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Actuators
Special Issues
Resilient Control and Estimation in Networked Systems
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Resilient Control and Estimation in Networked Systems

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 8770

Special Issue Editors

Dr. Shichao Liu

E-Mail Website
Guest Editor
Department of Electronics, Carleton University, Ottawa, ON K1S5B6, Canada
Interests: modeling, stability analysis and control of cyber-physical systems; distributed control and distributed estimation; Nonlinear system control
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Chen

E-Mail Website
Guest Editor
Department of Automation, Zhejiang University of Technology, Hangzhou 310012, China
Interests: data fusion; estimation and decision theory; distributed estimation and control; cyber-physical systems security
Prof. Dr. Haikuo Shen

E-Mail Website
Guest Editor
Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Ministry of Education, Beijing 100044, China
Interests: monte carlo methods; distributed power generation; distribution networks;load flow;photovoltaic power systems;power generation scheduling; power grids
Prof. Dr. Jianxing Liu

grade E-Mail Website
Guest Editor
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150000, China
Interests: renewable energy systems; nonlinear control systems; intelligent systems; robot technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Information and communication technologies (ICTs) have been increasingly utilized to support the exchange of data among actuators, sensors, and controllers in networked control systems (NCSs), for which control loops are closed via communication links. Typical examples of NCSs include automatic control systems that monitor and control the operation of critical infrastructures such as electrical power systems, intelligent transportation systems, and water resource management systems. While the communication infrastructure significantly facilitates the transmission of the vast amount of data over wide geographical areas, it makes NCSs more vulnerable to cyberattacks, such as cyberattacks on sensors, actuators, and controllers. There are many motivations for launching cyberattacks on critical NCSs, such as criminal extortion and the creation of political terror. Cyberattacks against critical NCSs have risen rapidly since 2010, such as the 2015 and 2016 Ukrainian power blackouts. Despite its critical importance, research on protecting industrial control systems of critical infrastructures from cyberattacks is in its infancy. While existing cyber-system-based security tools, such as firewalls, whitelisting, and network segmentation, can reduce the risk of being attacked by hackers, they are not sufficient to secure critical industrial control systems as highly skilled adversaries have been found to be able to bypass host-based or network-based security measures. In order to secure NCSs, a variety of open challenges need to be tackled. For example, new cyberphysical system modeling methods should be studied to accurately understand the interaction of cybersystem events and physical system behaviors. Other vital components to enhance the resiliency of NCSs against cyberattacks include new intrusion detection systems, resilient estimation, and mitigation control algorithms, to name a few.

This Special Issue aims to seek state-of-the-art solutions to the open challenges in securing NCSs and applications in critical NCSs, such as intelligent transportation systems, smart grids, the Internet of Things, smart manufacturing, autonomous driving, smart buildings, smart homes, etc. Topics of interest may be related to but not limited to

  • Risk analysis of cyberattacks on actuators and sensors of NCSs;
  • Distributed estimation of NCSs under cyberattacks;
  • Secure wireless sensor and actuator networks;
  • Distributed control of NCSs under cyberattacks;
  • Resilient actuations of NCSs under cyberattacks;
  • Intrusion detection for NCSs;
  • Model predictive control for NCSs under cyberattacks;
  • Consensus of NCSs under cyberattacks;
  • Machine learning for cyber security of NCSs;
  • Applications and co-simulations of NCSs under cyberattacks.

Dr. Shichao Liu
Dr. Bo Chen
Prof. Dr. Haikuo Shen
Prof. Dr. Jianxing Liu
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
  • Cyberattacks
  • Actuators
  • Sensors
  • Resilient Control
  • Intrusion Detection
  • Model Predictive Control
  • Machine Learning
  • distributed estimation
  • wireless sensor networks

Published Papers (4 papers)

