An accurate dynamic model for the computer numerical control machine tool feed system is of great significance to improve the machining accuracy. However, the accurate dynamic model of the feed system is difficult to be established because its dynamic characteristics not only depend on the performance of subsystems, such as mechanical, electrical, and control, but also on the interaction between them. In order to solve this problem, a modular modeling method based on a non-causal connection is proposed in this article, and the multi-domain seamless integrated model for the ball screw feed system is established. First, the feed system is decomposed by modularization, and the interface models in each domain are defined. Then all the subsystems are modeled strictly, and the nonlinear characteristics of each subsystem are analyzed. Finally, the multi-domain seamless integrated model of the ball screw feed system is established by the non-causal connection of subsystem models, and the experiment is carried out to validate the system model. The experimental results show that the multi-domain seamless integrated model of the ball screw feed system established in this article can accurately reflect the dynamic characteristics of the real physical system, and has high prediction accuracy for the dynamic following error. It is of great significance to further study the multi-domain coupling characteristics and compensation control methods of the machine tool system.

Position error-compensation control in the servo system of computerized numerical control (CNC) machine tools relies on accurate prediction of dynamic tracking errors of the machine tool feed system. In this paper, in order to accurately predict dynamic tracking errors, a hybrid modeling method is proposed and a dynamic model of the ball screw feed system is developed. Firstly, according to the law of conservation of energy, a complete multi-domain system analytical model of a ball screw feed system was established based on energy flow. In order to overcome the uncertainties of the analytical model, then the data-driven model based on the back propagation (BP) neural network was established and trained using experimental data. Finally, the data-driven model was coupled with the multi-domain analytical model and the hybrid model was developed. The model was verified by experiment at different velocities and the results show that the prediction accuracy of the hybrid model reaches high levels. The hybrid modeling method combines the advantages of analytical modeling and data-driven modeling methods, and can significantly improve the feed system’s modeling accuracy. The research results of this paper are of great significance to improve the compensation control accuracy of CNC machine tools.

Computed Torque Control (CTC) is the most direct and effective way to improve the motion control performance of robot. But the computation of the joint torque is quite difficult, and because of the uncertainty of the parameters, an accurate inverse robot dynamic model for torque generation is difficult to obtain. An efficient inverse dynamic model of the industrial robot based on lie algebra is proposed and applied to the computed torque control. In order to overcome the uncertainty of parameters, the inverse robot dynamic model is linearized and an adaptive computed torque control is proposed. In order to validate the adaptive torque computed control method, a multi-domain integrated system model of 6-DOF industrial robot is established and the simulation results show that the adaptive computed torque control system has the function of parameter self-learning, the inaccurate parameters converge to the true value finally. The adaptive control shows better control performance than the traditional computed torque control.

A novel feedforward control method of elastic-joint robot based on hybrid inverse dynamic model is proposed in this paper. The hybrid inverse dynamic model consists of analytical model and data-driven model. Firstly, the inverse dynamic analytical model of elastic-joint robot is established based on Lie group and Lie algebra, which improves the efficiency of modeling and calculation. Then, by coupling the data-driven model with the analytical model, a feed-forward control method based on hybrid inverse dynamics model is proposed. This method can overcome the influence of the inaccuracy of the analytical inverse dynamic model on the control performance, and effectively improve the control accuracy of the robot. The data-driven model is used to compensate for the parameter uncertainties and non-parameter uncertainties of the analytical dynamic model. Finally, the proposed control method is proved to be stable and the multi-domain integrated system model of industrial robot is developed to verify the performance of the control scheme by simulation. The simulation results show that the proposed control method has higher control accuracy than the traditional torque feed-forward control method.