As health and social care becomes increasingly digital-first, “participation” for older adults is often treated as a simple question: did they sign up, can they use it, and do they follow the rules? This paper argues that such a view misses what happens in everyday life. Many older people are not simply “non-compliant” or “low literacy”; they are trying to make sense of confusing systems, relying on family or community support to get things done, and sometimes stepping back when digital services feel risky, unfair, or hard to trust. If these experiences matter, then methods are needed that do more than measure uptake or performance. Older adults need ways to explain frustration, dependency, workaround, and refusal as part of living with digital-first systems. To address this gap, the paper proposes gamification not as a behaviour-change tool, but as a structured, low-pressure participatory format for sense-making and collective reflection around digital governance. Used in this way, gamification may help make visible where people get stuck, how support networks shape access, and what kinds of responsibility and control digital systems silently place on them. The paper develops this as a conceptual and methodological proposition, outlining a preliminary workflow, key design principles, and potential application scenarios rather than reporting a tested intervention or prototype. In doing so, it argues that digital participation should be understood not only as use, but as a lived and negotiated relationship with digital ageing governance.

This paper investigates a novel set-membership global estimation method for a class of discrete time-varying systems with unknown-but-bounded noises. Firstly, in order to improve the accuracy of the state estimation, a multi-sensor network structure is deployed for the considered system, in which the adjacent sensors can communicate with each other. Considering the different sampling rates of multiple sensors, a resampling strategy is proposed to transform the asynchronous sampling system into a synchronous one. Additionally, the unknown-but-bounded noises and system parameter variations are considered. A novel distributed set-membership filter with parameter variation is designed, and the optimal local state estimation ellipsoid is obtained by developing a convex optimization method. Subsequently, a soft actor-critic algorithm based on reinforcement learning is proposed, which fuses all local estimation ellipsoids from each sensor to obtain global estimation results, providing a solution for the considered system. Finally, a port crane system is employed for performance analysis to verify the feasibility and effectiveness of the proposed method.

In this work, an observability Gramian (OG)-based Chernoff fusion (CF) rule is investigated for dealing with unknown correlated probability density functions (PDFs). Specifically, we introduce a generalised uniform observability (GUO) condition, which ensures that error covariances and estimate errors are bounded under nonlinear settings within the extended Kalman filter (EKF) framework. Leveraging the GUO condition, we develop an OG-centric weighting selection method that optimises fusion weights while guaranteeing non-divergent performance using an approximated Chernoff fusion (ACF) algorithm. The resulting OG-centric weights are then embedded to develop an OG-based approximated Chernoff fusion (OGBACF) algorithm that can compute fusion weights and error covariances in parallel. Finally, we conduct simulations to demonstrate the efficacy of our proposed fusion methodology.

This paper studies group synchronization tracking problem for a class of high-order multi-agent systems (MASs) with nonidentical and unknown direction faults (NUDFs) under multiple cyber attacks (i.e., denial-of-service (DoS) attacks and false data injection attacks (FDIAs)). Be motivated by this, a fully distributed hierarchical (cyber layer and physical layer) Zeno-free event-triggered (ET) intrusion-and fault-tolerant controller is presented based on Nussbaum-type gain technique, where a positive inter-event time exists in the state-dependent asynchronous ET mechanism (ETM). The constructed virtual cyber layer realizes the interaction among different agents so as to avoid the interaction in physical processes and thus reduce the error propagation. This two layer controller greatly increase the flexibility of the controller design compared with single-layer control strategies. In addition, a novel Nussbaum function stability lemma (Lemma lem4lem4) is developed for the first time. Based on this lemma, the fully distributed adaptive controller can adjust the direction of the input to match the fault direction with the help of Nussbaum function. Finally, a robotic manipulator example demonstrates the effectiveness and merit of the proposed control scheme.Note to Practitioners—In industrial processes, NUDFs and cyber attacks often occur in many different systems, which usually lead to performance degradation or even serious accidents. Typically, NUDFs affect the performance of chemical and industrial processes, circuits, and sensors. Meanwhile, in driverless vehicle platoon, attackers change the control signal through the wireless network, and then issue emergency braking commands, etc. Therefore, this paper designs a fully distributed hierarchical ET intrusion-and fault-tolerant scheme for high-order MASs that are usually used to model ship dynamics, robotic manipulators, and quarter-car active suspension. The control scheme gives solutions to the problem of NUDFs, multiple cyber attacks, and network bandwidth limitations at different layers, and effectively integrates the physical and cyber layers to achieve group synchronization. Meanwhile, a new event-triggered mechanism under the framework of group synchronization is developed to save communication resources. Robotic manipulator simulation studies verify the validity of the proposed scheme.

