Model predictive control performance evaluation and diagnosis method

Abstract

The invention discloses a model predictive control performance evaluation and diagnosis method. The method includes the steps: calculating the real-time performance value Ji and the average form Jnew of a system; selecting a segment of a data set and making the segment a historical performance benchmark value J<hist><*>; comparing the Jnew with the J<hist><*> and obtaining a system performance index [gamma]<k><*>, determining that the system performance is good if the [gamma]<k><*> is close to 1, and moving to the next step if the [gamma]<k><*> is close to 0; calculating an interference error e<0>(k), a predictive error e(k) and a model quality index [eta], and determining that the reason of deterioration of the system performance is external factors or a controller factor, otherwise, determining a system model mismatch and moving to the next step; detecting autocorrelation of an information sequence e(k), and moving to the next step if the autocorrelation of the e(k) exists, otherwise, determining that the model matching degree is good; and since n corresponding to the minimal loss function is the class of the e(k), determining a process model mismatch if the class of the e(k) is greater than the class of a process model, otherwise, determining an interference model mismatch. The overall performance of the system can be evaluated and deterioration sources of the system performance can be positioned only through closed loop input and output data.

Description

technical field [0001] The invention relates to a model predictive control performance evaluation and diagnosis method, which belongs to the technical field of industrial predictive control performance monitoring. Background technique [0002] Model predictive control is an advanced control widely used in the current process industry. It is an important guarantee for enterprises to achieve safety, high efficiency, high quality, low consumption and environmental protection, especially in complex industrial processes such as chemical industry, metallurgy, oil refining and electric power. Favored by enterprises. This is mainly due to its following advantages: (1) The algorithm adopts an open optimization control strategy based on models, rolling optimization combined with feedback correction; (3) It has implicit decoupling ability, which can avoid many problems caused by decoupling control and decentralized control; (4) Model Predictive Control (MPC for short) has better contr...

AI Technical Summary

Problems solved by technology

[0003] However, model predictive control also has its disadvantages. In actual industrial applications, the controller generally has good performance in the initial operation stage, but after the system has been running for a period of time, its performance will gradually deteriorate due to various factors. This will directly affect a series of problems such as product quality, output and increase in production and maintenance costs, causing huge losses to the e

Images