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.

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 article proposes a resilient distributed model predictive control (DMPC) algorithm for a class of constrained dynamically coupled multiple cyber-physical systems (CPSs) subject to bounded additive disturbances. The algorithm is designed to address severe attacks on the forward controller-actuator (C-A) channel, the feedback sensor-controller (S-C) channel, and the channels between subsystems, without any prior information about the intruder available to the defender. To mitigate the negative effects of intruders, we consider a one-step time delay strategy in the local model predictive controller design. This strategy allows the generated controller data to be checked for acceptability before use. To ensure constraint satisfaction for an infinite-horizon MPC problem while accounting for the unknown duration of attacks, we develop a set of minimally conservative constraints in the open-loop control mode using a constraint tightening technique. Moreover, we obtain an equivalent finite number of constraints for the infinite-horizon problem to ensure recursive feasibility. To prevent tampered data from affecting control performance, a detector module is designed to decide whether data is used by its receiver. It is shown that the closed-loop system is uniformly ultimate boundedness (UUB) under any admissible attack scenario and disturbance realization. Finally, the effectiveness of the proposed algorithm is validated by a case study.

The modeling problem of converter-based multibus direct current (DC) microgrids (MGs) and the conflict between voltage regulation and current balancing in such MGs have been a hot topic of interest. Voltage regulation is essential for ensuring the stability and power quality of MGs, while current sharing is a reflection of the MGs' ability to coordinate power and is critical to extend the lifespan of the generation units. However, due to the presence of line impedance, currents no longer have the freedom of regulation under consistent voltages across the buses. Additionally, existing models have failed to strike a good balance between accuracy and simplicity in describing DC MGs, resulting in rare research on model-based secondary control. With this in mind, this article develops a DC MG error model containing the dynamics of both the circuit and inner control loops via the fully actuated system theory. Further, a distributed optimal control is proposed based on this model. Compared to existing studies, the suggested error model captures the power characteristics of MGs while possesses a simple structure. For regulation tasks of voltage recovery and precise current allocation, this article unifies these two into a single integrated regulation error, offering a novel approach to address their conflict. Subsequently, the stability of the closed-loop MG system is given. Furthermore, this article includes a consensus analysis of current sharing and a tracking analysis of the average voltages. Finally, a laboratory-scale MG prototype equipped with photovoltaics and batteries is developed to validate the effectiveness of the proposed method.

On the basis of fully actuated system (FAS) method, this research focuses on the solution to a secure tracking control problem of cyber-physical systems (CPSs) under a type of hybrid attacks, where a unified framework of hybrid attacks is given to indicate the impacts of random denial-of-service attacks and random false data injection attacks in the forward and backward channels. A FAS terminal sliding-mode predictive control scheme is proposed to achieve the desired secure tracking control performance. First of all, a FAS model of CPSs is constructed to describe the actual dynamics, which is named the fully actuated cyber-physical system (FACPS). Then, a terminal sliding-mode is introduced to defend the hybrid attacks by enhancing the system robustness, and an incremental FAS prediction model of terminal sliding-mode is established via a Diophantine Equation. Through this incremental FAS prediction model, the multistep predictions of terminal sliding-mode are constructed to minimize an objective function for obtaining an optimal secure tracking controller. A sufficient condition of the closed-loop FACPS is derived to discuss the bounded stability and tracking performance with the help of the linear matrix inequality approach. Finally, the proposed FAS terminal sliding-mode predictive control scheme offers a solution to the tracking control of air-bearing spacecraft simulator for verifying the feasibility and practicality.

This article addresses the privacy-preserving energy management problem of battery energy storage systems (BESSs). An autonomous privacy-preserving distributed optimization (APPDO) scheme is developed over time-varying networks with the aim of regulating the power output of local BESS to fulfill the total load demand at the minimum economic cost under battery capacity constraints without privacy leakage. To this end, a linearly convergent distributed algorithm is proposed by combining the gradient descent algorithm with leaderless and leader-following consensus schemes. This algorithm is applicable to both islanded and grid-connected modes of BESSs. Furthermore, a novel privacy-preserving approach is constructed by injecting well-designed perturbation sequences into the data exchanged between neighboring nodes, making it effective against malicious eavesdroppers. Furthermore, a comprehensive analysis framework is established to evaluate the convergence, optimality, and privacy-preserving performance of the APPDO algorithm. Finally, numerical studies are conducted to demonstrate the effectiveness of the developed APPDO scheme.

Microgrids have been identified as a viable solution to the integration of renewable distributed generations (DGs) into power systems, while the coordination of DGs is frequently hindered by nonideal communication networks. To eliminate the negative consequences of these communication constraints, this article proposes a distributed predictive-proportional-integral control strategy for dc microgrids subject to communication delays and packet dropouts. The proposed control method can achieve voltage regulation and proportional power sharing simultaneously in a distributed manner. To address the issues of time delay and packet loss, an active prediction method is recommended, in combination with the PID scheme, which makes use of the model information and historical data for predictive compensation as well as retain the benefits of PID controllers effectively. The closed-loop system of the dc microgrid under the proposed control scheme is presented for stability and consensus analysis. Finally, experimental results from a wind turbine-based dc microgrid system are provided to demonstrate the superiority and effectiveness of the proposed method.

This article addresses the cooperative tracking control problem of linear discrete-time heterogeneous networked multiagent systems suffering from random communication constraints. The communication constraints, including random network delays and packet losses, exist in the forward and feedback channels of each agent as well as the channels between agents. A dual-prediction-based cooperative control scheme with correction is proposed. Specifically, a dual-prediction generator is developed to compensate actively for the adverse effects of random communication constraints in the three communication channels. Besides, a correction item is introduced to ensure that the predicted value tends to the actual value. A novel cooperative tracking control law with proportional and integral action is designed for each agent. A sufficient condition is obtained for the derived closed-loop system to maintain stability. Comparative experimental results on a networked three-motor system demonstrate the effectiveness of the proposed method.

This article focuses on the consensus and tracking problem of multiagent systems under communication delays, and proposes an observer-based distributed predictive control scheme to actively compensate for communication delays. First, an auxiliary variable and its corresponding estimator are innovatively designed to provide an observation of a globally consensus value that converges faster than the actual system output. Then, a cost function is formulated for this observer, and the gain of the correction term is optimized in a data-driven way to enhance the accuracy of the estimation. Based on the auxiliary variables, a distributed predictive controller is presented. Unlike existing control schemes, the proposed controller compensates for the delayed data in an active way, and the rolling prediction process is carried out in a distributed manner, facilitated by the designed auxiliary variables. Case studies conducted in both numerical and industrial scenarios demonstrate the effectiveness of the proposed method.

