description

Technical field [0001] The invention relates to the field of intelligent inspection robot system model identification, in particular to a robot motion system identification method based on quasi-model calibration Kalman filter. Background technique [0002] It can be seen from the development trend of the industry that inspection robots will occupy a larger market in various fields in the future, and they will be widely used in substations, campuses, factories, military industries, ships and other places. To solve the problems related to the inconvenience of wheel speed control and debugging during the research and development process, the inspection robot first uses composite software and hardware filtering and incremental PID preliminary debugging, and then uses the system identification scheme to experiment with the wheel speed data output by the robot Modeling, use this model as a motion system model for further in-depth study. [0003] There are theoretical analysis methods, ...

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
CPCG05B13/042
Inventor 彭道刚戚尔江夏飞关欣蕾陈跃伟王立力赵晨洋邱正
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com