Advances and Applications for Data-Driven/Model-Free Control

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 801

Special Issue Editors


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Guest Editor
School of Automation and Electronic Information, Xiangtan University, Xiangtan, China
Interests: Intelligent control; electric drive; servo drive

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Guest Editor
School of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
Interests: industrial process control and applications; complex systems; mobile wheeled robot and motion control; disturbance rejection control; intelligent optimization; advanced control techniques and implementation
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Special Issue Information

Dear Colleagues,

Model-based control techniques have been developed in order to cope with the control problems with assumption that the models of the controlled systems are known. However, modelling a system is not easy, and is sometimes impossible. Data-driven control (DDC) or Modelfree control (MFAC) approaches focus on designing controllers merely using the input/output data of the controlled plant. In the past several years, the DDC/MFAC algorithms were widely studied and numerous interesting results have been obtained. However, the existing results related to the DDC/MFAC design focus on the uniformly ultimately boundedness, convergence analysis and high-order estimation. With advances in analysis and synthesis, more efficient algorithms of DDC/MFAC are expected to emerge, requiring the new mathematical tools and advanced control theories.

The purpose of this Special Issue is to advance the controller design and stability analysis of DDC/MFAC and further promote the research activities in analysis and synthesis for discrete-time systems. The Special Issue is targeted at original works to address some emerging issues and challenges from DDC/MFAC and their applications in science, and engineering.

The list of possible topics includes, but is not limited to:

  1. Equivalent dynamic linearization method
  2. Improved DDC/MFAC strategy
  3. DDC/MFAC-based disturbance estimation and rejection
  4. Robust stability analysis and parameters optimization of DDC/MFAC
  5. Fractional order DDC/MFAC
  6. Combination of DDC/MFAC and other advanced control methods
  7. Applications of DDC/MFAC in unmanned vehicle systems, smart grids, robotics, some other industrial control systems, etc.

Prof. Dr. Yonghong Lan
Dr. Zhuoyun Nie
Guest Editors

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Keywords

  • data-driven control
  • model free adaptive control
  • discrete-time systems
  • dynamic linearization
  • pseudo-partial derivative

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Published Papers (1 paper)

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Research

13 pages, 404 KiB  
Article
Fractional-Order MFAC with Application to DC Motor Speed Control System
by Haizhen Wang, Huihua Jian, Jianhua Huang and Yonghong Lan
Mathematics 2025, 13(4), 610; https://doi.org/10.3390/math13040610 - 13 Feb 2025
Cited by 1 | Viewed by 539
Abstract
Model-free adaptive control (MFAC) can carry out various tasks using only I/O data, providing advantages such as lower operational costs, higher scalability and easier implementation. However, the robustness of MFAC remains an open problem. In this paper, a robust fractional-order model-free adaptive control [...] Read more.
Model-free adaptive control (MFAC) can carry out various tasks using only I/O data, providing advantages such as lower operational costs, higher scalability and easier implementation. However, the robustness of MFAC remains an open problem. In this paper, a robust fractional-order model-free adaptive control (RFOMFAC) scheme is proposed to address the robust tracking control issue for a class of uncertain discrete-time nonlinear systems with bounded measurement disturbance. First, we use a fractional-order dynamic data model relating the relationship between the output signal and the fractional-order input variables based on the compact form dynamic linearization. Then, the pseudo-partial derivative (PPD) is obtained using a higher-order estimation algorithm that includes more information about past input and output data. With the introduction of a reference equation, a fractional-order model-free adaptive control (FOMFAC) law is then proposed. Consequently, using a higher-order PPD-based FOMFAC law can improve the control performance. Furthermore, a modified RFOMFAC algorithm with decreasing gain is constructed. Theoretical analysis indicates that the proposed algorithm can effectively attenuate measurement disturbances. Finally, simulation results demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advances and Applications for Data-Driven/Model-Free Control)
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