Subspace model identification prediction control method based on data driving

Abstract

The invention relates to a subspace model identification prediction control method based on data driving, and the method comprises the steps: carrying out offline identification on a controlled object through a subspace model identification method, and obtaining an offline state space model; taking the obtained off-line state space model as a prediction model, controlling the system based on a model prediction control strategy, changing a characteristic value of a controlled object, and collecting on-line input and output data after the characteristic value of the controlled object is changed; identifying the controlled object online through a subspace model identification method, and obtaining an online state space model; and updating the online state space model until the difference value between the system output value controlled according to the online state space model and the real output value of the controlled object is smaller than a preset value. According to the method, under the condition that a system model is unknown, the coefficient of a state space system matrix is obtained through the subspace model identification method, on this basis, the controlled object is controlled, the model is updated, finally, the model of the controlled object is identified accurately and operates stably, and a good operation state is achieved.

Description

technical field [0001] The invention relates to the technical field of industrial control, in particular to a data-driven subspace model identification and predictive control method, which can be widely used in process control, such as petroleum, smelting and other process industries, and power electronics and other industries. Background technique [0002] Model predictive control is a kind of algorithm applied in the field of process control that originated in the 1970s. The method has been extensively studied in academia and in various century industrial applications, and it has three basic features: predictive modeling, rolling optimization, and feedback correction. However, with the advancement of science and technology, the scale of modern industrial equipment is getting larger and larger, and the modeling work is becoming more and more complex. How to effectively establish the dynamic model of the control system becomes very necessary. Traditional industrial predicti...

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Problems solved by technology

[0003] For this reason, the present invention aims to overcome the technical problem that it is difficult to obtain a more complex state-space model in the prior art, and provides a data-driven subspace model identification predictive control method. In the case of an unknown system model, through the subspace The model identification method obtains the coefficients of the state-space system matrix, and on this basis, controls the controlled object and updates the model, and finally makes the controlled object model identification accurate and stable, and achieves a good operating state

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