This paper addresses the bipartite consensus control problem for a class of discrete-time stochastic networked multi-agent systems (S-NMASs) under an observer-based cloud controller (CC) with multiplicative noise and bounded exogenous disturbances. Agents interact through both cooperative and antagonistic relationships. To mitigate network bandwidth limitations, a stochastic communication protocol (SCP) is applied from each agent’s sensor to the CC and a dynamic event-triggered mechanism (DETM) is implemented from the CC to the actuator. A new concept of quasi-bipartite consensus (QBC) is introduced to evaluate the consensus of S-NMASs in a probabilistic sense. Based on switched Lyapunov functions and stochastic analysis, sufficient conditions are established for the S-NMASs to achieve QBC when the transition probabilities of the SCP are either fully known or partially unknown. Explicit expressions for CC gains and observer gains are obtained via linear matrix inequalities. The effectiveness of the developed theory and the advantages of the proposed methods (DETM, SCP) are validated through simulation examples.

Remotely accessible control system laboratories remain difficult to extend and administer, particularly when new experiment hardware must be integrated without disrupting an existing infrastructure. This paper presents two architectural contributions to Mobile Networked Control System (MNCS) Lab, a mobile-oriented remote laboratory platform. First, a Remote Laboratory Administration Server (RLAS) built on Laravel 12 and hosted at https://mncs.online handles user authentication, time-slot scheduling, and resource management through a REpresentational State Transfer (REST) Application Programming Interface (API). An integrated Progressive Web App (PWA) dashboard lets administrators configure and register new experiment units directly from the management interface. Second, the MNCS Gateway is a locally deployed server utility that connects any hardware or virtual instrument with a Transmission Control Protocol (TCP) or User Datagram Protocol (UDP) port to the MNCS Lab platform, making it available under a dedicated public subdomain and validating every incoming command against RLAS-issued reservation tokens. The Air Bearing Spacecraft (ABS) platform at Harbin Institute of Technology, China (HIT) validates the efficacy of the system, confirming correct access enforcement, real-time command delivery, and closed-loop control through an Android-native Mobile Experiment Terminal (MET).

DC microgrids play a crucial role in distributed energy systems, yet the integration of nonlinear dynamics and uncertain constant power loads (CPLs) introduces severe stability challenges. In this paper, a fully actuated control structure is designed for the nonlinear segments of the DC microgrid, which significantly simplifies the subsequent controller design process by leveraging the inherent advantages of fully actuated system approaches. To achieve the transformation toward a fully actuated system, an energy-related variable is specifically defined, which reconfigures the original nonlinear system dynamics into a fully actuated form. Aiming at resolving the issue of unknown CPLs in the microgrid, an observer-based fully actuated control method is further proposed, where the observer estimates the unknown load information in real time, thereby compensating for the uncertainties induced by unknown CPLs. This method provides a simple and direct design framework for nonlinear DC microgrids with unknown loads, avoiding the laborious parameter tuning and high design overhead typical of many nonlinear control strategies. Finally, experimental results validate the effectiveness of the proposed method in suppressing voltage oscillations, maintaining system stability, and adapting to variations in unknown CPLs.

This article presents a digital twin (DT)-integrated remote laboratory for synchronous generators to enhance electrical engineering education. It is developed from the Mathematical to Practice Laboratory (M2PLab), a universal simulation and rapid control prototyping platform. Based on a three-phase excitation synchronous generator, the laboratory employs a five-layer architecture upon M2PLab's core functions, transforming it into a specialized teaching system for electrical machinery. Graphical algorithm design and automatic code generation are supported. The same control algorithm can be seamlessly deployed to both physical and virtual controllers. A high-fidelity mathematical model is built via parameter identification with the particle swarm optimization algorithm. An interactive 3-D model, driven by real-time data, enhances visualization and operational monitoring. The proposed laboratory supports simulation-based short circuit tests and virtual-physical synchronized experiments for excitation control and speed regulation, while also encouraging students to conduct innovative experiments. Experimental cases and students’ feedback validate the laboratory's effectiveness. This work provides a practical and scalable approach for deeply integrating DT technology into electrical engineering education, enhancing both safety and accessibility.

Dear Editor, Based on the fully actuated system (FAS) theory, this letter aims at a human-in-the-loop control (HLC) problem for industrial Internet of Things (IoT) systems. The main role of HLC is to directly manipulate the tracking target by sending a control command of human operators to the non-zero input of the target. A modified FAS predictive control with an output prediction increment introduced into a cost function is proposed to address this problem for optimizing the tracking control and improving the dynamic performance. Further analysis derives a sufficient condition on the bounded stability and tracking performance of the closed-loop systems. An example of the heading angle control of the Nomoto model-based ship is shown to verify the feasibility.

