Model mismatching diagnosis method of data driven multivariable control system

A multi-variable control and data-driven technology, applied in general control systems, control/adjustment systems, test/monitoring control systems, etc., can solve problems such as model mismatch, high system maintenance costs, and low system security, and achieve savings Production costs, avoiding economic losses, and reducing system maintenance costs

Inactive Publication Date: 2018-09-14
HUAZHONG UNIV OF SCI & TECH
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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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  • Model mismatching diagnosis method of data driven multivariable control system
  • Model mismatching diagnosis method of data driven multivariable control system
  • Model mismatching diagnosis method of data driven multivariable control system

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Embodiment 1

[0135] Wood-Berry distillation column process such as image 3 As shown, it is a simulation process of methanol-water mixing. It is a chemical process model proposed by Wood and Berry to study and evaluate process control technology and performance monitoring. The details of the rectification process of the Wood-Berry rectification tower are as follows: (1) raw material D enters from the feed plate, and the feed plate divides the whole rectification tower into a rectification section and a stripping section; (2) the solution enters the rectification section. After the distillation tower, because methanol and water have different boiling points, the low-boiling point components are easily vaporized and rise upwards, while the high-boiling point components mostly flow down with the liquid and combine with the rising steam in the distillation tower. Full contact is carried out on the trays of each layer to carry out the heat transfer and mass transfer process; (3) After the liqui...

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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...

Claims

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Application Information

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IPC IPC(8): G05B23/02
CPCG05B23/0205
Inventor 郑英凌丹汪上晓张洪张永
Owner HUAZHONG UNIV OF SCI & TECH
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