In this article, a hierarchical predictive control (PC) algorithm is designed for visual servoing mobile robot systems. At the kinematic level, the image-based visual servoing model of a wheeled mobile robot is established. By defining the corresponding performance index of the PC, an iterative linear quadratic regulator (iLQR) is used to obtain the velocity controller and to provide reference velocity for dynamics. In dynamics, a data-driven PC controller based on the Gaussian process (GP) is proposed to obtain the torque controller with unknown dynamics. The input-to-state practical stability (ISpS) of the system based on the proposed data-driven PC method is proved by introducing reasonable assumptions. The corresponding theorem also analyzes the maximum upper bound of GP inference error. Finally, the effectiveness of the proposed hierarchical controller is verified by simulations and experiments.

This article investigates the localization problem of the automatic guided vehicle (AGV) system. In order to improve the reliability and flexibility of the localization process, a distributed sensor network structure is introduced to realize the localization of the AGV. Moreover, considering the influence of unknown-but-bounded noise and the accuracy requirements of the localization, a novel global set-membership filtering approach is proposed to obtain accurate localization results including a distributed set-membership filtering (DSMF) strategy and a circumscribed rectangle method. First, a DSMF strategy is designed to obtain local state estimation ellipsoids. Sufficient conditions for the existence of the state estimation ellipsoids are derived and a convex optimization process is developed to obtain the optimal local estimation ellipsoids. Then, a circumscribed rectangle method is proposed to fuse all local state estimation ellipsoids and obtain global set-membership filtering results. The proposed fusion method does not have a complicated optimization process, and can obtain more accurate estimation ellipsoids than local estimation results. Performance analysis verifies the effectiveness of the proposed global set-membership filtering approach.

Most of the existing localization schemes necessitate a priori statistical characteristic of measurement noise, which may be unrealistic in practical applications. This article addresses the problem of indoor localization by implementing distributed set-membership filtering based on a received signal strength indicator (RSSI) under unknown-but-bounded process and measurement noises. First, the transmit power and the path-loss exponent are estimated by a novel least-squares curve fitting (LSCF) method in RSSI-based localization. Since the localization process of trilateration is susceptible to inaccuracy caused by the noise-affected distance measurements, a convex optimization method is then developed to obtain the state ellipsoid estimation under the unknown-but-bounded noises. Third, a recursive algorithm is established to compute the global ellipsoid that guarantees to locate the true target at every time step. Finally, experimental validation is presented to demonstrate the accuracy and effectiveness of the proposed set-membership filtering method for indoor localization.

This article is concerned with set-membership global estimation for a networked system under unknown-but-bounded process and measurement noises. First, a group of local set-membership estimators is deployed to obtain the local ellipsoidal estimate of the true system state. Each estimator is capable of communicating with its neighbors within its communication range. Second, a global estimation approach is proposed which generates a trace-maximal ellipsoid within the intersection of all the local estimation sets with an aim to improve the difference of the local estimate at each time instant. Sufficient conditions for providing a global estimate under both complete and incomplete measurement transmissions are derived. Third, as an application, a modified distributed photovoltaic grid-connected generation system is provided to verify the effectiveness of the developed set-membership global estimation approach. Furthermore, an islanding fault detection scheme is derived based on the calculated global ellipsoidal estimate. Finally, simulation verification of the obtained theoretical results on the distributed generation system is presented.

