A Distributed Model Predictive Control Method Based on Hierarchical Decomposition

Abstract

The invention discloses a distributed model predictive control method based on hierarchical decomposition, comprising the following steps: for a distributed system, an adjacency matrix of the distributed system is acquired according to a communication network structure, and each subsystem is divided into a plurality of connected sets by the adjacency matrix by using an adjacent matrix based path search method; a reachable matrix of the connected sets is constructed, the level of each connected set is determined based on a hierarchical decomposition method in an interpretative modeling method, all the connected sets of the same level are combined into one connected set, and therefore, a connected-set set of a serial structure is constructed; and at each sampling moment, the optimal control input sequence of each subsystem in each connected set is solved in sequence according to the series order, and predictive control is performed on the distributed system. By using the method, communication between connected sets not correlated directly is avoided, the communication burden is reduced greatly while the system stability is ensured, and the problem that the traditional collaborative distributed model predictive control method is of high necessary communication burden is solved.

Description

technical field [0001] The present invention relates to the field of distributed model predictive control, in particular to a distributed model predictive control method based on hierarchical decomposition, which is characterized in that while ensuring system stability, it greatly reduces communication burden. Background technique [0002] 1. Distributed control [0003] Distributed control is a control method relative to centralized control. The control object is composed of multiple interconnected subsystems, each subsystem is controlled by an independent controller, and the controllers exchange information through the network, and Use certain coordinated strategies to achieve a common control objective or overall performance. [0004] Distributed control is generally applied to industrial processes with the following characteristics: it is composed of multiple subsystems, and the systems are coupled through energy and mass, and the system model is complex, with many con...

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Problems solved by technology

[0006] 1) For systems with scattered spatial distribution and wide range, centralized control is difficult to achieve;

[0007] 2) For the control of large-scale and coupled systems, if centralized control is adopted, it will be limited by the calculation speed and device scale;

[0008] 3) When one or several subsystems fail, the centralized control method will also fail to work, and the flexibility and fault tolerance are relatively weak

However, for large-scale systems, due to the large number of subsystems and the complexity of the communication network between subsystems, if the cooperative DMPC method is used, it will lead to a heavy communication burden.

At present, the main research on cooperative DMPC focuses on how to design the entire DMPC control system, but there are few studies on how to reduce the communication burden necessary for traditional cooperative DMPC methods

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