Design and Validation of Zeroing Neural Network to Solve Time-Varying Algebraic Riccati Equation

被引：11

作者：

Liu, Hang ^{[1
]}

Wang, Tie ^{[1
]}

Guo, Dongsheng ^{[2
]}

机构：

[1] Qiqihar Med Univ, Logist Management Dept, Qiqihar 161006, Peoples R China

[2] Huaqiao Univ, Coll Informat Sci & Engn, Xiamen 361021, Peoples R China

来源：

IEEE ACCESS | 2020年 / 8卷

关键词：

Mathematical model; Computational modeling; Analytical models; Time-varying systems; Riccati equations; Symmetric matrices; Linear systems; Algebraic Riccati equation; time-varying; zeroing neural network (ZNN); theoretical analysis; simulation validation; STABILIZING SOLUTIONS; VERIFIED COMPUTATION;

D O I：

10.1109/ACCESS.2020.3039253

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Many control problems require solving the algebraic Riccati equation (ARE). Previous studies have focused more on solving the time-invariant ARE than on solving the time-varying ARE (TVARE). This paper proposes a typical recurrent neural network called zeroing neural network (ZNN) to determine the solution of TVARE. Specifically, the ZNN model, which is formulated as an implicit dynamic equation, is developed by defining an indefinite error function and using an exponential decay formula. Then, such a model is theoretically analyzed and proven to be effective in solving the TVARE. Computer simulation results with two examples also validate the efficacy of the proposed ZNN model.

引用

页码：211315 / 211323

页数：9

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