Efficient energy optimization and scheduling in industrial factories depend on accurate, reasonable, and real-time monitoring of equipment power consumption. However, the power prediction of industrial equipment requires a large number of process data, which could be inevitably contaminated by some imperfect data, due to the harsh environments. Monitoring or predicting equipment power consumption is usually not feasible using data-driven black-box models with imperfect data. This paper proposes a prediction-based approach for power consumption monitoring using an interpretable data-driven model. First, a data preprocessing method is used to remove outliers and fill in missing values. Then, a Volterra polynomial basis function (VPBF) model is built to predict equipment power consumption. This model decomposes power values into a series of basis functions consisting of input parameters. Moreover, to compensate for data dropouts during the power consumption monitoring process, a networked predictive monitoring system is also proposed. Finally, this paper presents two case studies based on actual production equipment in an industrial manufacturing factory. The results demonstrate that the proposed approach can achieve satisfactory monitoring accuracy and adaptation ability.Note to Practitioners—This paper is motivated by the problem of power consumption monitoring of industrial equipment in harsh production environments. A conventional solution is to predict the power consumption using data-driven black-box models, with limited feasibility and interpretability. This paper proposes a new power consumption monitoring approach, utilizing an interpretable data-driven model and a networked predictive method. This approach accurately reveals the transparent relationships between the output and input parameters. The power prediction result is consequently interpretable and more reasonable. Meanwhile, this approach actively compensates for data dropouts in the network, which can help operators real-time grasp the power consumption of equipment. Furthermore, this approach can be integrated into energy optimization systems as a basis of optimization and decision-making. Practical applications in an aluminum manufacturing company located in Guangdong Province demonstrate that this approach is feasible and applicable. Future research is to expand this approach further for energy optimization and scheduling.

Timely delivery of first aid supplies is significant to saving lives when an accident happens. Among the promising solutions provided for such scenarios, the application of unmanned vehicles has attracted ever more attention. However, such scenarios are often very complex, while the existing studies have not fully addressed the trajectory optimization problem of multiple unmanned ground vehicles (multi-UGVs) against the scenario. This study focuses on multi-UGVs trajectory optimization in the sight of first aid supply delivery tasks in mass accidents. A two-stage completely decoupling fuzzy multiobjective optimization strategy is designed. On the first stage, with the proposed timescale involved tridimensional tunneled collision-free trajectory (TITTCT) algorithm, collision-free coarse tunnels are build within a tridimensional coordinate system, respectively, for the UGVs as the corresponding configuration space for a further multiobjective optimization. On the second stage, a fuzzy multiobjective transcription method is designed to solve the decoupled optimal control problem (OCP) within the configuration space with the consideration of priority constrains. Following the two-stage design, the computational time is significantly reduced when achieving an optimal solution of the multi-UGV trajectory planning, which is crucial in a first aid task. In addition, other objectives are optimized with the aspiration level reflected. Simulation studies and experiments have been curried out to testify the effectiveness and the improved computational performance of the proposed design.

The use of nonideal communication networks makes communication constraints become a topical issue in the research of dc microgrids. How to design a distributed secondary control scheme for voltage recovery and accurate current sharing in islanded dc microgrids subject to communication constraints is of interest in this article. In order to restore the bus voltage to the rated value, a nonlinear element is first introduced into the primary control layer. Then, the closed-loop system of primary control is modeled as a data-driven time-varying linear system. Based on the established model, considering communication constraints, a distributed secondary predictive control strategy is developed to achieve accurate current sharing. While actively compensating for network delays and packet losses, the proposed method renders mathematical physical models unnecessary for the traditional predictive control, and simultaneously completes the multitask in dc microgrids. Finally, several case studies are conducted on a hardware microgrid experimental platform, which not only verifies the effectiveness of the designed data-driven predictive control strategy but also tests microgrid properties such as the plug-and-play ability.

In this article, the consensus problem of sampled-data second-order integrator multiagent systems with switching topology and time-varying delay is studied. And, a zero rendezvous speed is not required in the problem. Two new consensus protocols that employ no absolute states are proposed, depending on the presence of delay. Sufficient synchronization conditions are obtained for both protocols. It is shown that consensus can be reached, provided there is a sufficiently small gain and periodically joint connectivity in the sense of scrambling graph or spanning tree. Finally, both numerical and practical examples are supplied for illustrative purpose, and both show the effectiveness of the theoretical results.

With the advancement of computing technology and big data technology, digital twins have gradually been applied in various fields, such as manufacturing, energy, and healthcare. This article studies the predictive control of nonlinear dynamic systems using digital twins. Based on a digital-twin control system framework, predictive control is discussed for three different nonlinear systems with time delays: 1) known nonlinear systems; 2) unknown nonlinear systems; and 3) unknown nonlinear cyber-physical systems. Both a digital-twin predictive control strategy and a digital-twin control predictor are proposed to compensate for time delays and communication delays actively. With the strategy and predictor, the digital-twin controller of a time-delay nonlinear system can be designed to achieve the desired performance based on the nonlinear system without time delays, which vastly simplifies the controller design procedure. A digital model is constructed using data to deal with unknown nonlinear dynamics. The three different closed-loop digital-twin predictive control systems are analyzed to derive a unified stability criterion. The simulation results show how the proposed digital-twin predictive control method performs well for nonlinear systems with time delays, unknown dynamics, and/or communication delays.