With the increasingly integrated nature of networked control systems (NCSs), security has become a challenging issue for their widespread deployment. Although resilient control methods against various attacks have been reported, the analysis and design of defense mechanisms for NCSs still require fresh efforts. To this end, this article is concerned with the security control of a class of NCSs vulnerable to smart false data injection (FDI) attacks. Specifically, the scenario of output tracking of NCSs is considered, where the communication between sensors and controllers, as well as between controllers and actuators, is compromised by sophisticated malicious adversaries. To enhance security, peer-to-peer (P2P) networks with blockchain technologies are utilized instead of traditional communication patterns to transmit measurement and control signals. Unlike previous work, this work carefully designs an optimal blockchain consensus policy by perceiving the performance of NCSs and develops a resilient dynamic output tracking controller based on this policy. The formulation of the consensus policy is derived from a game-theoretic framework that models the interaction between the blockchain and the malicious adversary, enabling deep integration of blockchain technology with NCSs. With the proposed approach, the adverse effects of malicious FDI attacks can be greatly mitigated by balancing energy consumption and tracking performance. Finally, the applicability of the proposed security control strategy is verified in a real-world power system.

The aluminum annealing furnace (AAF) is a large, energy-intensive industrial equipment widely used in manufacturing. Accurate prediction of its power consumption is crucial for optimizing energy management. However, conventional prediction methods often face challenges due to the furnace's multi-day production cycles, coupled operating conditions, and complex thermal interactions. To overcome these challenges, this paper proposes a digital twin (DT)-based approach for high-fidelity power consumption prediction of AAFs. First, a DT-empowered prediction framework is proposed, which establishes a closed-loop interaction between physical entities and virtual models through four synergistic layers: physical equipment, data integration, DT simulation, and application services. Within this framework, detailed power consumption profiles are generated via DT simulations that replicate the AAF's production process in advance, utilizing both the twin model and production data. The AAF DT model is developed using a CNN-BiLSTM-Attention network, effectively capturing the nonlinear dynamics of power consumption. Furthermore, an incremental learning strategy is implemented to continuously refine the model with increasing data, ensuring adaptability to varying production scenarios. Finally, a real-world case from an aluminum manufacturing facility is provided to validate the proposed approach. Experimental results demonstrate its superior performance, with all R2 values exceeding 0.96, consistently outperforming other baseline models.

This study considers the current tracking control of the LCL-type grid-connected inverters by using a fully actuated system (FAS) approach. To tackle this problem, a FAS approach-based predictive proportional-integral (PI) control is constructed with simple design and better performance. Firstly, an equivalent transformation is presented to convert the model of the LCL-type grid-connected inverters and its general form into the input-delay FASs, so that the proposed work can be regarded as the tracking control of the input-delay FASs. Secondly, a predictive PI control is developed by means of the FAS approach to realize the desired tracking control for eliminating the original nonlinearities and compensating for the input delays. Then, a sufficient criterion is proposed for the bounded stability and tracking performance of the closed-loop systems. Finally, a compared simulation is shown to demonstrate the feasibility.

As distributed energy sources are increasingly integrated into power grids, centralized optimization approaches face challenges including limited robustness, high communication and computational overhead, and privacy concerns. Distributed economic dispatch algorithms, leveraging edge computing for local optimization and inter-agent communication for collaborative decision-making, offer a promising solution. However, existing methods often struggle to solve large-scale mixed-integer quadratic programming (MIQP) problems, time-coupled constraints, and plug-and-play optimization in vehicle-to-grid (V2G) systems. To address these challenges, this study simplifies the V2G system, modeled as an MIQP problem, into a convex quadratic optimization problem, and combines the alternating direction method of multipliers (ADMM) with model predictive control (MPC) to enable distributed solutions and plug-and-play capabilities. Lagrange multipliers are extended to a vector form to manage time-state coupling constraints. Physical constraints, including the constant current-constant voltage (CC-CV) charging mode and charge-discharge efficiency factors, are incorporated to enhance battery lifespan. A dispatch packet strategy reduces communication and computational overhead. Simulation and physical experiments demonstrate the proposed approach's feasibility and effectiveness.

