A method for predicting the overall performance of industrial robots based on feed-forward neural network

Abstract

The invention belongs to the field of performance testing of industrial robots, and specifically discloses a method for estimating the performance of a complete industrial robot based on a feedforward neural network, which includes the following steps: S1 Constructing a model framework for the performance impact of an industrial robot and performing a performance test to Obtain the corresponding input parameters and output parameters, wherein the input parameters are the performance parameters of the core components of the industrial robot and the environmental impact parameters, and the output parameters are the performance parameters of the whole machine of the industrial robot; S2 normalizes the input parameters and output parameters, and Use the normalized data to train the neural network model to obtain the whole machine performance prediction network model; S3 substitutes the performance parameters and environmental impact parameters of the core components of the industrial robot to be tested into the whole machine performance prediction network model In order to obtain the performance parameters of the industrial robot. The invention can realize the prediction of the performance of the whole industrial robot, and has the advantages of wide applicability, accurate prediction and the like.

Description

technical field [0001] The invention belongs to the field of performance testing of industrial robots, and more particularly relates to a method for predicting the overall performance of industrial robots based on a feedforward neural network. Background technique [0002] In the field of industrial robot performance testing and evaluation technology, the research method of the influence law of the overall performance of industrial robots is usually based on the conventional control variable method to do a large number of test experiments, and obtain a certain amount of experimental test data for drawing analysis, so as to obtain the specified variables. Specifies the influence law of the performance index. For example, keep other variables constant, change the motor rigidity by adjusting the differential gain and proportional gain parameters of the industrial robot joints, and use the robot performance testing equipment to test the robot end accuracy performance, and obtain...

AI Technical Summary

Problems solved by technology

In this way, on the one hand, it will greatly increase the difficulty of executing the test experiment, which is time-consuming and labor-intensive; During the experimental testing process, a large amount of useless work is added, and most of the performance index data of core components are not of the same order of magnitude. The data analysis will be seriously disturbed, and the obvious influence law cannot be accurately analyzed in the end.

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