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Pattern-moving-based Robust Model-free Adaptive Control for a Class of Nonlinear Systems with Disturbance and Data Dropout

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  • Control Theory and Applications
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Abstract

The ability to deal with the system disturbance and/or data dropout is often referred to as the robustness of model-free or data-driven control theory. This paper addresses a novel pattern-moving-based partial-form dynamic linearization intermittent model-free adaptive control scheme for a class of nonlinear discrete-time systems with disturbance and random measurement data dropout. Furthermore, the bounded convergence of the tracking error of the closed-loop system is proved by the statistical approach with contraction mapping principle. The basic idea is to consider the pattern-moving-based partial-form dynamic linearization model-free adaptive control method under the condition of missing data which may be caused by network failure, failing sensor or actuator. The designed scheme mainly includes an improved intermittent tracking control law, an intermittent classification-metric bias estimation algorithm and a modified intermittent pseudo gradient vector estimation algorithm. The bounded convergence and effectiveness of the proposed scheme are demonstrated by both the rigorous mathematical inference and two numerical examples.

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Authors and Affiliations

  1. School of Information Engineering, Jingdezhen University, Jiangxi, Jingdezhen, China

    Xiangquan Li

  2. School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, China

    Xiangquan Li, Zhengguang Xu, Cheng Han & Jiarui Cui

  3. Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, 30 Xueyuan Road, Haidian District, Beijing, China

    Zhengguang Xu, Cheng Han & Jiarui Cui

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  1. Xiangquan Li
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  2. Zhengguang Xu
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  3. Cheng Han
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Correspondence to Zhengguang Xu.

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This study was partly supported by the National Natural Science Foundation of China (62076025) and partly supported by the Fundamental Research Funds for the Central Universities of China (FRF-GF-19-016B).

Xiangquan Li received his B.E. degree in measurement and control technology and an M.E. degree in signal and information processing from Harbin University of Science and Technology, Harbin, China, in 2001 and 2007, respectively. Now he is a Ph.D. candidate who majors in control science and engineering in the University of Science and Technology Beijing. His research interests include nonlinear control, adaptive control, pattern recognition, and system identification.

Zhengguang Xu received his Ph.D. degree in electrical engineering from the University of Science and Technology Beijing in 2001. He is a full professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. His research interests include modeling and control of complex processes, pattern recognition and its applications, and image processing.

Cheng Han received his B.E. degree in automation from Qilu University of Technology in 2015, and an M.E. degree in control science and engineering from Qingdao University of Science and Technology in 2019. Now he is a Ph.D. candidate who majors in control science and engineering in University of Science and Technology Beijing. His research interests include nonlinear control, pattern recognition, and system identification.

Jiarui Cui received his B.E. degree in electric engineering from Liaocheng University, China, in 2004, and an M.E. degree in signal and information processing from Harbin University of Science and Technology, China in 2007. He received a Ph.D. candidate from University of Science and Technology Beijing, China in 2011. Now he is a research scientist in the University of Science and Technology Beijing. His research interests include two-dimensional stochastic control, embedded systems, and intelligent manufacturing systems.

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Li, X., Xu, Z., Han, C. et al. Pattern-moving-based Robust Model-free Adaptive Control for a Class of Nonlinear Systems with Disturbance and Data Dropout. Int. J. Control Autom. Syst. 20, 3501–3511 (2022). https://doi.org/10.1007/s12555-021-0445-x

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  • DOI: https://doi.org/10.1007/s12555-021-0445-x

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