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Electronics
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Advances in Stochastic System Modeling, Control, Optimization, and Their Applications
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Advances in Stochastic System Modeling, Control, Optimization, and Their Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 11171

Special Issue Editors

Dr. Qichun Zhang

E-Mail Website
Guest Editor
Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
Interests: stochastic systems; control systems; non-gaussian systems; entropy optimisation; stochastic distribution control; probabilistic decoupling; performance enhancement; non-gaussian filtering; data-driven design and optimisation; computational neuroscience; brain-computer interface
Special Issues, Collections and Topics in MDPI journals
Dr. Zhan Shu

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, University of Alberta, Edmonton, AB T6G 2H5, Canada
Interests: control systems; connected/networked systems; cyberphysical fusion

Special Issue Information

Dear Colleagues,

Control system design is the core component for the automation of many industrial processes. The control design framework has been established from classical control to modern control, which has been widely used for many particular applications. However, practical systems contain random noises from processes and measurements. To describe the characteristics of systems with noises, stochastic system description has been adopted which covers modeling, control, and optimization. Recently, with the development of networked structures and AI, large-scale complex systems reflect various stochastic properties, while the existing results cannot be extended to the mentioned complicated cases directly. Then, some new investigations are essential to deal with new challenges in terms of stochastic systems.

Data-based stochastic system analysis is an important statistical approach to reflect the properties of the stochastic systems where the dynamics of the systems are reflected by the dynamical dataset. Using the collected data, system modeling can be achieved considering random variables. In addition, system control and optimization can be achieved using a data-driven approach when the system model is unknown or partly unknown. Therefore, stochastic system research is generalized from the point of view of data, which enriches the potential practical applications of stochastic systems in the near future.

The main aim of this Special Issue is to seek high-quality submissions that highlight recent advances in stochastic system theory and the related applications and address recent breakthroughs in stochastic system modeling, control, optimization, stability analysis, system monitoring, and fault diagnosis. The topics of interest include but are not limited to:

  • Stochastic system modeling, simulation, and analysis;
  • Stochastic nonlinear system control and stabilization;
  • Stochastic distribution control and optimization;
  • Nonlinear filtering and non-Gaussian filtering;
  • Stochastic system condition monitoring, fault diagnosis, and tolerant control;
  • Data-driven stochastic system design and analysis;
  • Applications of stochastic system design.

Dr. Qichun Zhang
Dr. Zhan Shu

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. Electronics 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

  • Stochastic system modeling
  • Stochastic system control
  • Stochastic system optimization
  • Stochastic system application

Published Papers (6 papers)

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Editorial

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3 pages, 167 KiB  
Editorial
Editorial: Advances in Stochastic System Modeling, Control, Optimization, and Their Applications
by Qichun Zhang and Zhan Shu
Electronics 2022, 11(24), 4133; https://doi.org/10.3390/electronics11244133 - 12 Dec 2022
Viewed by 768
Abstract
Stochastic systems can be widely adopted for describing practical complex systems, such as meteorology. Recently, there have been many advances in the design of stochastic systems, including system modeling, control, estimation, performance enhancement, and industrial applications. Motivated by these results, this Special Issue [...] Read more.
Stochastic systems can be widely adopted for describing practical complex systems, such as meteorology. Recently, there have been many advances in the design of stochastic systems, including system modeling, control, estimation, performance enhancement, and industrial applications. Motivated by these results, this Special Issue encourages researchers to publish their latest contributions in the study of stochastic systems. In summary, we first introduce the current technical challenges in stochastic systems. Then, a current prevalent problem is provided to demonstrate the challenges in these systems, while the developing trends for stochastic system research are summarised. In particular, data-driven non-Gaussian system analyses will be the one of the significant research focal points in future. Full article
(This article belongs to the Special Issue Advances in Stochastic System Modeling, Control, Optimization, and Their Applications)

