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Journals
Processes
Special Issues
Design and Analysis of Adaptive Identification and Control
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Design and Analysis of Adaptive Identification and Control

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1649

Special Issue Editors

Dr. Weicun Zhang

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: adaptive control; self-tuning control; multiple model adaptive control; multiple model adaptive estimation; stability analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Quanmin Zhu

E-Mail Website
Guest Editor
School of Engineering, University of the West of England-Frenchy Campus, Coldharbour Lane, Bristol, UK
Interests: dynamic system modeling; identification; control; simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Adaptive control originated from the gain scheduling control of high-performance aircraft in the early 1950s. To be specific, model reference adaptive control (MRAC) was proposed by Whitaker et al. to solve the control problem of an autopilot. From the viewpoint of theory research, self-tuning control (STC) was proposed by Kalman in 1958 to deal with the optimal control of a stochastic system with unknown or time-varying parameters and then connected with actual applications in paper-making machine through the pioneering work of Astrom and Wittenmark. It is well known that identification is the most important component of an adaptive control system.

This Special Issue will explore recent technological developments in adaptive identification and control (design methods and theoretical analysis), especially for nonlinear stochastic processes such as robotic systems, manufacturing systems, transportation systems, power systems, chemical systems, etc.

Original research articles and reviews are welcome in this Special Issue. Research areas may include (but are not limited to) the following:

  • Identification and self-tuning adaptive control;
  • Event-triggered adaptive identification and control;
  • Intelligent adaptive control;
  • Robust adaptive control;
  • Adaptive sliding-mode control.

Dr. Weicun Zhang
Prof. Dr. Quanmin Zhu
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. Processes 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

  • adaptive identification and control systems
  • design and analysis
  • adaptive identification and control system simulation
  • process modeling/identification
  • applications of adaptive identification and control system
  • stability and convergence of adaptive identification and control

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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21 pages, 28395 KiB  
Article
Sensorless Position Control in High-Speed Domain of PMSM Based on Improved Adaptive Sliding Mode Observer
by Liangtong Shi, Minghao Lv and Pengwei Li
Processes 2024, 12(11), 2581; https://doi.org/10.3390/pr12112581 - 18 Nov 2024
Viewed by 479
Abstract
To improve the speed buffering and position tracking accuracy of medium–high-speed permanent magnet synchronous motor (PMSM), a sensorless control method based on an improved sliding mode observer is proposed. By the mathematical model of the built-in PMSM, an improved adaptive super-twisting sliding mode [...] Read more.
To improve the speed buffering and position tracking accuracy of medium–high-speed permanent magnet synchronous motor (PMSM), a sensorless control method based on an improved sliding mode observer is proposed. By the mathematical model of the built-in PMSM, an improved adaptive super-twisting sliding mode observer is constructed. Based on the LSTA-SMO with a linear term of observation error, a sliding mode coefficient can be adjusted in real time according to the change in rotational speed. In view of the high harmonic content of the output back electromotive force, the adaptive adjustment strategy for the back electromotive force is adopted. In addition, in order to improve the estimation accuracy and resistance ability of the observer, the rotor position error was taken as the disturbance term, and the third-order extended state observer (ESO) was constructed to estimate the rotational speed and rotor position through the motor mechanical motion equation. The proposed method is validated in Matlab and compared with the conventional linear super twisted observer. The simulation results show that the proposed method enables the observer to operate stably in a wide velocity domain and reduces the velocity estimation error to 6.7 rpm and the position estimation accuracy error to 0.0005 rad at high speeds, which improves the anti-interference capability. Full article
(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)
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20 pages, 1758 KiB  
Article
Research on the Identification Method of Respiratory Characteristic Parameters during Mechanical Ventilation
by Yuxin Zhang, Jing Bai, Xingyi Ma and Yu Xu
Processes 2024, 12(8), 1719; https://doi.org/10.3390/pr12081719 - 15 Aug 2024
Viewed by 512
Abstract
In order to enhance the accuracy of ventilator parameter setting, this paper analyzes two identification methods for respiratory characteristic parameters of non-invasive ventilators and invasive ventilators. For non-invasive ventilators, a respiratory characteristic parameter identification method based on a respiration model is established. In [...] Read more.
In order to enhance the accuracy of ventilator parameter setting, this paper analyzes two identification methods for respiratory characteristic parameters of non-invasive ventilators and invasive ventilators. For non-invasive ventilators, a respiratory characteristic parameter identification method based on a respiration model is established. In this method, the patient’s respiratory sample set is obtained through non-invasive measurements. Experimental results demonstrate that the mean relative error of pulmonary elastance identification was 14.25%, and the mean relative error of intrapulmonary pressure identification was 12.33% using the Romberg integral algorithm. For chronic patients using non-invasive ventilators, the fault-tolerant space for ventilator parameter setting is large; this method meets the requirement of auxiliary setting of non-invasive ventilator parameters. For invasive ventilators, a respiratory characteristic parameter identification method based on the AVOV–BP neural network is established. In this method, the patient’s respiratory sample set is obtained through real-time invasive measurements. Even with small sample datasets, experimental results show that the mean relative error of pulmonary elastance identification and intrapulmonary pressure identification were both 0.22%. For critically ill patients using invasive ventilators, the fault-tolerant space for ventilator parameter setting is small; this method meets the requirement of auxiliary setting of invasive ventilator parameters. Full article
(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)
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