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Error prediction for large optical mirror processing robot based on deep learningJin, Zujin ...Predicting the errors of a large optical mirror processing robot (LOMPR) is very important when studying a feedforward control error compensation strategy to improve the motion accuracy of the LOMPR. ... Therefore, an end trajectory error prediction model of a LOMPR based on a Bayesian optimized long short-term memory (BO-LSTM) was established. First, the batch size, number of hidden neurons and learning rate of LSTM were optimized using a Bayesian method. Then, the established prediction models were used to predict the errors in the X and Y directions of the spiral trajectory of the LOMPR, and the prediction results were compared with those of back-propagation (BP) neural network. The experimental results show that the training time of the BO-LSTM is reduced to 21.4 % and 15.2 %, respectively, in X and Y directions than that of the BP neural network. Moreover, the MSE, RMSE, and MAE of the prediction error in the X direction were reduced to 9.4 %, 30.5 %, and 31.8 %, respectively; the MSE, RMSE, and MAE of the prediction error in the Y direction were reduced to 9.6 %, 30.8 %, and 37.8 %, respectively. It is verified that the BO-LSTM prediction model could improve not only the accuracy of the end trajectory error prediction of the LOMPR but also the prediction efficiency, which provides a research basis for improving the surface accuracy of an optical mirror.Vir: Strojniški vestnik = Journal of mechanical engineering. - ISSN 0039-2480 (Vol. 68, no. 3, Mar. 2022, str. 175-184)Vrsta gradiva - članek, sestavni delLeto - 2022Jezik - angleškiCOBISS.SI-ID - 105665027
Avtor
Jin, Zujin |
Cheng, Gang |
Xu, Shichang |
Yuan, Dunpeng
Teme
Bayesian optimization |
error prediction |
optical mirror processing |
hybrid manipulators |
hyperparametrics |
deep learning |
Bayesova optimizacija |
napovedovanje napak |
obdelava optičnih zrcal |
hibridni manipulatorji |
hiperparametrika |
globoko učenej
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vir: Strojniški vestnik = Journal of mechanical engineering. - ISSN 0039-2480 (Vol. 68, no. 3, Mar. 2022, str. 175-184)
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Jin, Zujin | ![]() |
Cheng, Gang | ![]() |
Xu, Shichang | ![]() |
Yuan, Dunpeng | ![]() |
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