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Identification method of robot motion system based on quasi-model calibrated Kalman filter

A Kalman filter, robot motion technology, applied in general control systems, control/regulation systems, instruments, etc., can solve problems such as lack of fit and linearization, and achieve convenient parameter adjustment, good identification effect, and good fitting. effect of effect

Active Publication Date: 2020-09-01
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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  • 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

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  • Identification method of robot motion system based on quasi-model calibrated Kalman filter
  • Identification method of robot motion system based on quasi-model calibrated Kalman filter
  • Identification method of robot motion system based on quasi-model calibrated Kalman filter

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Experimental program
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Effect test

Embodiment 1

[0047] figure 1 Shown is a schematic structural diagram of the inspection robot motion system identification platform in 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 hand-held remote control is used to manually control the robot to perform corresponding actions when an abnormal situation occurs. The main controller is used to receive instructions from the remote controller, and obtain speed information by decoding the instructions from the remote controller. And the given speed information is calculated by the PID algorithm to obtain the PWM wave signal of the corresponding duty ratio, and the PWM wave signal is output to the...

Embodiment 2

[0092] This embodiment compares the identification effects of the Kalman filter with quasi-model calibration and without 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 without quasi-model are used to identify the model of the motion system. The identification effect is compared and verified by the identification curve diagram and identification related parameters, and finally the comparison effect in the two cases is obtained. Since the quasi-model is integrated into the Kalman filtering algorithm, the Q value of the system noise parameter can be appropriately small. In this embodiment, the Q value is set to be 0.015. When the set robot wheel speed is 60r / min, a set of wheel speed adjustment data is collected, and the quasi-model fitted by least squares is substituted into the Kalman filter to draw a fitting curve, as shown in image 3 As ...

Embodiment 3

[0103] In this embodiment, the robustness of Kalman filter identification with quasi-model calibration and without quasi-model calibration is compared, different Q values ​​are used for identification, and identification curves and key parameter quantitative analysis tables are made.

[0104] Take a robot wheel speed sample with a rotation speed of 60r / min, Figure 7-9 Shown are the identification curves of the group of samples identified by the quasi-model calibration Kalman filter when Q takes different values, and Table 2 shows the corresponding identification key quantities. Such as Figure 10-12 As shown, it is the identification curve of the group of samples identified by the Kalman filter without quasi-model calibration when Q takes different values ​​respectively, and Table 3 is the list of corresponding identification key quantities.

[0105]Since the Kalman filter without an accurate model requires a large number of sample points for calibration, in order to improve...

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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 model identification of an intelligent inspection robot system, in particular to a robot motion system identification method based on a 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 large market in various fields in the future, especially in substations, campuses, factories, military industries, ships and other places. Aiming at the solution to the inconvenient debugging of wheel speed control during the research and development process, the inspection robot is firstly debugged with composite software and hardware filtering and incremental PID, and then the wheel speed data output by the robot is tested using the system identification scheme. Modeling, take this model as the motion system model for further in-depth research. [0003] The methods of establishing robot motion model include theoretica...

Claims

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Application Information

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Patent Type & Authority Patents(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
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