Resilient Estimation, Control, Optimization, and Their Applications in Positive Networked Systems

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

Deadline for manuscript submissions: closed (15 September 2024) | Viewed by 6351

Special Issue Editors

College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Interests: positive networked systems; multi-agent systems; resilient control and distributed optimization

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Guest Editor
Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China
Interests: complex networks; networked control systems; multi-agent systems; positive systems; robust control and filtering; switched systems; vibration control
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK
Interests: robust control; networked control systems; large-scale complex dynamic network; distributed control/optimization; multiplier-based and dissipativity-based analysis
Guangzhou Institution of Technology, Xidian University, Guangzhou 510555, China
Interests: resilient control of multi-agent systems; distributed optimization; game theory and their applications in UAV platforms

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Guest Editor
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: multi-agent systems; distributed estimation and control; machine learning; autonomous vehicles; secure control

Special Issue Information

Dear Colleagues,

In recent years, estimation and cooperative control for networked systems have obtained increasing interest due to their wide employment in distributed sensor networks, electric power networks, unmanned mobile swarms, and intelligent vehicle formation. Among these practical networks, there exists a specific kind of networked system with non-negative state variables. Take the susceptible-infected-susceptible model for example, which is usually used to describe networked epidemic processes. Since variables such as the infection rate and the recovery rate are naturally non-negative in the infectious disease model, the positive network model can be used to describe the dynamic characteristics of the infectious disease network. Positive networked systems also have extensive applications in the modeling of compartmental networks, logical networks, traffic flow control, and gene regulatory networks.

Due to the large scale of networks, it is very important to estimate the state of nodes in the network effectively. Distributed state estimation is widely used in a networked system to estimate the state information of the tracking targets in real-time via the communication topology. The distributed nature of the network can effectively reduce communication costs and the probability of failure, thereby greatly improving system efficiency and robustness. On the other hand, networked systems are particularly vulnerable to external attacks. Various kinds of attacks could jeopardize the security, robustness, resiliency, safety, and information integrity of the networks, including replay attacks, denial-of-service attacks, false-data injection attacks, camouflage attacks, and actuation attacks. To resist the above attacks against distributed systems, two methodologies have been proposed, that is, diagnosis-based defense and attack-resilient defense. Diagnosis-based defense is more conventional, which detects, identifies, and removes (or recovers) the compromised nodes and/or edges on the topology of networks. In contrast, attack-resilient defense does not need a diagnosis process while maintaining an acceptable system performance against malicious attacks, which has been studied extensively in recent years.

Though results about resilient estimation, control, and optimization have been reported successively, little attention has been attracted to positive networked systems. For example, the resilient estimation and control of networked epidemic processes are very sensitive to unintentional or malicious intruders from outside regions, which have been widely reported in the media. Moreover, network optimization, such as the optimal allocation of resources at each node, is also worthy of further discussion and analysis.

The primary goal of this Special Issue is to disseminate the latest findings, new research developments, and future trends and innovations in the estimation, control, and optimization of positive systems, positive networked systems, and their applications in electric circuits. Both theoretical and experimental studies are encouraged. Moreover, high-quality reviews and survey papers are welcomed.

The submitted papers may focus on, but not necessarily be limited to, the following areas:

  • Robust, adaptive, and intelligent estimation of positive networked systems;
  • Safety-critical control of positive networked systems subject to various constraints;
  • Resilient and network-based estimation and control of positive networked systems subject to various network attacks;
  • Prescribed-time and finite-time control of positive networked systems;
  • Distributed control and optimization of positive networked systems;
  • Distributed Nash equilibrium of games between positive networked systems;
  • Fault detection filtering and fault tolerant control of positive networked systems;
  • Applications in electric circuits such as energy-efficient resource allocation, network capacity optimization, maximization of network sum rate, and so on.

Dr. Yukang Cui
Prof. Dr. James Lam
Dr. Lanlan Su
Dr. Xin Gong
Dr. Ahmadreza Jenabzadeh
Guest Editors

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Keywords

  • positive networked systems
  • multi-agent systems
  • resilient control and distributed optimization
  • robust control and filtering
  • stability of dynamic systems
  • networked control systems
  • positive systems
  • genetic regulatory networks
  • vibration control

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Published Papers (4 papers)