This research is intended to address a robust cooperative control problem of heterogeneous uncertain nonlinear high-order fully actuated multiagent systems (HUN-HOFAMASs). A nonlinear HOFA system model is used to describe the multiagent systems (MASs) with heterogeneous uncertain nonlinear dynamics, which is called the HUN-HOFAMASs. A predictive terminal sliding-mode control-based robust cooperative control scheme is presented to address this problem. In this scheme, heterogeneous nonlinear dynamics of original system are offset to establish a linear constant HOFA system with the help of full actuation feature. Then, a terminal sliding-mode variable for enhancing the system robustness is introduced to handle the uncertainties. Furthermore, a linear incremental prediction model is developed in a HOFA form by means of a Diophantine equation. According to this model, the multistep terminal sliding-mode predictions are yielded to optimize the robust cooperative control performance and compensate for the network-induced communication constraints in the feedback and forward channels. Based on a linear matrix inequality (LMI) method, a necessary and sufficient criterion is derived to discuss the simultaneous consensus and stability of closed-loop HUN-HOFAMASs. The simulation and comparison results of cooperative flying around of multiple spacecraft system are shown to illustrate the capability and advantage of the presented predictive terminal sliding-mode control for robust cooperative control.

This research addresses a coordinated control problem for high-order fully actuated networked multiagent systems (HOFANMASs) under random denial-of-service (DoS) attacks. A type of Bernoulli processes is exploited to denote the successful rate of launching random DoS attacks happened to the forward and feedback channels. When acting these attacks successfully, random data losses and disorders are caused in the forward and feedback channels. A high-order fully actuated (HOFA) secure predictive coordinated control scheme is provided to achieve the security coordination. In this scheme, a dynamic model of networked multiagent system is established with the help of a HOFA system model, which is called the HOFANMAS. Then, a prediction model in an incremental HOFA (IHOFA) form is developed by means of a Diophantine equation, which aims at constructing the multistep ahead output predictions for the optimization of coordinated control performance and the compensation of random data losses and disorders. Furthermore, a necessary and sufficient condition is proposed to analyze the consensus and stability of closed-loop HOFANMASs. The effectiveness and superiority of HOFA secure predictive control scheme can be demonstrated via simulated and experimental results of formation control for three air-bearing spacecraft (ABS) simulators.

In large-scale networks, communication links between nodes are easily injected with false data by adversaries. This paper proposes a novel security defense strategy from the perspective of attack detection scheduling to ensure the security of the network. Based on the proposed strategy, each sensor can directly exclude suspicious sensors from its neighboring set. First, the problem of selecting suspicious sensors is formulated as a combinatorial optimization problem, which is non-deterministic polynomial-time hard (NP-hard). To solve this problem, the original function is transformed into a submodular function. Then, we propose an attack detection scheduling algorithm based on the sequential submodular optimization theory, which incorporates expert problem to better utilize historical information to guide the sensor selection task at the current moment. For different attack strategies, theoretical results show that the average optimization rate of the proposed algorithm has a lower bound, and the error expectation is bounded. In addition, under two kinds of insecurity conditions, the proposed algorithm can guarantee the security of the entire network from the perspective of the augmented estimation error. Finally, the effectiveness of the developed method is verified by the numerical simulation and practical experiment.

This article is concerned with the privacy-preserving distributed economic dispatch problem of microgrids. The main goal of this work is to develop a privacy-preserving distributed optimization algorithm over directed networks, aiming to achieve supply-demand balance at the lowest economic cost under practical constraints while preventing the leakage of power-sensitive information. For this purpose, a distributed optimization algorithm with aconstantstep size is proposed by combining the decentralized exact first-order algorithm with the push-sum protocol, which offers an advantage in terms of fast convergence. In addition, to ensure privacy preservation, a state-decomposition approach is employed by randomly dividing the state into two parts, where only partial state information is transmitted. Moreover, the effectiveness of the privacy-preserving scheme against honest-but-curious nodes and external eavesdroppers is demonstrated through rigorous analysis. Finally, simulation studies demonstrate the validity and superiority of the developed privacy-preserving distributed algorithm.

This article is concerned with the secure distributed economic dispatch (DED) problem of microgrids. A quantized distributed optimization algorithm using the Paillier encryption–decryption scheme is developed. This algorithm is designed to optimally coordinate the power outputs of a collection of distributed generators (DGs) in order to meet the total load demand at the lowest generation cost under the DG capacity limits while ensuring communication efficiency and security. First, to facilitate data encryption and reduce data release, a novel dynamic quantization scheme is integrated into the DED algorithm, through which the effects of quantization errors can be eliminated. Next, utilizing matrix norm analysis and mathematical induction, a sufficient condition is provided to demonstrate that the developed DED algorithm converges precisely to the optimal solution under finite quantization levels (and even the three-level quantization usingsigntransmissions). Moreover, an encryption–decryption scheme is developed based on quantized outputs, which ensures confidential communication by leveraging the homomorphic property of the Paillier cryptosystem. Finally, the effectiveness and superiority of the implemented secure distributed algorithm are confirmed through a simulated example.

This article introduces an Internet of Things (IoT)-based solution to a multiagent system (MAS) experimentation with high space utilization and high flexibility. The solution has been applied in the Networked Control System Laboratory (NCSLab), which provides hundreds of educational equipment instances, offering abundant choices for MAS experiments. Based on the networked control theory and integrated circuit technology, a novel compact architecture for online laboratories is proposed first. Combined with multiple features of NCSLab, such as online algorithm design, parameter tuning, and live video monitor, researchers can conduct interactive verification. To achieve good dynamic performance and coordinated performance in a communication-constrained network, a predictive state feedback controller for MAS is proposed. Three web-based leader–follower MAS experiments are carried out to demonstrate the effectiveness of the proposed controller and powerful configurable ability for the MAS in NCSLab. The proposed IoT-based compact laboratory provides a cost-effective and space-efficient solution for other remote laboratories.

This article addresses the output tracking of networked high-order fully actuated (NHOFA) systems under random denial-of-service (DoS) attacks, where an independent Bernoulli process is used to denote the possibility of launching random DoS attacks in sensor to networked controller (SNC) and networked controller to actuator (NCA) channels. A successful attack leads to random data losses and disorders in the SNC and NCA channels. A secure predictive control method is given to implement the security tracking. In this method, a high-order fully actuated (HOFA) model is applied to establish the dynamics of networked control systems, which is called the NHOFA systems. Then, an incremental HOFA (IHOFA) prediction model is constructed via a Diophantine Equation, such that multistep output predictions are obtained to achieve the optimization of tracking performance and the compensation of random data losses and disorders. A simple condition is proposed to analyze the stability and tracking performance of closed-loop NHOFA systems. A tracking control experiment of air-bearing spacecraft simulator is taken to illustrate the feasibility of secure predictive control method.

