Industrial model predictive control method realizing dynamic optimization based on linear programming

Abstract

An industrial model predictive control method realizing dynamic optimization based on linear programming comprises the following steps: 1) carrying out steady state target optimization: computing the expected target value output in a controlled way in the steady state according to the input quantity of the sampling period; and 2) carrying out dynamic optimization: 2.1) obtaining step response models of the industrial process by a least square method, wherein the step response models are established in N-numbered periods; 2.2) predicting the output quantity at each future moment from the k+1 moment, referring to formula (2); 2.3) ensuring the input and the output in the dynamic process to be in own upper and lower bound ranges, referring to formula (3), setting target constraint, referring to formula (4) and creating the following performance indexes, referring to formula (5); forming the dynamic optimization problem by combining the formulas (2), (3), (4) and (5), solving all the control increments deltau(i) by linear programming, wherein i=0, 1,..., N-m-1, and then substituting the control increments into the formula (2) to compute the predictive output values y(k+1), y(k+2),..., y(N). The method simplifies computation, shortens the computation period and is good in practicability.

Description

technical field The invention relates to an industrial model predictive control method, in particular to a predictive control method for constraining dynamic process input and output, which can be applied to process industrial control, such as papermaking, food processing, petrochemical and other industries. Background technique The existing dynamic matrix control is to generate a non-parametric model offline through the identification method of the non-parametric model. Usually, a discrete FIR (finite impulse response) model is used to obtain the model coefficients after processing and verification. optimization and dynamic control. The dynamic matrix control algorithm is divided into two steps: the first is the steady-state target optimization step, an independent local steady-state optimization is performed to calculate the expected target value of the steady-state controlled output, and this step generally uses linear programming; The step of dynamic optimization is to...

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Problems solved by technology

In order to overcome the shortcomings of high computational complexity, time-consuming and poor practicability in the dynamic optimization process of the existing industrial model predictive control method, the present invention provides a dynamic optimization method based on linear programming that simplifies calculation, shortens the calculation cycle, and has good practicability. industrial model predictive control method

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