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Applied Sciences
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New Topics on System Learning and Control and Its Applications
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New Topics on System Learning and Control and Its Applications

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 719

Special Issue Editors

Prof. Dr. Mingcong Deng

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan
Interests: nonlinear safety control and fault detection; real time estimation of human arm impedance; smart material actuators; micro hands; wireless power transfer systems; micro reactors
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lin Meng

E-Mail Website
Guest Editor
Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu 525-8577, Japan
Interests: processor architecture; high-performance computing; AI-based IoT; underwater drones; cultural heritage preservation and protection
Special Issues, Collections and Topics in MDPI journals
Dr. Ya Zhang

E-Mail Website
Guest Editor
Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo 1848588, Japan
Interests: terahertz; MEMS sensor; nonlinear oscillation; mode coupling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

System learning and control are fundamental and evolving research areas, increasingly vital for the development of intelligent, adaptive, and robust systems. Recent advancements in artificial intelligence, machine learning, and high-performance computing have enabled the integration of learning-based techniques into control systems. This integration opens new frontiers for dynamic optimization, autonomous decision-making, and reliable control under uncertain or nonlinear conditions.

This Special Issue aims to showcase innovative approaches, theoretical developments, and practical applications that combine system learning techniques with modern control theory. In particular, it encourages submissions focusing on fuzzy modeling, data-driven control, nonlinear and nonsmooth system handling, and intelligent optimization in uncertain environments, especially for real-world and industrial systems. Topics of interest include, but are not limited to, the following areas:

  • Reinforcement learning and adaptive control.
  • Data-driven system identification and model-free control.
  • Fuzzy modeling and control for nonlinear and uncertain systems.
  • Optimization techniques for real-time and embedded systems.
  • Human-in-the-loop and cooperative control systems.
  • Applications in robotics, industrial automation, energy systems, and smart infrastructure.
  • Single-electron transistors and nanoscale control systems.
  • MEMS/NEMS-based sensing and actuation.
  • Semiconductor nanostructures for intelligent system integration.
  • Terahertz technologies in learning-based communication and control.

This Special Issue welcomes original research papers, experimental studies, and comprehensive review articles that contribute to the advancement of intelligent control systems through the integration of system learning techniques and control theory.

Prof. Dr. Mingcong Deng
Dr. Lin Meng
Dr. Ya Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • learning
  • control systems
  • human factor
  • MEMS
  • IoT

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Published Papers (1 paper)

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Research

16 pages, 2197 KB  
Article
Machine Learning and Operator-Based Nonlinear Internal Model Control Design for Soft Robotic Finger Using Robust Right Coprime Factorization
by Zizhen An and Mingcong Deng
Appl. Sci. 2026, 16(2), 808; https://doi.org/10.3390/app16020808 - 13 Jan 2026
Viewed by 315
Abstract
Currently, machine learning (ML) methods provide a practical approach to model complex systems. Unlike purely analytical models, ML methods can describe the uncertainties (e.g., hysteresis, temperature effects) that are difficult to deal with, potentially yielding higher-precision dynamics by a learning plant given a [...] Read more.
Currently, machine learning (ML) methods provide a practical approach to model complex systems. Unlike purely analytical models, ML methods can describe the uncertainties (e.g., hysteresis, temperature effects) that are difficult to deal with, potentially yielding higher-precision dynamics by a learning plant given a high-volume dataset. However, employing learning plants that lack explicit mathematical representations in real-time control remains challenging, namely, the model can be conversely looked at as a mapping from input data to output, and it is difficult to represent the corresponding time relationships in real applications. Hence, an ML and operator-based nonlinear control design is proposed in this paper. In this new framework, the bounded input/output spaces of the learning plant are addressed rather than mathematical dynamic formulation, which is realized by robust right coprime factorization (RRCF). While the stabilized learning plant is explored by RRCF, the desired tracking performance is also considered by an operator-based nonlinear internal model control (IMC) design. Eventually, practical application on a soft robotic finger system is conducted, which indicates the better performance of using the controlled learning plant and the feasibility of the proposed framework. Full article
(This article belongs to the Special Issue New Topics on System Learning and Control and Its Applications)
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