Device for identifying non-linear process dynamic model

Abstract

The invention relates to a device for identifying a non-linear process dynamic model, which comprises an experimental module and an identifying module. The experimental module is connected with a non-linear industrial process through DCS or PLC or other control machines and is connected with the identifying module. The experimental module generates an experimental signal and automatically carries out an experiment; and the identifying module uses the current process experimental data input by the experimental module to automatically identify the non-linear process dynamic model to check the quality of the model and send an adjusting signal according to the quality of the model, and the adjusting signal is input into the experimental module to adjust the current experimental parameter. The device for identifying the non-linear process dynamic model can be used for carrying out an identifying experiment and model identification for the non-linear industrial process, wherein the non-linear industrial process can be continuous, intermittent or feeding intermittent. An obtained non-linear process dynamic model can be used for model predictive controllers, conventional PID (proportion, integral and differential) controllers and other advanced process controllers, and can be also used for inference models for predicting product quality and soft measuring equipment.

Description

technical field [0001] The invention belongs to the technical field of control, and relates to a model predictive control (MPC) technology, in particular to an identification device for a nonlinear dynamic model in model predictive control (MPC) equipment, which is used to identify oil refining, petrochemical, electric power, chemical Nonlinear dynamic models of production processes in process industries such as pharmaceuticals, metallurgy, food and paper. The device is capable of handling large-scale industrial processes with multiple controlled variables (MV) and multiple controlled variables (CV). The nonlinear model obtained by the present invention can be used in model predictive control (MPC) and other advanced control (APC), and can also be used in reasoning models or soft sensors to predict product quality that cannot be frequently measured due to high cost. Background technique [0002] Model predictive control (MPC: Model Predictive Control) or model predictive co...

AI Technical Summary

Problems solved by technology

However, linear MPC has limitations: when the industrial process works in a large area or in intermittent operation, the linear model is usually not accurate enough, so linear MPC may not be able to achieve satisfactory control results

For nonlinear MPC, one of the most challenging tasks is to identify the dynamic model of the nonlinear process. However, there is no method to systematically conduct low-cost identification experiments and establish a reliable nonlinear dynamic process model (Qin and Badgwell, 2000: An overview of nonlinear model predictive control applications, Proceedings Nonlinear Model PredictiveControl, edited by F.Allgower and A.Zheng)

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