Networked multiagent control systems (NMCSs) with predictive proportional-integral-derivative (PPID) tracking are convenient, improve multiagent system consistency and address communication delays. However, they may be vulnerable to attacks. Blockchain can improve security but introduces delays. Therefore, an Ethereum blockchain-based PID predictive tracking networked multiagent control system (EPIDP-NMCS) is proposed to increase resilience and reduce impacts on multiagent tracking. Predictive-error practical Byzantine fault tolerance (PE-PBFT) with Ethereum increases the commit probability by 25% without prior knowledge, improving attack resistance. Navigation predictive PID (NPPID) and active delay compensation quickly reduce the impacts of delays while maintaining NMCS control, and NPPID is stable and consistent in NMCSs. An experimental prototype is built and tested under false data injection attacks. The proposed method practically and feasibly improves NMCS security and reliability.

This paper considers a fault-tolerant tracking control problem of discrete-time nonlinear fully actuated systems (FASs) with actuator and communication-link failures. A fault observer-based predictive proportional-integral (PI) control is presented to solve this problem with better performances in the fault estimation and the tracking control. Concretely, a discrete-time FAS model of nonlinear systems with actuator and communication-link failures is firstly given as a control-oriented basic one. Then, a fault observer is developed to achieve the accurate estimation of the unknown but bounded total failures. By using a FAS method, a predictive PI control is designed to realize the desired tracking control with eliminating the original open-loop nonlinearities and compensating for the transmission delays caused by the communication-link failures. A sufficient criterion is further constructed for the bounded stability and tracking performance of the closed-loop FASs. Finally, the spacecraft rendezvous tracking trajectory is provided to demonstrate the feasibility of the proposed approach.

This paper investigates the relay cooperative tracking problem of networked nonlinear multi-agent systems under network delays, and introduces an event-triggered control strategy to reduce communication transmission. When the target crosses the monitoring area, the nearby agents will capture its distance from the target. The purpose of this study is to track targets traversing the monitoring area using a relay cooperative tracking strategy. To achieve more efficient target tracking by the agent, this paper designs a dynamic event-triggered mechanism on own error of the agent rather than errors between agents. This mechanism reduces the communication transmission between agents, thereby avoiding the impact of network delay in agent communication. The results show that the proposed relay event-triggered control strategy effectively enhances the tracking performance of the pursuing agents with respect to the target and significantly reduces the communication burden on the network. Additionally, the strategy incorporates a delay compensation mechanism to mitigate the effects of communication delays. Finally, the effectiveness of the proposed method is validated through simulation experiments.

Networked predictive control (NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems (NCSs), such as network-induced delays, packet dropouts, and packet disorders. Despite significant advancements, the increasing complexity and dynamism of network environments, along with the growing complexity of systems, pose new challenges for NPC. These challenges include difficulties in system modeling, cyber attacks, component faults, limited network bandwidth, and the necessity for distributed collaboration. This survey aims to provide a comprehensive review of NPC strategies. It begins with a summary of the primary challenges faced by NCSs, followed by an introduction to the control structure and core concepts of NPC. The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control, fault-tolerant control, distributed coordinated control, and event-triggered control. Moreover, it reviews notable works that have implemented these schemes. Finally, the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts.

Modern Networked Control Systems (NCSs) leverage wireless sensors and actuators, enabling smartphones to function as capable control terminal for remote laboratories. However, existing web-based and native mobile interface approaches face several challenges, including under utilization of device resources, fragmented workflows across disparate systems, reliance on root privileges, and restrictions on accessibility and usability. In this paper, we propose a novel unified native Android-based application and framework named Mobile Controller and Supervisory Client (MCSC), which transforms Android based normal smartphones into comprehensive remote laboratory terminals, operating as efficient mobile controllers. It integrates visual algorithm design, real-time control process execution, and data analysis into a single platform. The proposed MCSC deals with accessibility barriers by integrating visual algorithm design, real-time control execution, and data analysis within a single client application that implements touch-optimized user interface. Built on a three-tier distributed architecture, MCSC offloads compilation tasks to Servers Tier while a Mobile Algorithm Run-Time (MART) service executes algorithms locally without elevated privileges. The system features a block-based programming interface with 80+ functional blocks, customizable supervisory dashboards, and responsive operations via Kotlin coroutines. Extensive experiments on an Air-Bearing Spacecraft (ABS) simulator platform demonstrate that the proposed system achieves average control loop response times of 336.777 ms in a local wireless network. Moreover, user evaluation with 32 engineering students achieved 89.26% satisfaction for technical performance and 85.44% for interface usability. The results indicate that the MCSC system can successfully provide real-time remote laboratory access while maintaining technical rigor for advanced control systems applications.

