Algorithm research for increasing accuracy of rolling mill servo system under the condition of state feedback

The control problem under the condition of uncertainty is an important subject of modern control field. In most control problems, the actual control object and mathematical model for the controller design are always present differences. These differences come from the object parameter uncertainty and time-varying, etc. Mill hydraulic position servo system is the complex nonlinear control system with parameter uncertainty and lots disturbance factors. There is biggish error of the model and the actual object. To eliminate the steady-state error caused by this kind of model error, the paper builds parallel compensation, gain integral compensation, online identification self-calibration and dynamic feedback control strategies on the basis of the steady state error resource and model correction. The simulation results show that the dynamic feedback algorithm has the advantages of both dynamic and static performance.