Research

Jump to: Editorial

23 pages, 82248 KiB  
Article
Extended State Observer Based-Backstepping Control for Virtual Synchronous Generator
by Shamseldeen Ismail Abdallah Haroon, Jing Qian, Yun Zeng, Yidong Zou and Danning Tian
Electronics 2022, 11(19), 2988; https://doi.org/10.3390/electronics11192988 - 21 Sep 2022
Cited by 4 | Viewed by 1575
Abstract
The penetration of distributed generators (DGs)-based power electronic devices leads to low inertia and damping properties of the modern power grid. As a result, the system becomes more susceptible to disruption and instability, particularly when the power demand changes during critical loads or [...] Read more.
The penetration of distributed generators (DGs)-based power electronic devices leads to low inertia and damping properties of the modern power grid. As a result, the system becomes more susceptible to disruption and instability, particularly when the power demand changes during critical loads or the system needs to switch from standalone to a grid-connected operation mode or vice versa. Developing a robust controller to deal with these transient cases is a real challenge. The inverter control method via the virtual synchronous generator (VSG) control method is a better way to supply the system’s inertia and damping features to boost system stability. Therefore, a nonlinear control strategy for VSG with uncertain disturbance is proposed in this paper to enhance the system stability in the islanded, grid-connected, and transition modes. Firstly, the mechanical equations for a VSG’s rotor, which include virtual inertia and damping coefficient, are presented, and the matching mathematical model is produced. Then, the nonlinear backstepping controller (BSC) method combined with the extended state observer (ESO) is constructed to compensate for the uncertainty. The Lyapunov criteria were used to prove the method’s stability. Considering the issue of uncertain items, a second-order ESO is built to estimate uncertainty and external disruption. Finally, the suggested control strategy is validated through three simulation experiments; the findings reveal that the proposed control method has an excellent performance with fast response and tracking under various operating situations. Full article
(This article belongs to the Special Issue Advances in Stochastic System Modeling, Control, Optimization, and Their Applications)
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27 pages, 7840 KiB  
Article
Optimal Scheduling of Cogeneration System with Heat Storage Device Based on Artificial Bee Colony Algorithm
by Xinfu Pang, Xu Zhang, Wei Liu, Haibo Li and Yibao Wang
Electronics 2022, 11(11), 1725; https://doi.org/10.3390/electronics11111725 - 29 May 2022
Cited by 4 | Viewed by 1430
Abstract
The rigid constraint of using heat in determining electricity for thermal power units is eliminated to improve the absorption capacity of wind power. In this study, heat storage devices and electric boilers are added to the cogeneration system to alleviate the wind curtailment [...] Read more.
The rigid constraint of using heat in determining electricity for thermal power units is eliminated to improve the absorption capacity of wind power. In this study, heat storage devices and electric boilers are added to the cogeneration system to alleviate the wind curtailment phenomenon. First, the main reasons for wind curtailment are analyzed according to the structural characteristics of the power supply in the northern part of China. Second, a scheduling model of a cogeneration system, including a heat storage device and an electric boiler, is constructed. An improved artificial bee colony algorithm program is also designed and compiled based on MATLAB. Finally, the feasibility of the proposed scheme is verified by simulation examples, and an economic analysis of wind power consumption is performed. Results show that adding electric boilers lessens coal consumption costs and improves economic benefits. Full article
(This article belongs to the Special Issue Advances in Stochastic System Modeling, Control, Optimization, and Their Applications)
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12 pages, 785 KiB  
Article
An Efficient Simulation-Based Policy Improvement with Optimal Computing Budget Allocation Based on Accumulated Samples
by Xilang Huang and Seon Han Choi
Electronics 2022, 11(7), 1141; https://doi.org/10.3390/electronics11071141 - 04 Apr 2022
Cited by 1 | Viewed by 1478
Abstract
Markov decision processes (MDPs) are widely used to model stochastic systems to deduce optimal decision-making policies. As the transition probabilities are usually unknown in MDPs, simulation-based policy improvement (SBPI) using a base policy to derive optimal policies when the state transition probabilities are [...] Read more.