Some new results about the uniform lower and upper bounds of the predicted and filtered error covariances of the Kalman filter for linear time-varying systems from the perspective ofuniform detectabilityare provided. First, a novel and general extendedly uniform detectability (EUD) is proposed, and the appropriate conditions to guarantee the existence of the uniform upper and lower bounds of the predicted and filtered error covariances are presented, respectively. Next, the explicit expressions of both upper and lower bounds are obtained with less conservatism. Moreover, the relations between those bounds, and their connections to estimation errors are revealed. Finally, an illustrative example is given to validate the effectiveness of the proposed theoretical developments in deriving the uniform lower and upper bounds, and a comparison with the existing results is made.

Finding and identifying the minimal number of sensor nodes for a sensor network is one of the most basic problems for the implementation of distributed state estimators. Despite a plethora of research studied sensor networks, most of them ignored this problem or assumed the considered sensor network comes with an ideal number of sensor nodes. We revisit this problem in the current paper. To this end, the minimal number of sensor nodes problem is first formalized and a novel observability condition, namely, minimal nodes uniform observability (MNUO), is then proposed. Next, this MNUO is applied to study the stability issues of the distributed Kalman filtering algorithm. In what follows, under the condition of MNUO, conditions to ensure its stability are given and the results about the relation of the filtering performance before and after selecting the minimal number of sensor nodes are obtained. Finally, optimization solutions and an example are given to find the minimal number of sensor nodes for a sensor network.

This article is concerned with the resilient tracking control of a networked control system under cyber attacks. The attacker is an active adversary whose aim is to severely degrade the tracking performance of the system by launching deception attacks on the sensor-to-controller communication channels and denial-of-service attacks on the controller-to-plant channels, respectively. First, a concept of resilient set-membership tracking control is presented, through which the system's true state is guaranteed to reside in a bounding ellipsoidal set of the reference state regardless of the existence of attacks and unknown-but-bounded (UBB) noises. Second, in the case that full information of the system's state is not implicitly trusted in the presence of attacks, a resilient set-membership estimation strategy is provided to secure the state estimates against the deception attacks. Furthermore, based on a recursive computation of a reference state ellipsoid and confidence state estimation ellipsoids, a convex optimization algorithm in terms of recursive linear matrix inequalities is proposed to obtain the gain parameters for both the desired resilient state estimator and the tracking controller. Finally, the effectiveness of the proposed method is illustrated through an Internet-based three-tank system.

This paper is concerned with the distributed H∞-consensus filtering problem on attitude tracking over a radar filter network subject to switching topology and random packet dropouts occurring in the data transmission from both the Sun sensor and the filters. Since ground-based radars cannot directly measure the satellite attitude, a Sun sensor is deployed at the satellite side and its measurements are transmitted to radar filters through different network communication channels while suffering from random packet dropouts with different probabilities. In the radar filter network, each radar filter receives data not only from the Sun sensor but also from its local neighboring radar filters in accordance with a switching network topology. A delicate distributed H∞-consensus filtering algorithm, which incorporates the effects of switching network topology and random packet dropouts, is adopted to estimate attitude and attitude-rate. The algorithm guarantees H∞-consensus attenuation performance for the estimation deviations among radar filters, and the robustness against the switching network topology and packet dropouts for the radar filter network. The illustrative examples are given to verify the effectiveness of the proposed distributed H∞-consensus filtering algorithm.

This article is concerned with the problem of dissipativity and stability analysis for a class of neural networks (NNs) with time-varying delays. First, a new augmented Lyapunov-Krasovskii functional (LKF), including some delay-product-type terms, is proposed, in which the information on time-varying delay and system states is taken into full consideration. Second, by employing a generalized free-matrix-based inequality and its simplified version to estimate the derivative of the proposed LKF, some improved delay-dependent conditions are derived to ensure that the considered NNs are strictly ( Q , S , R )- γ -dissipative. Furthermore, the obtained results are applied to passivity and stability analysis of delayed NNs. Finally, two numerical examples and a real-world problem in the quadruple tank process are carried out to illustrate the effectiveness of the proposed method.

