{"id":3023,"date":"2026-04-09T23:20:09","date_gmt":"2026-04-09T15:20:09","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=3023"},"modified":"2026-06-08T22:51:37","modified_gmt":"2026-06-08T14:51:37","slug":"quantized-output-observer-based-data-driven-model-free-adaptive-control","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=quantized-output-observer-based-data-driven-model-free-adaptive-control","title":{"rendered":"Quantized Output Observer-based Data Driven Model-free Adaptive Control"},"content":{"rendered":"\n
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<\/i> 2024<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 27, Issue 2<\/a><\/p>\n<\/div>\n\n\n\n

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Bing Ren1,2<\/sup> and Guangqing Bao3<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China<\/p>\n\n\n\n

2<\/sup>School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, China<\/p>\n\n\n\n

3<\/sup>School of Electronics & Information Engineering, Southwest Petroleum University, Chengdu, China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0December 5, 2022
Accepted:\u00a0March 13, 2023
Publication Date:\u00a0April 9, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Quantizaion leves and transmission.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202402_27(2).0004<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This paper studies the quantized output data observer-based data-driven model-free adaptive control(qMFAC) for discrete-time nonlinear systems with unknown structures and network transmission constraints. First, an adaptive observer based on quantized output data is generated with the use of a logarithmic quantizer, and a pseudo-biased derivative(PPD) estimation scheme based on the output quantized data observer is proposed. By dynamic linearization(DL) techniques, a incomplete equivalent data model containing quantized output data are built. Then, the observer output is used to develop a data-driven model-free adaptive control strategy that only makes use of quantified output and input. With the Lyapunov function and sector boundary approaches, the bounded tracking performance of the proposed qMFAC is strictly theoretical analyzed, and the effectiveness of qMFAC is verified through numerical simulation and simulation experiments of the shell and tube heat exchanger control system.<\/p>\n\n\n\n

Keywords:\u00a0Quantized output data; Adaptive observer; Data-driven; Logarithmic quantizer; Pseudo-partial derivative;<\/em><\/p>\n\n\n\n

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