Parameter Optimization and Model Identification of Identification Model Control Based on Improved Generalized Predictive Control

The existing identification methods have complex control algorithm, large amount of computation and poor real-time performance. Therefore, it is difficult to meet the real-time requirements of high-speed and high-precision CNC position servo control system. In this paper, the problem of identification control is deeply analyzed from the independent axis servo control, and the advanced theories and methods such as predictive control and identification control are synthetically applied, and an improved combination of the generalized pretest control and identification model control is proposed. Combined mode control method, through MATLAB simulation and physical simulation, the simulation results show that the identification method can effectively improve the dynamic response performance, steady state performance and robustness of the servo control system, thus reducing the motion error of each servo axis and improving the motion stability.