This paper is concerned with the sliding mode control problem for a class of continuous-time switched neural networks with mode-dependent average dwell time (MDADT). The considered continuous-time switched neural networks are motivated by biological neural networks which contain a nonlinear term and a changeable switched signal. The concept of MDADT is introduced, in which every subsystem has its own dwell time before switching to another subsystem. Moreover, a novel sliding mode controller is designed by an event-triggered mechanism which is based on the observer error and the system mode, where its triggered condition can be more conservative and practical than the existing triggered conditions. Sufficient conditions are derived to ensure that the closed-loop system is stochastically exponentially stable in terms of linear matrix inequalities. The designed sliding mode controller can promote the sliding mode motion of the system state. Finally, an illustrative example is provided to demonstrate the effectiveness and merits of the proposed method.

An event-triggered mechanism is of great efficiency in reducing unnecessary sensor samplings/transmissions and, thus, resource consumption such as sensor power and network bandwidth, which makes distributed event-triggered estimation a promising resource-aware solution for sensor network-based monitoring systems. This paper provides a survey of recent advances in distributed event-triggered estimation for dynamical systems operating over resource-constrained sensor networks. Local estimates of an unavailable state signal are calculated in a distributed and collaborative fashion based on only invoked sensor data. First, several fundamental issues associated with the design of distributed estimators are discussed in detail, such as estimator structures, communication constraints, and design methods. Second, an emphasis is laid on recent developments of distributed event-triggered estimation that has received considerable attention in the past few years. Then, the principle of an event-triggered mechanism is outlined and recent results in this subject are sorted out in accordance with different event-triggering conditions. Third, applications of distributed event-triggered estimation in practical sensor network-based monitoring systems including distributed grid-connected generation systems and target tracking systems are provided. Finally, several challenging issues worthy of further research are envisioned.

This paper is concerned with the adaptive backstepping control problem for a cloud-aided nonlinear active full-vehicle suspension system. A novel model for a nonlinear active suspension system is established, in which uncertain parameters, unknown friction forces, nonlinear springs and dampers, and performance requirements are considered simultaneously. In order to deal with the nonlinear characteristics, a backstepping control strategy is developed. Meanwhile, an adaptive control strategy is proposed to handle the uncertain parameters and unknown friction forces. In the cloud-aided vehicle suspension system framework, the adaptive backstepping controller is updated in a remote cloud based on the cloud storing information, such as road information, vehicle suspension information, and reference trajectories. Finally, simulation results for a full vehicle with 7-degree of freedom model are provided to demonstrate the effectiveness of the proposed control scheme, and it is shown that the addressed controller can improve the performances more than 80% compared with passive vehicle suspension systems.

This paper is concerned with the distributed attack detection and recovery in a vehicle platooning control system, wherein inter-vehicle information is propagated via a wireless communication network. An active adversary may launch malicious cyber attacks to compromise both sensor measurements and control command data due to the openness of the wireless communication. First, a distributed attack detection algorithm is developed to identify any of those attacks. The core of the algorithm lies in that each designed filter can provide two ellipsoidal sets: a state prediction set and a state estimation set. Whether a filter can detect the occurrence of such an attack is determined by the existence of intersection between these two sets. Second, two recovery mechanisms are put forward, through which the adversarial effects of cyber attacks can be mitigated in a timely manner. The recovery mechanisms depend on reliable modifications of the attacked signals required for the computation of the two ellipsoidal sets. Finally, simulation is provided to validate the effectiveness of the proposed method in both detection and recovery phases.

In this paper, the event-triggered distributed Kalman filtering problem is considered for a class of networked multisensor fusion systems (NMFSs) under sensor energy and network bandwidth constraint. A general event-triggered scheme is employed for the NMFSs to reduce the energy consumption and communication burden between the sensor nodes and fusion center (FC) under the communication bandwidth constraints. Local estimation information is allowed to transmit partial components to FC over the network with limited bandwidth. A group of binary variables are introduced to describe the component transmitting process when the triggering condition is violated. Furthermore, the untransmitted local estimation signals are compensated by the previous transmitted one, and a recursively event-triggered distributed fusion Kalman filter in the linear minimum mean square error sense is designed from the restructured local unbiased estimators. At each time instant, a set of binary variables are determined by an optimal judgement criterion. Finally, a simulation example is provided to illustrate the effectiveness and advantages of the proposed methods.

