Learning Control, Fault Diagnosis, and Actuator Applications of Complex Networked Systems

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Control Systems".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 1533

Special Issue Editors


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Guest Editor
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China
Interests: networked systems; fault diagnosis; event-based control; nonlinear control
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Guest Editor
Department of Automation, Southeast University, Nanjing, China
Interests: unmanned systems; multi-agent systems; cooperative control; nonlinear control

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Guest Editor
Department of Automation, Southeast University, Nanjing, China
Interests: stochastic complex network; fixed-time cooperative control; network security controlol

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Guest Editor
Department of Applied Mathematics, Bharathiar University, Coimbatore, Tamil Nadu, India
Interests: systems and control theory; automatic and optimal control; synchronization; consensus control; model predictive control

Special Issue Information

Dear Colleagues,

The integration of networked communication technology and intelligent control has enabled the development of advanced networked control systems, which can adapt to the changes in the system and improve overall performance. Networked control systems have a wide range of applications across various industries, such as smart grid, aerospace systems, intelligent manufacture, etc. At the same time, the diagnosis and prognosis of faults in networked systems is essential for maintaining system performance and avoiding catastrophic failures. This Special Issue aims to bring together researchers and practitioners in the field of fault diagnosis and learning-based control of networked system to share their latest findings and advancements. The focus will be on the development of innovative techniques, algorithms, and strategies for learning-based control and fault diagnosis of complex networked systems. The learning-based control of complex networked systems involves the use of artificial intelligence and machine learning techniques to optimize system performance and adapt to changes in the system. Meanwhile, the fault diagnosis of complex networked systems is challenging due to the complexity of the systems and the large amount of data generated. Therefore, the development of advanced learning-based control and fault diagnosis techniques is crucial for ensuring the safe and reliable operation of complex networked systems. The scope of this Special Issue includes but is not limited to the following topics:

(1) New control and fault detection methods for networked systems involve actuators;

(2) Actuator applications in multi-agent systems, cyber-physical systems, or intelligence systems;

(3) Machine learning and artificial intelligence for networked systems with smart actuators;

(4) Model-based and data-driven approaches for fault diagnosis;

(5) Learning-based control and fault tolerance control of networked systems with actuator fault;

(6) Applications of actuator control systems in industrial, transportation, and aerospace systems.

Dr. Guangtao Ran
Dr. Jian Liu
Dr. Yongbao Wu
Prof. Dr. Rathinasamy Sakthivel
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Actuators is an international peer-reviewed open access monthly 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

  • networked control systems
  • fault detection and fault diagnosis
  • learning-based control

Published Papers (1 paper)

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Research

22 pages, 3851 KiB  
Article
LSTM-CNN Network-Based State-Dependent ARX Modeling and Predictive Control with Application to Water Tank System
by Tiao Kang, Hui Peng and Xiaoyan Peng
Actuators 2023, 12(7), 274; https://doi.org/10.3390/act12070274 - 6 Jul 2023
Cited by 1 | Viewed by 1089
Abstract
Industrial process control systems commonly exhibit features of time-varying behavior, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still a challenging task. In this paper, data-driven modeling techniques and deep learning methods [...] Read more.
Industrial process control systems commonly exhibit features of time-varying behavior, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still a challenging task. In this paper, data-driven modeling techniques and deep learning methods are used to accurately capture a category of a smooth nonlinear system’s spatiotemporal features. The operating point of these systems may change over time, and their nonlinear characteristics can be locally linearized. We use a fusion of the long short-term memory (LSTM) network and convolutional neural network (CNN) to fit the coefficients of the state-dependent AutoRegressive with the eXogenous variable (ARX) model to establish the LSTM-CNN-ARX model. Compared to other models, the hybrid LSTM-CNN-ARX model is more effective in capturing the nonlinear system’s spatiotemporal characteristics due to its incorporation of the strengths of LSTM for learning temporal characteristics and CNN for capturing spatial characteristics. The model-based predictive control (MPC) strategy, namely LSTM-CNN-ARX-MPC, is developed by utilizing the model’s local linear and global nonlinear features. The control comparison experiments conducted on a water tank system show the effectiveness of the developed models and MPC methods. Full article
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