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Model‐free adaptive control for a class of nonlinear systems with uniform quantizer
Author(s) -
Bu Xuhui,
Zhu Panpan,
Yu Qiongxia,
Hou Zhongsheng,
Liang Jiaqi
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5107
Subject(s) - control theory (sociology) , quantization (signal processing) , decoding methods , decodes , nonlinear system , tracking error , computer science , algorithm , mathematics , artificial intelligence , control (management) , physics , quantum mechanics
Summary The problem of model‐free adaptive control (MFAC) design for a class of nonlinear systems with data quantization is considered in this article. Consider the case that the system output signal should be quantized before being transmitted to the controller. A MFAC algorithm with uniform quantizer is first proposed. As a result, the proposed MFAC algorithm cannot obtain a zero‐tracking error because of the reduction of available information due to data quantization. To suppress the influence of data quantization, an improved MFAC algorithm with encoding and decoding mechanism is proposed. The improved design first encodes the system information and then transmits it through the network. Then, the controller receives the information and then decodes it to construct the MFAC algorithm. Theoretical result shows that the improved MFAC algorithm with encoding and decoding mechanism can obtain the aim of zero‐tracking error. Finally, there have two examples to verify the effectiveness and applicability of the quantized MFAC algorithm design.