With the development of mobile technologies, mobile applications are increasingly designed and developed. In the field of science, technology, engineering, and mathematics education, the idea of using mobile devices for teaching and learning has gradually gained popularity and gained in-depth research. Owing to the advantages of convenience, portability, and excellent interactive abilities, mobile devices are a good choice for accessing remote laboratories. In propose of promoting the massive online experimentation of control education, a mobile application with framework based on react-native is implemented in this article. The overall remote laboratory architecture adopts front-end and back-end separation scheme, which benefits the mobile application focusing on the construction of front-end interface and user experience. Various functional modules and technologies are applied in the mobile application, allowing users to access various experiment test rigs in the remote laboratory with intuitive experimental immersion. Moreover, a complete laboratory management system to manage the massive users from different universities has also been integrated into the mobile application. At the end, a remote networked control experiment has been conducted to prove the practicability of the mobile application.

This article investigates the privacy-preserving distributed economic dispatch (DED) problem of islanded microgrids. To improve the convergence rate of the DED algorithm, anacceleratedconsensus scheme is adopted by utilizing a short memory. Then, a privacy-preserving strategy is introduced to prevent sensitive information leakage by adding well-designed perturbations into the proposed consensus algorithm at the initial time instant. The primary objective of this article is to design a privacy-preserving accelerated consensus scheme to achieve a balance between supply and demand at the globally minimized cost while preserving the initial local demand information. By virtue of rigorous algebra manipulation and mathematical induction, a unified framework is established under which the convergence, the optimal convergence rate, and the optimality of the proposed DED algorithm are simultaneously analyzed, and the main results are extended to satisfy the privacy-preserving needs. Furthermore, the proposed privacy-preserving DED algorithm is shown to be resilient against both internal (honest-but-curious) and external eavesdroppers. Finally, the effectiveness of the developed privacy-preserving accelerated consensus algorithm is validated on the IEEE 39-bus power systems.

The study considers the strategies for multiple high-order integrator agents to reach an agreement using local measurements and local communications over networks with time-varying connectivity and delays. Two new consensus protocols are presented using only relative states and parameterized weights in polynomial form, one for networks where the connectivity is time varying, the other for networks where connectivity and delays are both time varying. Analysis shows the ensuring of consensus as well as the weights’ existence for both protocols, provided that the network connectivity is jointly and periodically kept as scrambling or with directed spanning tree. A new analysis method, termed as block seminorm is employed in the consensus analysis, and shows less conservative in parameter validation. Finally, both numeric simulations and experiments show the proposed consensus schemes’ effectiveness.

The technology of multiagent system (MAS) has been developed rapidly to fulfill the information interconnection nowadays. Meanwhile, implementing MAS-based projects is facing great challenges in workload and technologies while most work is still focused on simulation, implying the gap between the simulation and implementation. Aiming at this problem, this article proposes an all-in-one framework for the design, simulation, and implementation of MASs via network, based on a triple-layer data structure that includes overall topology, entity, and physical device. Through the proposed framework, MASs could be designed together with graphic, then corresponding simulation and multitask codes generation could be attained based on the designed model. A simultaneous multiapplication launching for distributed entities could be completed with instant automatic deployment eventually. The entire process provides a unified scheme for construction, verification, and practice of various MAS projects. Using such scheme, users could prototype and deploy MASs quickly with convenient operation, which allows more time and focus to be dedicated to the design and exploration of the control system itself. The practicability of the proposed framework has been verified by typical cases, based on which a more general solution could be improved and applied in the future.

This article investigates the coordinated control of networked high-order fully actuated multiagents (NHOFAMAs) under random deception attacks in the feedback and forward channels, where a Bernoulli process is used to denote the launching success rate of random deception attacks. When launching the attacks successfully, the output and control signals are tampered via the injection of false data. A predictive sliding-mode control method is proposed to achieve the security coordination. In this method, a sliding variable for the improvement of the robustness of closed-loop systems is introduced to defend random deception attacks. Then, a Diophantine equation is applied to develop a prediction model in an incremental high-order fully actuated (IHOFA) form. Based on this model, multistep ahead predictions of sliding variables are established to realize the optimization of coordinated control performance and the defense of random deception attacks. By utilizing the Lyapunov function and linear matrix inequalities (LMIs) approach, a necessary and sufficient condition is given to maintain the stability and consensus of closed-loop NHOFAMAs. The feasibility and practicability of the proposed method can be illustrated via simulated and experimental results of formation control for air-bearing spacecraft (ABS) simulators.

This article is concerned with the differentially private average consensus (DPAC) problem for a class of multiagent systems with quantized communication. By constructing a pair of auxiliary dynamic equations, a logarithmic dynamic encoding-decoding (LDED) scheme is developed and then utilized during the process of data transmission, thereby eliminating the effect of quantization errors on the consensus accuracy. The primary purpose of this article is to establish a unified framework that integrates the convergence analysis, the accuracy evaluation, and the privacy level for the developed DPAC algorithm under the LDED communication scheme. By means of the matrix eigenvalue analysis method, the Jury stability criterion, and the probability theory, a sufficient condition (with respect to the quantization accuracy, the coupling strength, and the communication topology) is first derived to ensure the almost sure convergence of the proposed DPAC algorithm, and the convergence accuracy and privacy level are thoroughly investigated by resorting to the Chebyshev inequality and ϵ -differential privacy index. Finally, simulation results are provided to illustrate the correctness and validity of the developed algorithm.

Security is a crucial issue for cyber-physical systems, and has become a hot topic up to date. From the perspective of malicious attackers, this article aims to devise an efficient scheme on false data-injection (FDI) attacks such that the performance on remote state estimation is degraded as much as possible. First, an event-based stealthy FDI attack mechanism is introduced to selectively inject false data while evading a residual-based anomaly detector. Compared with some existing methods, the main advantage of this mechanism is that it decides when to launch the FDI attacks dynamically according to real-time residuals. Second, the state estimation error covariance of the compromised system is used to evaluate the performance degradation under FDI attacks, and the larger the state estimation error covariance, the more the performance degradation. Moreover, under attack stealthiness constraints, an optimal strategy is presented to maximize the trace of the state estimation error covariance. Finally, simulation experiments are carried out to illustrate the superiority of the proposed method compared with some existing ones.

