Power prediction of a 4-CRU parallel mechanism based on extra gradient boosting regressor

Abstract

Power analysis of a robot is the last step of the dynamic modelling after calculating its kinematics. The power calculation takes a long time due to the complexity of the process. This paper deals with the power prediction of a 4-CRU parallel mechanism by using Extra Gradient Boosting Regressor (XGBR). XGBR is one of the algorithms in machine learning that can take good learners and leave weak learners from the models built. Then, XGBR is optimized using Random Search to overcome the hyper parameter tuning with the best prediction accuracy. The XGBR tuned hyper parameter was performed a satisfactory prediction model. The prediction results can perfectly show the robot behavior since it is based on the smallest error of model prediction. The error value based on Mean Absolute Percentage Error (MAPE) is 0.05099% and based on Mean Square error (MSE) is 0.0001 which took 11 minutes. The value of accuracy and efficiency is very reasonable to say that the power prediction model of a 4-CRU parallel mechanism has successfully performed.