Markov decision processes (MDPs) are widely used to model stochastic systems to deduce optimal decision-making policies. As the transition probabilities are usually unknown in MDPs, simulation-based policy improvement (SBPI) using a base policy to derive optimal policies when the state transition probabilities are unknown is suggested. However, estimating the Q-value of each action to determine the best action in each state requires many simulations, which results in efficiency problems for SBPI. In this study, we propose a method to improve the overall efficiency of SBPI using optimal computing budget allocation (OCBA) based on accumulated samples. Previous works have mainly focused on improving SBPI efficiency for a single state and without using the previous simulation samples. In contrast, the proposed method improves the overall efficiency until an optimal policy can be found in consideration of the state traversal property of the SBPI. The proposed method accumulates simulation samples across states to estimate the unknown transition probabilities. These probabilities are then used to estimate the mean and variance of the Q-value for each action, which allows the OCBA to allocate the simulation budget efficiently to find the best action in each state. As the SBPI traverses the state, the accumulated samples allow appropriate allocation of OCBA; thus, the optimal policy can be obtained with a lower budget. The experimental results demonstrate the improved efficiency of the proposed method compared to previous works. Full article
(This article belongs to the Special Issue Advances in Stochastic System Modeling, Control, Optimization, and Their Applications)
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13 pages, 1180 KiB  
Article
DWT-LSTM-Based Fault Diagnosis of Rolling Bearings with Multi-Sensors
by Kai Gu, Yu Zhang, Xiaobo Liu, Heng Li and Mifeng Ren
Electronics 2021, 10(17), 2076; https://doi.org/10.3390/electronics10172076 - 27 Aug 2021
Cited by 25 | Viewed by 2553
Abstract
Bearings are widely used in many steam turbine generator sets and other large rotating equipment. With the rapid development of contemporary industry, there is a great number of rotating equipment in various large factories, such as nuclear power plants. As the core component [...] Read more.
Bearings are widely used in many steam turbine generator sets and other large rotating equipment. With the rapid development of contemporary industry, there is a great number of rotating equipment in various large factories, such as nuclear power plants. As the core component of rotating machinery, the failure of rolling bearings may lead to serious accidents during the industrial production operation. In order to accurately diagnose the fault status of rolling bearings, a novel long short-term memory (LSTM) model with discrete wavelet transformation (DWT) for multi-sensor fault diagnosis is proposed in this paper. The main purpose of this paper is to use the DWT-LSTM model to diagnose the health of rolling bearings. Firstly, the DWT is used to obtain detailed fault information in both different frequency and time scales. Then, the LSTM network is employed to characterize the long-term dependencies hidden in the time series of the fault information. The proposed DWT-LSTM method makes full use of the advantages of feature extraction based on expert experience and deep network learning to discover complex patterns from a large amount of data. Finally, the feasibility and efficiency of the proposed method are illustrated by comparison with the existing methods. Full article
(This article belongs to the Special Issue Advances in Stochastic System Modeling, Control, Optimization, and Their Applications)
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20 pages, 5565 KiB  
Article
Application of Nonlinear Adaptive Control in Temperature of Chinese Solar Greenhouses
by Yonggang Wang, Yujin Lu and Ruimin Xiao
Electronics 2021, 10(13), 1582; https://doi.org/10.3390/electronics10131582 - 30 Jun 2021
Cited by 8 | Viewed by 1731
Abstract
The system of a greenhouse is required to ensure a suitable environment for crops growth. In China, the Chinese solar greenhouse plays a crucial role in maintaining a proper microclimate environment. However, the greenhouse system is described with complex dynamic characteristics, such as [...] Read more.
The system of a greenhouse is required to ensure a suitable environment for crops growth. In China, the Chinese solar greenhouse plays a crucial role in maintaining a proper microclimate environment. However, the greenhouse system is described with complex dynamic characteristics, such as multi-disturbance, parameter uncertainty, and strong nonlinearity. It is difficult for the conventional control method to deal with the above problems. To address these problems, a dynamic model of Chinese solar greenhouses was developed based on energy conservation laws, and a nonlinear adaptive control strategy combined with a Radial Basis Function neural network was presented to deal with temperature control. In this approach, nonlinear adaptive controller parameters were determined through the generalized minimum variance laws, while unmodeled dynamics were estimated by a Radial Basis Function neural network. The control strategy consisted of a linear adaptive controller, a neural network nonlinear adaptive controller, and a switching mechanism. The research results show that the mean errors were 0.8460 and 0.2967, corresponding to a conventional PID method and the presented nonlinear adaptive scheme, respectively. The standard errors of the conventional PID method and the nonlinear adaptive control strategy were 1.8480 and 1.3342, respectively. The experimental results fully prove that the presented control scheme achieves better control performance, which meets the actual requirements. Full article
(This article belongs to the Special Issue Advances in Stochastic System Modeling, Control, Optimization, and Their Applications)
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