In this paper, the event-triggered H∞state estimation problem is investigated for a two-degree-of-freedom quarter-car suspension system operated over a switching-channel network environment. First, the channel-switching is governed by a nonhomogeneous Markov chain whose probability transition matrix is time-varying. Then, a Markov jump linear system model is adopted to represent the overall networked system in view of the event-triggered communication scheme, signal quantization and random packet losses on account of the limited network bandwidth. By virtue of the Lyapunov functional and linear matrix inequality method, the event-triggered H∞state estimation problem is transformed into an optimization problem that switching-channel-dependent estimators are designed such that the estimation error system is exponentially stable in the mean square sense and achieves a desired performance level. Finally, a simulation example is used to demonstrate the validity of proposed design method.

This paper studies an attack detection problem for a networked leader-following multi-agent system subject to unknown-but-bounded system noises and quantization effects, where an adversary launches malicious cyber attacks on agents' measurement outputs aiming to distrust the leader-following consensus. An effective distributed attack detection algorithm is firstly developed for each follower such that the attack can be identified at the time of its occurrence. The core of the algorithm lies in a set-membership filtering approach from which each designed filter can provide an ellipsoidal state prediction set and an ellipsoidal state estimation set. Whether a filter can detect the occurrence of such an attack is then determined by the existence of intersection between these two sets. Furthermore, a convex optimization algorithm is established to solve out anticipated consensus protocol and two-step set-membership filter by resorting to some recursive linear matrix inequalities. Finally, an illustrative example is given to show the effectiveness of the proposed main results.

This paper is concerned with the distributed H∞state estimation for a discrete-time target linear system over a filtering network with time-varying and switching topology and partial information exchange. Both filtering network topology switching and partial information exchange between filters are simultaneously considered in the filter design. The topology under consideration evolves not only over time but also by an event switch which is assumed to be subject to a nonhomogeneous Markov chain. The probability transition matrix of the nonhomogeneous Markov chain is time-varying. In the filter information exchange, partial state estimation information and channel noise are simultaneously considered. In order to design such a switching filtering network with partial information exchange, stochastic Markov stability theory is developed. The switching topology-dependent filters are derived to guarantee an optimal H∞disturbance rejection attenuation level for the estimation disagreement of the filtering network. It is shown that the addressed H∞state estimation problem is turned into a switching topology-dependent optimal problem. The distributed filtering problem with complete information exchanges from its neighbors is also investigated. An illustrative example is given to show the applicability of the obtained results.

This paper addresses the distributed adaptive event-triggered H∞filtering problem for a class of sectorbounded nonlinear system over a filtering network with timevarying and switching topology. Both topology switching and adaptive event-triggered mechanisms (AETMs) between filters are simultaneously considered in the filtering network design. The communication topology evolves over time, which is assumed to be subject to a nonhomogeneous Markov chain. In consideration of the limited network bandwidth, AETMs have been used in the information transmission from the sensor to the filter as well as the information exchange among filters. The proposed AETM is characterized by introducing the dynamic threshold parameter, which provides benefits in data scheduling. Moreover, the gain of the correction term in the adaptive rule varies directly with the estimation error and inversely with the transmission error. The switching filtering network is modeled by a Markov jump nonlinear system. The stochastic Markov stability theory and linear matrix inequality techniques are exploited to establish the existence of the filtering network and further derive the filter parameters. A co-design algorithm for determining H∞filters and the event parameters is developed. Finally, some simulation results on a continuous stirred tank reactor and a numerical example are presented to show the applicability of the obtained results.