Modular dc–dc converters are popular in dc power systems, such as demagnetization systems due to their advantages of high efficiency, high reliability, and low current stress on components. However, the dynamic consensus of output currents of the multiple converter degaussing systems (MCDSs) has not been well solved yet, especially when communication delay is considered. In this article, a novel coordinated control method based on multistep state predictions with active compensation of delay is proposed to address the poor dynamic consensus performance of currents in MCDSs. The implementation of the scheme consists of the following two main phases: the design of a multistep state predictor for MCDSs and the optimization of the coordinating cost of currents. The multistep state predictor can estimate immeasurable future states of converters over a large horizon length, which presents a novel approach to the current regulator design. The optimal control protocol is derived by minimizing a distributed coordinating performance index function (PIF) for the output currents. This optimization of the PIF minimizes the consensus error between output currents for converters and compensates for communication constraints. Furthermore, the conditions for simultaneous stability and consensus of the closed-loop degaussing system with the distributed multistep state prediction controller are given. Finally, the performance of the proposed scheme is verified by numerous case studies in an experimental testbed.

This article focuses on the mean-square consensus control problem for a class of discrete-time multiagent systems (DT-MASs) over time-correlated multistate Markovian fading channels, where the packet loss probability is time-varying and depends on the current channel state. In order to save limited network bandwidth, a compressed coding scheme is developed by preprocessing the measurement output. With the aid of a stochastic Lyapunov–Krasovskii functional, a sufficient condition is first obtained under which the consensus error system is mean-square stable for DT-MASs over identical fading channels. Then, the consensus gain is formulated as the feasible solution to a set of linear matrix inequalities (LMIs) whose dimensions are independent of the number of agents. Furthermore, for the case that agents communicate over nonidentical fading channels, the mean-square consensus problem is transformed into an analyzable edge agreement issue in the mean-square sense by means of properties of the edge Laplacian combined with a mapping technique. Next, a sufficient condition is derived to ensure the mean-square consensus performance, based on which the existence of the controller can be guaranteed by the feasibility of a set of LMIs. Finally, the validity and feasibility of the developed design scheme are shown by two illustrative examples.

This research is devoted to the cooperative control of high-order fully actuated networked multiagent systems (HOFA-NMASs) with communication delays between network node and sensors, and network node and actuators. A proportional integral (PI) predictive control scheme is developed to effectively implement the cooperation among HOFA-NMASs and actively compensate the communication delays. In this scheme, a local PI feedback controller is applied to stabilize the closed-loop nominal HOFA-NMASs, then a Diophantine equation is presented to establish an incremental HOFA prediction model rather than a reduced-order one, so that the optimization of cost function considered cooperative performance and the compensation of communication delays are completed via multistep output predictions, thus the optimal cooperative controllers are constructed. The further discussion provides a simple condition of simultaneous stability and output consensus of closed-loop HOFA-NMASs, which is easy to verify and extend in practice and is independent on communication delays. An experiment of formation control of air-bearing simulators is shown to prove the availability of the proposed PI predictive control scheme.

This paper proposes a distributed cooperative control strategy for cyber-physical microgrids in a distributed sparse network with communication delays by using predictive control theory, which enables each distributed generator (DG) unit to achieve voltage restoration. In the proposed control strategy, we first design a primary droop-free model predictive controller to make the output voltage track its nominal set points timely, with the neighbor-based error information a secondary distributed coordinated predictive controller is then proposed to actively compensate the general communication delays for cyber-physical microgrids caused by low-bandwidth communication networks. Sufficient conditions, in terms of communication network connectivity and control gains for the system stability are derived by using the tools of special matrix theory and algebraic graph theory. The proposed control protocols are fully distributed and can be implemented through a sparse communication network and thus, satisfy the plug-and-play feature of the future smart grid. The effectiveness of the control strategy in compensating communication delays is also verified by real-time simulation experiments in OPAL-RT on a test microgrid.

In this article, a delay-compensation-based state estimation (DCBSE) method is given for a class of discrete time-varying complex networks (DTVCNs) subject to network-induced incomplete observations (NIIOs) and dynamical bias. The NIIOs include the communication delays and fading observations, where the fading observations are modeled by a set of mutually independent random variables. Moreover, the possible bias is taken into account, which is depicted by a dynamical equation. A predictive scheme is proposed to compensate for the influences induced by the communication delays, where the predictive-based estimation mechanism is adopted to replace the delayed estimation transmissions. This article focuses on the problems of estimation method design and performance discussions for addressed DTVCNs with NIIOs and dynamical bias. In particular, a new distributed state estimation approach is presented, where a locally minimized upper bound is obtained for the estimation error covariance matrix and a recursive way is designed to determine the estimator gain matrix. Furthermore, the performance evaluation criteria regarding the monotonicity are proposed from the analytic perspective. Finally, some experimental comparisons are proposed to show the validity and advantages of the new DCBSE approach.

This article investigates the dynamic consensus problem for the discrete-time second-order integrator networked multiagent system with time-varying delay and switching topology, in which the speed of each agent is not required to be synchronized to zero value. Novel consensus protocols using only relative state information are proposed, and sufficient conditions for dynamic consensus are derived. The results show that consensus can be reached for both the case with delay and the case without delay, if the gain is sufficiently small and the union of interaction graphs is scrambling or contains a spanning tree frequently enough as the system evolves. Numerical examples demonstrate the effectiveness of the theoretical results.

This article studies the coordinated control problem of networked multiagent systems via distributed cloud computing. A distributed cloud predictive control scheme is proposed to achieve desired coordination control performance and compensate actively for communication delays between the cloud computing nodes and between the agents. This scheme includes the design of a multistep state predictor and optimization of control coordination. The multistep state predictor provides a novel way of predicting future immeasurable states of agents in a large horizontal length. The optimization of control coordination minimizes the distributed cost functions which are presented to measure the coordination between the agents so that the optimal design of the coordination controllers is simple with little computational increase for large-scale-networked multiagent systems. Further analysis derives the conditions of simultaneous stability and consensus of the closed-loop-networked multiagent systems using the distributed cloud predictive control scheme. The effectiveness of the proposed scheme is illustrated by an example.

