Modeling quality monitoring method for model predictive controller (MPC) with drift interference

Abstract

The invention discloses a modeling quality monitoring method for a model predictive controller (MPC) with drift interference. The method comprises the following steps: an interference model for a closed loop control system is built; according to the actual condition of the closed loop control system and a given control target, a dynamic model predictive controller (MPC) for a process is designed; the interference model and the MPC are adopted for controlling the closed loop control system, and process data obtained by operation of the closed loop control system are acquired; according to the structure of the closed loop control system, orthogonal projection is carried out on process output and process input data, and process estimation interference update is acquired; according to an established reference signal of the closed loop control system and the process actual output, the actual tracking error of the closed loop control system is acquired; according to the process estimation interference update and the actual tracking error, a model quality index for the closed loop control system is acquired; and according to the structure of the closed loop control system, the model quality index is used for monitoring the modeling quality. The method of the invention has the advantages of high feasibility, few consumed resources for processing, and high evaluation result accuracy.

Description

technical field [0001] The invention belongs to the field of model predictive control, and more specifically relates to a modeling quality monitoring method of a model predictive controller with drift disturbance. Background technique [0002] Model Predictive Control (Model Predictive Control, MPC) is an advanced model-based control method widely used in the field of industrial process control. It has the advantages of good control effect, strong robustness, and low requirements for model accuracy. [0003] The practical application of model predictive control in industrial processes is called model predictive controller (Model Predictive Controller, MPC Controller). The MPC controller has the characteristics of simple modeling, good dynamic control effect, and strong robustness, and has good control performance in the initial stage of production; however, as time goes by, the performance of the MPC controller will gradually decline, and finally even have to be switched to...

AI Technical Summary

Problems solved by technology

[0005] On the one hand, white noise is mostly used as the system interference noise source in current modeling quality monitoring technologies, but in actual industrial processes, the interference tends to increase slowly with time, showing non-Gaussian characteristics; on the other hand, the current The technology of

Images