In this letter, a new type of fast covariance intersection (CI) rule to deal with unknown correlations is proposed. Different from the existing CI and its variants, our approach can obtain the optimized CI weights offline while preserving a guaranteed filtering accuracy and stability in the online implementation stage. To this end, the connection between the upper bound of the fused error covariances and the observability Gramian is first established. Next, the optimization of error covariances is converted into the optimization of observability Gramian, which is made of system matrices. Accordingly, the CI weights can be calculated prior to the real implementation. Moreover, the stability result of the fusion is also established with the help of the proposed jointly uniform observability condition. At last, simulations are given to demonstrate the effectiveness of the proposed fast CI method.

This paper is concerned with cyber attack detection in a networked control system. A novel cyber attack detection method, which consists of two steps: 1) a prediction step and 2) a measurement update step, is developed. An estimation ellipsoid set is calculated through updating the prediction ellipsoid set with the current sensor measurement data. Based on the intersection between these two ellipsoid sets, two criteria are provided to detect cyber attacks injecting malicious signals into physical components (i.e., sensors and actuators) or into a communication network through which information among physical components is transmitted. There exists a cyber attack on sensors or a network exchanging data between sensors and controllers if there is no intersection between the prediction set and the estimation set updated at the current time instant. Actuators or network transmitting data between controllers and actuators are under a cyber attack if the prediction set has no intersection with the estimation set updated at the previous time instant. Recursive algorithms for the calculation of the two ellipsoid sets and for the attack detection on physical components and the communication network are proposed. Simulation results for two types of cyber attacks, namely a replay attack and a bias injection attack, are provided to demonstrate the effectiveness of the proposed method.

This paper is concerned with the guaranteed cost control problem for a class of Markov jump discrete-time neural networks (NNs) with event-triggered mechanism, asynchronous jumping, and fading channels. The Markov jump NNs are introduced to be close to reality, where the modes of the NNs and guaranteed cost controller are determined by two mutually independent Markov chains. The asynchronous phenomenon is considered, which increases the difficulty of designing required mode-dependent controller. The event-triggered mechanism is designed by comparing the relative measurement error with the last triggered state at the process of data transmission, which is used to eliminate dispensable transmission and reduce the networked energy consumption. In addition, the signal fading is considered for the effect of signal reflection and shadow in wireless networks, which is modeled by the novel Rice fading models. Some novel sufficient conditions are obtained to guarantee that the closed-loop system reaches a specified cost value under the designed jumping state feedback control law in terms of linear matrix inequalities. Finally, some simulation results are provided to illustrate the effectiveness of the proposed method.

In this paper, the distributed H∞state estimation problem is investigated for a class of filtering networks with time-varying switching topologies and packet losses. In the filter design, the time-varying switching topologies, partial information exchange between filters, the packet losses in transmission from the neighbor filters and the channel noises are simultaneously considered. The considered topology evolves not only over time, but also by event switches which are assumed to be subjects to a nonhomogeneous Markov chain, and its probability transition matrix is time-varying. Some novel sufficient conditions are obtained for ensuring the exponential stability in mean square and the switching topology-dependent filters are derived such that an optimal H∞disturbance rejection attenuation level can be guaranteed for the estimation disagreement of the filtering network. Finally, simulation examples are provided to demonstrate the effectiveness of the theoretical results.

This paper considers the problem of distributed H∞filtering for a class of switched repeated scalar nonlinear systems with randomly occurred sensor nonlinearities and asynchronous switching due to practical reasons. The possibility of randomly occurred sensor nonlinearities is described by a Bernoulli stochastic variable, and the asynchronous switching filtering means that the mode of the plant is different from the mode of the designed filter possibly. A distributed filtering network is used to estimate the system state instead of a filter to improve reliability in case of faults of the filter. A distributed mode-dependent filter is designed by constructing a unified mode-dependent Lyapunov function and solving a set of linear matrix inequalities. Some novel sufficient conditions are obtained by using the average dwell time switching mechanism such that the augmented filtering error system is stochastically exponentially stable and achieves a prescribed H∞disturbance attention index. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed designed method.