The reliability of the microgrid (MG) can be improved by interconnecting MGs that are in close proximity, because the power shortfall in one MG can be compensated by the excess power available from other interconnected MGs. For multiple dc MG clusters consisting of a large number of heterogeneous distributed generators (DGs), this article establishes a master-slave cooperation framework containing a two-layer voltage estimator. All master-DGs implement current economical allocation among multiple MG clusters and drive their respective slave-DGs to realize current sharing accuracy. Compared with the previous work, the proposed control strategy has the advantages of simultaneous achievement of accurate current sharing and current economical allocation, short time consumption and faster convergence, and robustness against uncertain communication environments. Moreover, all distributed controllers are allowed to be implemented in a variable cyber network, thus well matching the characteristics of frequent switching operation in MG systems. Sufficient conditions in terms of control time constants for the two-layer cyber network are also deduced to ensure entire system stability. Different cases are tested to verify the effectiveness of the results in MATLAB/SimPowerSystems.

In this article, the tracking problem of networked discrete-time second-order nonlinear multiagent systems (MASs) is studied. First, for the MASs without communication delay, a novel method, called distributed model-free sliding-mode control algorithm is proposed, which can make the system converge quickly without the accurate model. Furthermore, for the MASs with delay, in order to eliminate the influence of time delay on the system, a distributed model-free sliding-mode predictive control strategy based on time-delay compensation technology is proposed, which can actively compensate for time delay while ensuring system stability and consensus tracking performance requirements. Both the simulation and experiment results reveal the superiority of the proposed methods.

As a crucial part of cyber-physical systems, a digital twin can process data, visualize processes, and send commands to the control system, which can be used for the research on thermal power plants that are vital for providing energy for manufacturing and industry, and also daily consumptions. This article introduces the methodologies and techniques toward a web-based digital twin thermal power plant. To implement a web-based digital twin thermal power plant, the architecture, modeling, control algorithm, rule model, and physical-digital twin control are explored. The potential functionalities of the web-based digital twin including real-time monitoring, visualization and interactions, and provided services for physical thermal plants and universities are also presented. A case study has been provided to illustrate the web-based digital twin power plant. The research in this article can provide potential solutions for web-based digital twin research and education.

Online experimentation systems that support remote and/or virtual experiments in an Internet-based environment play an important role in skill-enhanced online learning, especially in the field of engineering. This article explores a human-centered online system with a unified architecture that covers the entire process of control engineering experimentation. The control and security-oriented design are presented and the human-centered design including the HTML5-based web application and the configurable graphical user interface is also introduced. Interactive features such as tuning parameters and 3-D animations and interactions are integrated into the system. Enhanced 3-D effects such as anaglyph 3-D and parallax 3-D are also provided. Thus, users can experience different types of 3-D effects on experiment equipment or in a 3-D virtual world while conducting experiments. Details of an application example with a dual tank system are also explored to verify the performance of the proposed system.

This article addresses the prediction-based distributed filtering problem for a class of time-varying nonlinear stochastic systems with communication delays and missing measurements through the sensor networks. The phenomenon of the missing measurements is depicted by a set of Bernoulli distributed random variables, where each sensor node possesses its own missing probability. The communication delays are taken into account, which commonly occur during the estimation exchanges among the sensor nodes with communication links. A new prediction-based suboptimal distributed filter is designed by taking the missing probabilities and the prediction estimation into account, which has the advantages on the active compensation of the impacts caused by the missing measurements and communication delays. That is, a new compensation filtering method within the time-varying framework is presented based on the predictive estimation and the innovation measurements. A locally minimum upper bound matrix for the estimation error covariance is obtained by properly designing the distributed filter gain at every sampling step. Furthermore, the performance analysis problem of the prediction-based distributed filtering algorithm is discussed by providing the desirable theoretical derivations. Finally, some comparative simulations are used to show the advantages of the presented prediction-based distributed filtering strategy under delay compensation mechanism.

This paper is concerned with the coordinated control problem of multiagent systems with communication constraints. A cost function of measuring the coordinated control between networked multiagents is presented. The networked proportional integral predictive control scheme is proposed so that not only the cost function is optimized but also simultaneous consensus and stability of networked multiagent systems is achieved and the communication constraints are actively compensated. The further analysis provides the necessary and sufficient conditions of reaching simultaneous stability and consensus. An example shows the proposed scheme works effectively.

In this paper, a new recursive state estimation problem is discussed for a class of discrete time-varying stochastic complex networks with uncertain inner coupling and signal quantization under the error-variance constraints. The coupling strengths are allowed to be varying within certain intervals, and the measurement signals are subject to the quantization effects before being transmitted to the remote estimator. The focus of the conducted topic is on the design of a variance-constrained state estimation algorithm with the aim to ensure a locally minimized upper bound on the estimation error covariance at every sampling instant. Furthermore, the boundedness of the resulting estimation error is analyzed, and a sufficient criterion is established to ensure the desired exponential boundedness of the state estimation error in the mean square sense. Finally, some simulations are proposed with comparisons to illustrate the validity of the newly developed variance-constrained estimation method.

This paper investigates the control problem of networked nonlinear multiagent systems with communication constraints and unknown dynamics, based on system input-output data. A cost function for the design of networked nonlinear multiagent control systems is presented, which considers not only the coordination performance between agents but also the performance of individual agents. Utilizing a data model to represent discrete-time nonlinear multiagent systems, a networked data-driven predictive control scheme is proposed to optimize control performance, compensate for communication constrains, and achieve both consensus and stability of networked nonlinear multiagent systems. An optimal control protocol is derived, which minimizes the cost function and makes compensation for communication constraints. The criteria of achieving both consensus and stability of networked nonlinear multiagent systems are provided. An example illustrates the performance of the proposed networked data-driven predictive control scheme.

This paper comprehensively reviews the state-of-the-art development in formation control of small satellites. Satellite formation flying, distributed satellite systems, and fractionated satellite formation are discussed first. Various formation control architectures and methods of small satellites are then introduced, including the leader-following method, the behavior-based method, the virtual structure method, the cyclic pursuit method, the artificial potential function method, the algebraic graph method, and the noncontact force method. Coordinative control of multiple small satellites is also reviewed, covering coordinative control of satellite formation, coordinative attitude control of satellite formation, and coordinative coupled attitude and orbit control of satellite formation. The achievements and development trends of the formation control of small satellites are considered and analyzed.