This article focuses on the energy management problem of microgrids with battery energy storage systems. The primary objective of this work is to develop a distributed algorithm over time-varying networks to real-time regulate the power output of dispatchable generators and energy storage devices, thereby achieving a balance between supply and demand. To accomplish this, the offline time-coupled optimization problem is initially relaxed to a time-average form and subsequently decoupled into a time-independent problem through the use of the Lyapunov optimization technique. An analytical framework is provided to demonstrate that the battery energy constraints can be satisfied by appropriately selecting parameters. Subsequently, a consensus-based distributed algorithm is formulated over time-varying communication topologies, with its linear convergence proven using the small-gain theorem. Finally, case studies are presented to validate the theoretical results.

This article addresses the distributed economic dispatch (ED) issue of microgrids. The primary objective of this study is to derive a distributed optimization algorithm with a compressed communication scheme over directed networks. Specifically, the algorithm aims to solve the ED problem, where the total power generation of distributed energy resources (DERs) is dispatched to meet the overall demand at the minimum operational cost under DER capacity constraints. To improve communication efficiency, a novel data compressed transmission mechanism is introduced into the consensus-based distributed algorithm by constructing estimator-like equations. Furthermore, by resorting to the property of matrix norms and system theory, a sufficient condition is derived to ensure that the proposed algorithm linearly converge to the optimal solution under arbitrary compression rate. This condition explicitly depends on the communication topologies and the algorithm parameters but is independent of the compression rate. Finally, simulated examples are provided to validate the theoretical claims and demonstrate the performance of the proposed algorithm.

This paper proposes a networked active fault-tolerant predictive control strategy based on an adaptive event-triggered mechanism for networked multi-agent systems subject to simultaneous actuator and sensor faults. First, a state and fault estimator is designed to estimate the system states as well as the actuator and sensor faults under the double-fault scenario. Second, an adaptive event-triggered mechanism is developed to reduce the communication frequency and alleviate the communication burden of the system. Then, a fault-tolerant control law is constructed to achieve active fault-tolerant control for multi-agent systems with double faults. Finally, a delay compensator is introduced to address the network-induced delay problem. The proposed strategy effectively compensates for communication delays and reduces the occupation of network resources.

By means of a fully actuated system (FAS) approach, this article is concerned with an anti-disturbance tracking control problem toward a class of lumped disturbances containing the model uncertainties and external disturbances. A FAS predictive control with a generalized proportional-integral observer (GPIO) is presented to address this problem. Concretely, a FAS model of discrete-time nonlinear systems with the lumped disturbances is firstly given as a control-oriented one. Then, a GPIO is developed to achieve an accurate estimation for the lumped disturbances by adopting a less conservative disturbance assumption, which provides a better foundation to construct a disturbance preview. Furthermore, an incremental FAS (IFAS) prediction model with a disturbance preview is constructed by utilizing a new type of Diophantine Equation. Dependent on this IFAS prediction model, the multistep ahead predictions can be obtained to minimize an objective function to yield an optimal anti-disturbance controller, such that the desired tracking performance can be guaranteed. The depth analysis derives a sufficient condition for the bounded stability and tracking performance of the closed-loop FASs. The proposed GPIO-based FAS predictive control provides a solution to the spacecraft attitude control for verifying the feasibility.

This paper proposes a predictive control strategy for networked buck converter systems with time delays, leveraging fully actuated system (FAS) theory to address the control challenges caused by delays in networked environments. A higher-order dynamic model of the buck converter is derived using FAS modeling theory, enabling precise voltage regulation through state feedback and integral control. To compensate for time delays, a predictive control approach is designed by discretizing the FAS model and using recursive prediction to correct delayed states. Simulation results demonstrate that the proposed method achieves faster dynamic response, reduced overshoot, and improved robustness under sudden load changes compared to traditional PI control. Furthermore, the predictive controller effectively stabilizes the system under fixed network delays.

Motors are widely used in industrial applications and are key equipment to improve production efficiency. To ensure safety and reduce economic losses, an intelligent fault diagnosis system for motors is required. Traditional fault diagnosis methods are mainly based on vibration signal analysis, which are vulnerable to the interference of environment and noise, and lack of analysis of key motor electrical signals. This paper proposes a fault diagnosis method based on multivariable time series features image fusion of motor electrical signals. First, the electrical signals are collected by Hall sensor and multivariable features are extracted by feature engineering technologies. The sliding window strategy is used to divide the feature data into samples. Then, the extracted features are stacked into images to achieve multivariable features fusion. The Smote-Enn strategy is used to balance the proportion of different types of samples. Finally, Mobilenet-v2 is used to classify feature images, and focal-loss is used as a loss function during training to enhance the recognition ability of difficult to classify samples. The experimental results show that the proposed method has achieved a favorable fault diagnosis effect, providing an effective solution for the intelligent fault diagnosis of motor.