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Research

31 pages, 9806 KiB  
Article
Deep Reinforcement Learning for Stability Enhancement of a Variable Wind Speed DFIG System
by Rahul Kosuru, Shichao Liu and Wei Shi
Actuators 2022, 11(7), 203; https://0-doi-org.brum.beds.ac.uk/10.3390/act11070203 - 20 Jul 2022
Cited by 3 | Viewed by 1653
Abstract
Low-frequency oscillations are a primary issue for integrating a renewable source into the grid. The objective of this study was to find sensitive parameters that cause low-frequency oscillations and design a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent controller to damp the [...] Read more.
Low-frequency oscillations are a primary issue for integrating a renewable source into the grid. The objective of this study was to find sensitive parameters that cause low-frequency oscillations and design a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent controller to damp the oscillations without requiring an accurate system model. In this work, a Q-learning (QL)-based model-free wind speed DFIG was designed on the rotor-side converter (RSC), and a QL-based model-free DC-link voltage regulator was designed on the grid-side converter (GSC) to enhance the stability of the system. In the next step, the TD3 agent was trained to learn the system dynamics by replacing the inner current controllers of the RSC, which replaced the QL-based model. In the first stage, the conventional PSS and Proportional–Integral (PI) controllers were introduced to both the RSC and GSC. Then, the system was trained to become model-free by replacing the PSS and the PI controller with a QL algorithm under very small wind speed variations. In the second stage, the QL algorithm was replaced with the TD3 agent by introducing large variations in wind speed. The results reveal that the TD3 agent can sustain the stability of the DFIG system under large variations in wind speed without assuming a detailed control structure beforehand, while QL-based controllers can stabilize the doubly fed induction generator (DFIG)-equipped wind energy conversion system (WECS) under small variations in wind speed. Full article
(This article belongs to the Special Issue Resilient Control and Estimation in Networked Systems)
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12 pages, 297 KiB  
Communication
Nonlinear Gaussian Filter with Multi-Step Colored Noise
by Yidi Teng, Shouzhao Sheng and Yubin Zheng
Actuators 2022, 11(4), 103; https://0-doi-org.brum.beds.ac.uk/10.3390/act11040103 - 29 Mar 2022
Cited by 1 | Viewed by 1832
Abstract
Color noise is a special kind of noise often occurring in localization systems, and it is more suitable than the general Gaussian white noise to model time dependence due to time delay or high-frequency sampling. This paper derives a nonlinear Gaussian filtering framework [...] Read more.
Color noise is a special kind of noise often occurring in localization systems, and it is more suitable than the general Gaussian white noise to model time dependence due to time delay or high-frequency sampling. This paper derives a nonlinear Gaussian filtering framework for multi-step colored noise systems using noise whitening techniques and Bayes rule. Meanwhile, the cubature rule is used to solve the Gaussian-weighted integral in the proposed Gaussian filtering framework, resulting in an analytic form of posterior state estimate. Compared with the existing nonlinear filtering algorithms, the proposed method has obvious advantages in colored noise systems because it fully takes into account the time dependence of colored noise. Finally, the effectiveness and advantages of the proposed algorithm are verified with a classical target tracking system. Full article
(This article belongs to the Special Issue Resilient Control and Estimation in Networked Systems)
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15 pages, 458 KiB  
Article
Intermediate-Variable-Based Distributed Fusion Estimation for Wind Turbine Systems
by Shengwei Yang, Rusheng Wang, Jing Zhou and Bo Chen
Actuators 2022, 11(1), 15; https://0-doi-org.brum.beds.ac.uk/10.3390/act11010015 - 6 Jan 2022
Cited by 2 | Viewed by 1827
Abstract
In wind turbine systems, the state of the generator is always disturbed by various unknown perturbances, which leads to system instability and inaccurate state estimation. In this paper, an intermediate-variable-based distributed fusion estimation method is proposed for the state estimation problem in wind [...] Read more.
In wind turbine systems, the state of the generator is always disturbed by various unknown perturbances, which leads to system instability and inaccurate state estimation. In this paper, an intermediate-variable-based distributed fusion estimation method is proposed for the state estimation problem in wind turbine systems. By constructing an augmented state error system and using the idea of bounded recursive optimization, the local estimators and distributed fusion criterion are designed, which can be used to estimate the disturbance signals and system states. Then, the local estimator gains and the distributed weighting fusion matrices are obtained by solving the established convex optimization problems. Furthermore, a compensation strategy is designed by using the estimated disturbance signals, which can potentially reduce the influence of the disturbance signals on the system state. Finally, a numerical simulation is provided to show that the proposed method can effectively improve the accuracy of the estimation of the wind turbine state and disturbance, and the superiority of the proposed method is illustrated as a comparison to the Kalman fusion method. Full article
(This article belongs to the Special Issue Resilient Control and Estimation in Networked Systems)
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16 pages, 655 KiB  
Article
Multi-Agent Distributed Deep Deterministic Policy Gradient for Partially Observable Tracking
by Dongyu Fan, Haikuo Shen and Lijing Dong
Actuators 2021, 10(10), 268; https://0-doi-org.brum.beds.ac.uk/10.3390/act10100268 - 14 Oct 2021
Cited by 9 | Viewed by 2278
Abstract
In many existing multi-agent reinforcement learning tasks, each agent observes all the other agents from its own perspective. In addition, the training process is centralized, namely the critic of each agent can access the policies of all the agents. This scheme has certain [...] Read more.
In many existing multi-agent reinforcement learning tasks, each agent observes all the other agents from its own perspective. In addition, the training process is centralized, namely the critic of each agent can access the policies of all the agents. This scheme has certain limitations since every single agent can only obtain the information of its neighbor agents due to the communication range in practical applications. Therefore, in this paper, a multi-agent distributed deep deterministic policy gradient (MAD3PG) approach is presented with decentralized actors and distributed critics to realize multi-agent distributed tracking. The distinguishing feature of the proposed framework is that we adopted the multi-agent distributed training with decentralized execution, where each critic only takes the agent’s and the neighbor agents’ policies into account. Experiments were conducted in the distributed tracking tasks based on multi-agent particle environments where N(N=3,N=5) agents track a target agent with partial observation. The results showed that the proposed method achieves a higher reward with a shorter training time compared to other methods, including MADDPG, DDPG, PPO, and DQN. The proposed novel method leads to a more efficient and effective multi-agent tracking. Full article
(This article belongs to the Special Issue Resilient Control and Estimation in Networked Systems)
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