Method for performing scattering predictive control on multi-time-scale complex huge system

Abstract

The invention discloses a method for performing scattering predictive control on a multi-time-scale complex huge system. Aiming at a difference of the dynamical property of the interconnected subsystems of the multi-time-scale complex huge system, the state of each subsystem is sampled and calculated in different sampling periods. The method is characterized in that a predictive control method of a variable time domain is adopted to resolve the contradiction between the control performance optimization and the calculation complexity; an inequality constraints conversion method is adopted to convert a constraint optimization problem to a constraint-free optimization problem; the following actions of the subsystem are predicted by using a subsystem module; a predicting result of the subsystem is fed back and corrected by using a measured value; a loss relevant signal is estimated by using an original value substitution method; the calculation complexity is further decreased by using a reduction state space decomposition algorithm so as to promote the optimization timeliness. In this way, the distributed control of an industrial huge system is realized. A system for manufacturing iron proves the effectiveness of the method and shows that the method has the advantages of high convergence rate, high computing efficiency and excellent optimization performance.

Description

technical field [0001] The invention relates to overall optimization and control of a system, in particular to a decentralized predictive control method for a multi-time-scale complex large system. Background technique [0002] A large-scale interconnected system composed of a large number of dynamic unit systems connected to each other is a common system form in industrial production processes such as chemical industry, petroleum, and metallurgy, and its optimal control plays an important role in improving economic benefits. However, the nonlinear and uncertain characteristics of its component units and the complex correlation between them [1], as well as the differences in the time scale of the dynamic characteristics of each subsystem, make it difficult for conventional control methods oriented to a single small-scale object to achieve expected control effect. [0003] As we all know, predictive control based on model prediction, rolling optimization and feedback correct...

AI Technical Summary

Problems solved by technology

In recent years, theoretical research on industrial complex large-scale systems [2]-[6] is basically limited to single-time-scale distributed predictive control, and very few studies [7] involve multi-time-scale predictive control, but they only involve a single window The multi-time-scale optimization problem in the paper does not clarify the specific length and moving mode of the rolling optimization window, and the calculation efficiency of its optimization algorithm is low, while the research on multi-rate systems [8]-[11] is similar to the "multi-time-scale large System" is not the same in terms of concept and research focus

Since the dynamic characteristics of interconnected subsystems in actual objects are often quite different, if the same sampling calculation cycle is still adopted, it will lead to poor control effects, deterioration of control performance, and unnecessary energy consumption.

However, in practical applications, most complex large-scale industrial systems still do not adopt the method of coordinated optimization, but optimize each subsystem in isolation. The production situation has become a technical bottleneck restricting production

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