文献类型：期刊文献

英文题名：Norm-Based Adaptive Coefficient ZNN for Solving the Time-Dependent Algebraic Riccati Equation

作者：Jiang, Chengze[1];Xiao, Xiuchun[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China

年份：2023

卷号：10

期号：1

起止页码：298

外文期刊名：IEEE-CAA JOURNAL OF AUTOMATICA SINICA

收录：SCI-EXPANDED(收录号：WOS:000967214800001)、、EI(收录号：20230613548372)、Scopus(收录号：2-s2.0-85147277217)、WOS

基金：This work was supported in part by the Natural Science Foundation of Guangdong Province, China (2021A1515011847), Postgraduate Education Innovation Project of Guangdong Ocean University (202214, 202250, 202251, 202159, 202160),the Special Project in Key Fields of Universities in Department of Education of Guangdong Province (2019KZDZX1036), the Key Laboratory of Digital Signal and Image Processing of Guangdong Province (2019GDDSIPL-01)

语种：英文

外文摘要：The time-dependent algebraic Riccati equation (TDARE) problem is applied to many optimal control industrial applications. It is susceptible to interference from measurement noises in the virtual environment, which current methods cannot effectively address. A norm-based adaptive coefficient zeroing neural network (NACZNN) model to solve the TDARE problem is proposed, with an adaptive scale coefficient based on the residual error norm to accelerate convergence speed to the theoretical solution. Momentum enhancement terms enable NACZNN to effectively solve the TDARE problem in real time when perturbed by measurement noise. Simulation experiments were designed and executed, and results confirm the NACZNN model's superior robustness and accuracy when solving the TDARE problem disturbed by noises in real time.