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Research

19 pages, 12776 KiB  
Article
Advanced 3D Navigation System for AGV in Complex Smart Factory Environments
by Yiduo Li, Debao Wang, Qipeng Li, Guangtao Cheng, Zhuoran Li and Peiqing Li
Electronics 2024, 13(1), 130; https://doi.org/10.3390/electronics13010130 - 28 Dec 2023
Cited by 3 | Viewed by 2585
Abstract
The advancement of Industry 4.0 has significantly propelled the widespread application of automated guided vehicle (AGV) systems within smart factories. As the structural diversity and complexity of smart factories escalate, the conventional two-dimensional plan-based navigation systems with fixed routes have become inadequate. Addressing [...] Read more.
The advancement of Industry 4.0 has significantly propelled the widespread application of automated guided vehicle (AGV) systems within smart factories. As the structural diversity and complexity of smart factories escalate, the conventional two-dimensional plan-based navigation systems with fixed routes have become inadequate. Addressing this challenge, we devised a novel mobile robot navigation system encompassing foundational control, map construction positioning, and autonomous navigation functionalities. Initially, employing point cloud matching algorithms facilitated the construction of a three-dimensional point cloud map within indoor environments, subsequently converted into a navigational two-dimensional grid map. Simultaneously, the utilization of a multi-threaded normal distribution transform (NDT) algorithm enabled precise robot localization in three-dimensional settings. Leveraging grid maps and the robot’s inherent localization data, the A* algorithm was utilized for global path planning. Moreover, building upon the global path, the timed elastic band (TEB) algorithm was employed to establish a kinematic model, crucial for local obstacle avoidance planning. This research substantiated its findings through simulated experiments and real vehicle deployments: Mobile robots scanned environmental data via laser radar and constructing point clouds and grid maps. This facilitated centimeter-level localization and successful circumvention of static obstacles, while simultaneously charting optimal paths to bypass dynamic hindrances. The devised navigation system demonstrated commendable autonomous navigation capabilities. Experimental evidence showcased satisfactory accuracy in practical applications, with positioning errors of 3.6 cm along the x-axis, 3.3 cm along the y-axis, and 4.3° in orientation. This innovation stands to substantially alleviate the low navigation precision and sluggishness encountered by AGV vehicles within intricate smart factory environments, promising a favorable prospect for practical applications. Full article
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14 pages, 417 KiB  
Article
Fault-Tolerant Consensus Control of Positive Networked Systems
by Zhansheng He and Jun Shen
Electronics 2023, 12(23), 4789; https://doi.org/10.3390/electronics12234789 - 26 Nov 2023
Cited by 1 | Viewed by 799
Abstract
In this paper, we explore the consensus of positive networked systems with actuator faults. Firstly, the undirected and strongly connected topology is established with graph theory. The positive system theory is used to analyze the positive consensus of the closed-loop networked systems. State [...] Read more.
In this paper, we explore the consensus of positive networked systems with actuator faults. Firstly, the undirected and strongly connected topology is established with graph theory. The positive system theory is used to analyze the positive consensus of the closed-loop networked systems. State feedback gains are derived utilizing Algebraic Riccati Inequalities. Bounded multiplicative faults are regarded as uncertainties in the system matrix, while treating additive faults as external disturbances. Further, this transformation refocuses the analysis on the consensus problem with an L2-gain. Subsequently, the Genetic Algorithm is employed to optimize the L2 performance criteria. Finally, the effectiveness of the proposed theory is validated through simulations involving both single-input electric circuit systems and multi-input networked systems. Full article
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11 pages, 454 KiB  
Communication
Positivity and Stability of Fractional-Order Coupled Neural Network with Time-Varying Delays
by Jiyun Gong, Hongling Qiu and Jun Shen
Electronics 2023, 12(23), 4782; https://doi.org/10.3390/electronics12234782 - 26 Nov 2023
Viewed by 949
Abstract
This brief paper analyzes the positivity and asymptotic stability of incommensurate fractional-order coupled neural networks (FOCNNs) with time-varying delays. Under a reasonable assumption about the activation functions of neurons, a sufficient and necessary condition is proposed to guarantee that FOCNNs are positive systems. [...] Read more.
This brief paper analyzes the positivity and asymptotic stability of incommensurate fractional-order coupled neural networks (FOCNNs) with time-varying delays. Under a reasonable assumption about the activation functions of neurons, a sufficient and necessary condition is proposed to guarantee that FOCNNs are positive systems. Furthermore, the sufficient and necessary condition ensuring the asymptotic stability of FOCNNs is also given via introducing a linear auxiliary system. Finally, a simulation experiment was carried out to justify the effectiveness of the derived results. Full article
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14 pages, 4139 KiB  
Article
Extended State Observer-Based Predictive Current Control for Dual Three-Phase PMSM with High Dynamic Performance
by Huanli Liu, Dayu Luo and Weiyang Lin
Electronics 2023, 12(20), 4266; https://doi.org/10.3390/electronics12204266 - 15 Oct 2023
Viewed by 1071
Abstract
Model predictive controllers are widely discussed in the field of dual three-phase permanent magnet synchronous motor control. However, conventional predictive current controllers usually suffer from parameter inaccuracies or model uncertainties, resulting in prediction errors and deterioration of control performance. Therefore, in this paper, [...] Read more.
Model predictive controllers are widely discussed in the field of dual three-phase permanent magnet synchronous motor control. However, conventional predictive current controllers usually suffer from parameter inaccuracies or model uncertainties, resulting in prediction errors and deterioration of control performance. Therefore, in this paper, an extended state observer-based (ESO) model predictive current controller is proposed to effectively improve the dynamic performance of the motor and its robustness to parameters or disturbances. Parameter inaccuracies or model uncertainties are considered to be lumped disturbances and expressed in the modified mathematical model of the motor. Then, with the designed observer estimating the external disturbances in real time, the prediction error is compensated and corrected periodically. Additionally, the parameter design method of the observer is presented to simplify the controller design. Finally, comparative experiments are implemented to sufficiently demonstrate the effectiveness of the proposed method for dynamic performance improvement as well as for parameter robustness. The results show that the proposed method takes only 17μs of computation time with a closed-loop bandwidth of 1839rad/s. In addition, the maximum d-axis following error of the proposed method is only 0.10A in the load dynamics experiments, which is a significant improvement compared to the 0.79A of the traditional proportional-integral controller. Full article
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