This paper is concerned with the consensus and stability problem of multiagent control systems via networks with communication delays and data loss. A networked multiagent predictive control scheme is proposed to achieve output consensus and also compensate for the communication delays and data loss actively. The necessary and sufficient conditions of achieving both consensus and stability of the closed-loop networked multiagent control systems are derived. An important result that is obtained is that the consensus and stability of closed-loop networked multiagent predictive control systems are not related to the communication delays and data loss. An example illustrates the performance of the networked multiagent predictive control scheme.

This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.

This paper studies the problem of consensus tracking for heterogeneous discrete-time networked multiagent systems (NMASs) with network-induced communication delay. By introducing the networked predictive control scheme, novel consensus tracking protocols are designed to ensure that states of the NMASs follow the external reference signal. One of the agents is defined as the leader agent, all the other agents are defined as the followers, and the external reference signal is always known by the leader agent, both cases where the external reference signal is available and unavailable to the follower agents are discussed in this paper. The necessary and sufficient conditions for the novel consensus tracking protocols are proposed based on algebraic and graph theory. Numerical examples are given to validate the theoretical results.

An extended asynchronous switching model is investigated for a class of switched stochastic nonlinear retarded systems in the presence of both detection delay and false alarm, where the extended asynchronous switching is described by two independent and exponentially distributed stochastic processes, and further simplified as Markovian. Based on the Razumikhin-type theorem incorporated with average dwell-time approach, the sufficient criteria for global asymptotic stability in probability and stochastic input-to-state stability are given, whose importance and effectiveness are finally verified by numerical examples.

This paper is concerned with the design and performance analysis of networked control systems with network-induced delay, packet disorder, and packet dropout. Based on the incremental form of the plant input-output model and an incremental error feedback control strategy, an incremental networked predictive control (INPC) scheme is proposed to actively compensate for the round-trip time delay resulting from the above communication constraints. The output tracking performance and closed-loop stability of the resulting INPC system are considered for two cases: 1) plant-model match case and 2) plant-model mismatch case. For the former case, the INPC system can achieve the same output tracking performance and closed-loop stability as those of the corresponding local control system. For the latter case, a sufficient condition for the stability of the closed-loop INPC system is derived using the switched system theory. Furthermore, for both cases, the INPC system can achieve a zero steady-state output tracking error for step commands. Finally, both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method.

This paper investigates the global bounded consensus problem of networked multiagent systems consisting of nonlinear nonidentical node dynamics with the communication time-delay topology. We derive globally bounded controlled consensus conditions for both delay-independent and delay-dependent conditions based on the Lyapunov-Krasovskii functional method. The proposed consensus criteria ensure that all agents eventually move along the desired trajectory in the sense of boundedness. Meanwhile, the bounded consensus criteria can be viewed as an extension of the case of identical agent dynamics to the case of nonidentical agent dynamics. We finally demonstrate the effectiveness of the theoretical results by means of a numerical simulation.

A recursive networked predictive control (RNPC) approach is proposed for networked control systems (NCSs), which mainly consists of two parts: a control prediction generator (CPG) and a network delay compensator (NDC). Based on a NARMA model, the CPG is applied to generate control predictions using the historical input-output data of the plant. The NDC is designed in the actuator to actively compensate for the network communication constraints such as network-induced delay, data packet disorder, accumulation and dropout. The RNPC is easy to be implemented in practice compared with previous results in that the recursive method is used to derive the future output predictions and control predictions, and the round-trip time delay is also used in the compensation scheme. Two RNPC systems are designed for a DC motor Internet-based control system, which are based on the nonlinear model and the simply linearized model, respectively. Practical experiments have been carried out to demonstrate the effectiveness of the proposed approach.

This paper studies the robustness of networked predictive control systems (NPCS) with uncertainties. A networked predictive control strategy that compensates for delay actively rather than passively is introduced to cope with - varying network delay and data dropout. The closed-loop networked predictive control system is described as a normal robust control system, which makes the control design and stability analysis convenient. The robustness analysis of the closed-loop networked predictive control system is discussed in details.

This paper is concerned with the stability analysis for a class of MIMO networked control systems (NCSs) with network constraints. In view of MIMO NCSs where network is of limited access channels, a discrete-time switched delay model is formulated. By constructing a novel piecewise Lyapunov-Krasovskii functional, a new stability criterion is developed in terms of linear matrix inequalities. A numerical example is given to show the effectiveness of the proposed method.

This paper introduces the NCSLab (Networked Control System Laboratory) at http://www.ncslab.net, which provides a complete Web-based solution for users to carry out experiments on experiment devices located globally. A scalable and service-oriented architecture which is composed of Web browsers, central Web server, MATLAB servers, regional experiment servers, control units and experiment devices is proposed. Based on the architecture, many novel functionalities including visual algorithm designing, simulation, compilation, visual monitor configuration, real-time monitoring and supervisory control are designed and implemented by combination of state-of-the-art technologies such as Web2.0, J2EE and MATLAB. The service-oriented architecture designed and the functionalities provided distinguish NCSLab from other existing remote laboratory systems. An experiment example is given to demonstrate that users can enjoy all these rich interactive features with a simple Web browser from anywhere at any time.

In this paper, the problems of the exponential stability and stabilization for a class of discrete systems with time-varying delays are considered. By converting discrete systems with time-varying delays into switched systems and using the average dwell-time method, a new stability criterion is obtained and presented in terms of linear matrix inequality. Based on the obtained stability condition, a design method for the feedback controller to stabilize the system is also proposed. Finally, some numerical examples are given to show the effectiveness of the proposed method.

This paper discusses the control of a magnetic levitation (MagLev) system over networks. In order to improve the control performance, the networked predictive control method is employed based on the feedback linearization and direct local linearization models of the nonlinear MagLev system. Further a test-rig is set up to implement the above control. Simulation and experiment results show that the networked predictive control has clear performance advantages over other networked control strategies which do not incorporate compensation for the network-induced delay.

Control systems are widely used in daily life and support various important infrastructure, such as power, hydraulics, petrochemicals, transport, telecom, etc. Once the control system is attacked, the consequence would be unthinkable. The DES (Data Encryption Standard) encryption algorithm is open, and it has the merit of large encryption strength and fast computational speed. This paper describes the DES algorithm, the hardware and software design of the DES hardware encryption system based on the DES encryption algorithm and FPGA (Field Programmable Gate Array). The hardware design includes the design program of the DES hardware encryption system, the design of S-box and the design of circuit architecture. The software design is mainly to write a s-function of the DES hardware interface. An experiment of a networked DC motor speed control based on DES is described, and this networked control system has the function of hardware encryption.

