Memory, nonlinear predictive control method, device and equipment

Abstract

The invention discloses a memory, a non-linear predictive control method, a non-linear predictive control device and non-linear predictive control equipment. The non-linear predictive control method comprises the following steps of: constructing an object model group of a non-linear system through model identification; generating a dynamic equation of the object model group, and calculating disturbance characteristics of the object model group according to the dynamic equation; respectively acquiring an actual output measurement value and a state estimation value of the nonlinear system at the current moment; calculating the optimal state estimation value of the nonlinear system at the next moment through an extended Kalman filter; and substituting the optimal state estimation value of the dynamic equation of the object model group into a nonlinear predictive control algorithm of the nonlinear system to obtain an optimal solution, wherein the optimal solution is used for input of the object model group. According to the non-linear predictive control method, the non-linear predictive control device and the non-linear predictive control equipment, the influence caused by model mismatch can be reduced by reducing the frequency and degree of model mismatch.

Description

technical field [0001] The invention relates to the field of industrial control, in particular to a memory, a nonlinear predictive control method, device and equipment. Background technique [0002] Traditional industrial control mostly adopts layered structure to solve economic optimization and control problems. In the hierarchical structure, the first layer is the real-time optimization layer; the second layer is the supervisory layer, which is commonly used for nonlinear predictive control; the third layer is the conventional control layer, including most single-input single-output control loops. [0003] The real-time optimization layer takes the maximum operating profit or the minimum operating cost as the control target, and obtains the optimal solution through optimization calculation, and the nonlinear predictive controller takes this as the set value. In each operating cycle of the nonlinear predictive controller, it will perform an optimization calculation based o...

AI Technical Summary

Problems solved by technology

In practical applications, due to the generation of model mismatch, it will be impossible to effectively control the controlled object, and even control failure will occur in severe cases. Therefore, it is necessary to develop a nonlinear predictive control method that can eliminate model mismatch.

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