LAUSR

As the main supporting component of the truss robot, the thermal deformation of the beam often has a great influence on the overall thermal error of the truss robot due to its large span. In order to improve the thermal error prediction accuracy of long-span truss robot, a thermal error prediction method based on multiple linear regression and long short-term memory network is proposed based on mechanism and data drive. Firstly, the multiple linear regression model is used to predict the thermal error, and the prediction error data processing. Secondly, the long short-term memory network is established. In order to improve the performance of the long short-term memory network more effectively, an improved particle swarm optimization algorithm is proposed to optimize the hyper-parameters of the long short-term memory network. Finally, the improved particle swarm optimization–long short-term memory network is used to correct the prediction error of the multiple linear regression model. The experimental results show that the combined thermal error prediction model based on multiple linear regression and improved particle swarm optimization–long short-term memory algorithm has higher prediction accuracy than multiple linear regression model and long short-term memory network. The method has stable prediction accuracy and can provide a basis for thermal error compensation.