A packet-based deadband control approach is proposed for Networked Control Systems (NCSs). Within the packet-based control framework for NCSs, the proposed deadband control strategy takes full advantage of the packet-based data transmission in NCSs, and thus considerably reduces the use of the communication resources in NCSs whilst maintaining the system performance at a satisfactory level. The stability conditions of the closed-loop system are obtained and a numerical example illustrating the effectiveness of the proposed approach is presented.

A novel networked multisensor data-fusion method is developed in this paper. A federated filter is employed to fuse the data transmitted over the network, which plays an important role in the data-processing center. The stability of filters under the network is considered; an algorithm to deal with the delayed data is introduced, and the principle for data fusion is presented. Finally, two numerical examples show the effectiveness of the proposed scheme.

In this paper, the packet-based control approach to networked control systems (NCSs) is extended to the continuous-time case with the use of a discretization technique for continuous network-induced delay. The derived approach can effectively simultaneously deal with network-induced delay, data packet dropout, and data packet disorder and leads to a novel model for NCSs. This model offers the designer the freedom of designing different controllers with respect to specific network conditions, which is distinct from previous results and ensues better system performance. By applying switched system theory, the stability criterion for the derived model is obtained, which is then used to obtain an linear matrix inequality-based stabilized controller design method for the packet-based control approach. A numerical example is also presented, which illustrates the effectiveness of the proposed packet-based control approach by comparison.

The issue of stability of linear systems with time-varying delay is considered in this paper. By constructing a new type of Lyapunov functional which contains a novel triple integral term and using Finsler's lemma, new delay-dependent stability criteria are derived in terms of linear matrix inequality (LMI). Numerical examples are given to illustrate the effectiveness of the proposed method.

In this paper, the packet-based control approach to networked control systems is extended to the continuous time case, with the use of a discretization technique for the continuous network-induced delay. The derived approach can effectively deal with network-induced delay, data packet dropout and data packet disorder simultaneously, and also leads to a novel model for networked control systems. This model offers the designer the freedom of designing different controllers with respect to specific network conditions, which is distinct from previous results and is expected to result in better performance. By applying switched system theory, the stability criterion for the closed-loop system is obtained. A numerical example to illustrate the effectiveness of the proposed approach is also presented.

This correspondence considers the stability problem for a class of linear switched systems with time-varying delay in the sense of Hurwitz convex combination. The bound of derivative of the time-varying delay can be an unknown constant. It is concluded that the stability result for linear switched systems still holds for such systems with time-varying delay under a certain delay bound. Moreover, the delay bound of guaranteeing system stability can be easily obtained based on linear matrix inequalities (LMIs). As a special case, when the time-varying delay becomes constant, the criterion obtained in this correspondence is less conservative than existing ones. The reason for less conservativeness is also explicitly explained in this correspondence. Simulation examples illustrate the effectiveness of the proposed method.

In this paper, a predictive control-based approach is proposed for a Hammerstein-type system which is closed through some form of network. The approach uses a two-step predictive controller to deal with the static input nonlinearity of the Hammerstein system and a delay and dropout compensation scheme to compensate for the communication constraints in a networked control environment. Theoretical results are presented for the closed-loop stability of the system. Simulation examples illustrating the validity of the approach are also presented.

In this letter, a new method is proposed for stability analysis of neural networks (NNs) with a time-varying delay. Some less conservative delay-dependent stability criteria are established by considering the additional useful terms, which were ignored in previous methods, when estimating the upper bound of the derivative of Lyapunov functionals and introducing the new free-weighting matrices. Numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method.

This letter is concerned with the stability analysis of neural networks (NNs) with time-varying interval delay. The relationship between the time-varying delay and its lower and upper bounds is taken into account when estimating the upper bound of the derivative of Lyapunov functional. As a result, some improved delay/interval-dependent stability criteria for NNs with time-varying interval delay are proposed. Numerical examples are given to demonstrate the effectiveness and the merits of the proposed method.

This paper is concerned with the design of networked control systems (NCSs) with random network delay in the feedback channel and gives stability criteria of closed-loop networked predictive control systems. The principle of predictive control is adopted to overcome the effects of network time delay. The necessary and sufficient conditions on the stability of the closed-loop NCS are derived, which provides useful analytical stability criteria. The closed-loop networked predictive control system with bounded random network delay is stable if the corresponding switched system is stable. Simulation and real-time results give an illustration of the proposed control strategies

BACKGROUND: MicroRNAs (miRNAs) are a group of short (approximately 22 nt) non-coding RNAs that play important regulatory roles. MiRNA precursors (pre-miRNAs) are characterized by their hairpin structures. However, a large amount of similar hairpins can be folded in many genomes. Almost all current methods for computational prediction of miRNAs use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins. Ab initio method for distinguishing pre-miRNAs from sequence segments with pre-miRNA-like hairpin structures is lacking. Being able to classify real vs. pseudo pre-miRNAs is important both for understanding of the nature of miRNAs and for developing ab initio prediction methods that can discovery new miRNAs without known homology. RESULTS: A set of novel features of local contiguous structure-sequence information is proposed for distinguishing the hairpins of real pre-miRNAs and pseudo pre-miRNAs. Support vector machine (SVM) is applied on these features to classify real vs. pseudo pre-miRNAs, achieving about 90% accuracy on human data. Remarkably, the SVM classifier built on human data can correctly identify up to 90% of the pre-miRNAs from other species, including plants and virus, without utilizing any comparative genomics information. CONCLUSION: The local structure-sequence features reflect discriminative and conserved characteristics of miRNAs, and the successful ab initio classification of real and pseudo pre-miRNAs opens a new approach for discovering new miRNAs.

Environment exploration and map-building are fundamental robotic application researches. An appropriate navigation algorithm can facilitate robots to map the environment within shorter time. A real-time navigation algorithm based on virtual centrifugal force is proposed in this paper for a robot to implement on-line exploration of an unknown environment in real time, and collision between a robot and an obstacle can be avoided with the VCF algorithm. The wall-following strategy is one of the special cases of the proposed algorithm when the robot approaches an obstacle. Simulation experiments are implemented to verify the effectivity of the proposed algorithm.

This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated examples.