Tracking Control of the Dynamic Input-Output Economic System Based on Data Fusion
Author(s) -
Yirun Chen,
Wensheng Dai
Publication year - 2022
Publication title -
security and communication networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.446
H-Index - 43
eISSN - 1939-0122
pISSN - 1939-0114
DOI - 10.1155/2022/1461977
Subject(s) - computer science , control theory (sociology) , controller (irrigation) , control (management) , tracking system , sensor fusion , tracking (education) , optimal control , control system , process (computing) , mathematical optimization , kalman filter , mathematics , artificial intelligence , engineering , agronomy , biology , electrical engineering , pedagogy , operating system , psychology
In recent years, under the background of stable economic operation, people's research on economic systems has become increasingly popular. The dynamic input-output model reflects the change and development process of the input-output relationship of the economic system over a period of time. The main purpose of tracking control is to design a suitable controller so that the output of the control system can track the output of the reference system as much as possible. In the economic system, data is an important factor. Based on this, this paper mainly studies the tracking control of dynamic input-output economic system based on data fusion. This research takes the data fusion of the dynamic input-output economic system as the starting point and takes the optimal control and tracking of the economic system as the research object of this research. Based on data fusion technology, a new dynamic input-output economic system tracking and control is proposed. This paper studies the finite-time optimal tracking control of linear systems. Through numerical examples, comparing the finite-time and infinite-time optimal control simulation results, it is proved that the algorithm can achieve good tracking control. Experimental data shows that the optimal and suboptimal performance indicators for a limited time are 0.7729412 and 1.5687310, respectively. Therefore, compared with the infinite-time optimal control, the performance loss and the final tracking error of the suboptimal control proposed in this study are reduced.
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