In the main drive system of a rolling mill, shaft torsional vibration is often generated when a motor and a roll are connected with a flexible shaft. State feedback control can effectively suppress the torsional vibration of the main drive system of a rolling mill. Because of the difficulty of measuring the load speed and the shaft torque, and moreover the sensitivity of the Luenberger observer to the model uncertainties and the noise include in the detected signal, in this paper, we propose an extended state observer (ESO), a new observer, to estimate the unknown states and the load torque disturbance. We propose an ESO and linear quadratic (LQ) based speed controller with an integrator and load torque feedforward compensation for torsional vibration suppression in a two-mass main drive system of a rolling mill. The simulation results show the controller effectively improves the performances of command following, torsional vibration suppression, and robustness to parameter variation. This is the first time that the ESO has been utilized in torsional vibration control of the main drive system of a rolling mill, and its validity and superiority is verified in comparison with the conventional proportional integral (PI) controller and the state feedback controller based on a reduced-order state observer.
The dynamic model of 4-h mill, which couples with the rolling process model, the mill roll stand structure model, and the hydraulic servo system model, is built by analyzing the vibration process of cold rolling. By linearization, the multiple input multiple output linear transfer function matrix model of single stand 4-h cold mill system is obtained. With the consideration of strip quality, the model of strip thickness control system is established in a simplified form. Meanwhile, the robust controller based on quantitative feedback theory is designed for the gauge control model. A comparison with PID controller shows that the controller has better disturbance attenuation performance for parameter uncertainty and external disturbance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.