Model mismatching diagnosis method of data driven multivariable control system

Abstract

The invention discloses a model mismatching diagnosis method of a data driven multivariable control system. The method comprises the following steps: according to closed loop operation data, estimating white noise, identifying an interference model, and acquiring global process model residual errors and process model residual errors of different output channels; acquiring global model quality indexes according to the white noise and the global process model residual errors, detecting overall process model mismatching of a multivariable control system according to the global model quality indexes, acquiring overall model quality indexes and partial model quality indexes according to the white noise and the process model residual errors of different output channels, confirming input / output sub-channels of model mismatching of the multivariable control system, and judging that a process model of an input / output sub-channel has serious model mismatching if a partial model quality index isapproximate to 0. By adopting the method, process model mismatching can be diagnosed under a normal operation condition in the industrial process, system maintenance cost can be lowered, and system security can be improved.

Description

technical field [0001] The invention belongs to the technical field of industrial control, and more specifically relates to a model mismatch diagnosis method of a data-driven multivariable control system. Background technique [0002] Since the controllers of industrial control systems are designed by control engineers according to the process model, the process model is very important for the research of control performance optimization and fault diagnosis. Researchers design the proportional, integral and differential parameters of the classic PID controller according to the process model. MPC controllers use process models and historical data to predict future process outputs, resulting in optimal manipulated variables. After the system is put into operation, the dynamic characteristics of the controlled object are always changing, resulting in process model mismatch. If the mismatch between the process model and the actual object is serious, the mismatch of the process...

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Problems solved by technology

[0006] Aiming at the above defects or improvement needs of the prior art, the present invention provides a model mismatch diagnosis method for a data-driven multivariable control system, thereby solving the problem in the prior art that it is difficult to diagnose the process model under normal operating conditions of the industrial process Technical problems of mismatch, high system maintenance cost, and low system security

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