This paper proposes an event-based networked set-membership filtering method to detect islanding fault for distributed grid-connected solar photovoltaic generation systems. The method enables each set-membership filter to offer an ellipsoidal estimation set, which is used to judge whether or not the islanding fault happens. When islanding fault happens, the intersection of the ellipsoids is empty, and when islanding fault is free, the intersection of the ellipsoids is nonempty. In the filtering scheme, a novel event-triggered mechanism is proposed to reduce the transmission frequency for saving the communication resources. The condition of the existence of the set-membership algorithm is derived by a time-varying convex optimization approach. A simulation experiment and a comparative experiment are provided using Sim-Power-Systems implementation based on a 2-kW single-phase grid-connected power generation system to illustrate the effectiveness of the proposed method for the detection of the islanding fault and the reduction of the resource consumption, respectively.

This paper is concerned with the problem of vertical attitude estimation of a two-degree-of-freedom quarter-car suspension system by designing a distributed filtering network, where several distributed filters estimate vehicle heave motion cooperatively under consideration of external disturbance, network channel noises, and measurement error. The sampled data are transmitted through wireless networks. In order to reduce network traffic load and save communication resources, a novel periodic event-triggered sampling scheme is proposed, under which data are transmitted only when the proposed triggering condition is violated. Codesign of event-triggered and distributed filters is derived to guarantee well H∞robustness to the system noises considered above. Finally, the experiments are given to show the effectiveness of the proposed filtering system.

This paper proposes a novel islanding fault detection scheme based on the networked ellipsoidal estimation method for distributed grid-connected photovoltaic (PV) generation systems. The detection of the islanding condition is determined by computing the intersection of the global ellipsoidal estimations. While the global estimations are integrated at each sampling instant with all the local ellipsoidal estimations provided respectively by the local estimators in the communication network. If the islanding fault exists, the intersection of the global ellipsoids would be empty. If the islanding condition does not happen, the intersection of the global ellipsoids would be non-empty. The conditions on the existence of the local and the global ellipsoidal estimation methods are derived by two convex optimization approaches respectively. A simulation is provided by Matlab based on a model of 2-kW single-phase grid-connected power generation system to illustrate the effectiveness of the designed scheme on the detection of the islanding fault.

A novel distributed adaptive output feedback control strategy is developed to solve the cooperative output regulation problem of heterogeneous general linear multi-agent systems(MASs) under switching topology. The proposed control strategy avoids using the minimal non-zero eigenvalue of Laplacian matrix when calculating gain matrix, and the communication topology is assumed to contain a directed spanning tree only frequently. It is shown that individual agents could track external signal asymptotically and achieve disturbance rejection. Ultimately, a simulation is presented to exemplify the effectualness of the main result.

This paper is concerned with optimal communication network-based H∞ quantized control for a discrete-time neural network with distributed time delay. Control of the neural network (plant) is implemented via a communication network. Both quantization and communication network-induced data packet dropouts are considered simultaneously. It is assumed that the plant state signal is quantized by a logarithmic quantizer before transmission, and communication network-induced packet dropouts can be described by a Bernoulli distributed white sequence. A new approach is developed such that controller design can be reduced to the feasibility of linear matrix inequalities, and a desired optimal control gain can be derived in an explicit expression. It is worth pointing out that some new techniques based on a new sector-like expression of quantization errors, and the singular value decomposition of a matrix are developed and employed in the derivation of main results. An illustrative example is presented to show the effectiveness of the obtained results.

In this paper, a quantized H∞control problem for networked control systems (NCSs) subject to randomly multi-step transmission delays is investigated. A quantizer is used before the measurement signal enters the communication network and the randomly multi-step transmission delays which are described by a mathematical model are considered during the transmission through the network. Sufficient conditions are derived for the considered system to satisfy the H∞norm constraint subject to the randomly multi-step transmission delays. Simulation results demonstrate the effectiveness of the proposed method.

In this paper, a linear unbiased minimum-variance filtering problem is considered for a class of systems with randomly multi-step sensor delays. A new mathematical model is established for the multi-step sensor delays. Different from the augmented method for dealing with delayed systems, a linear unbiased minimum-variance filter design method is proposed without augmenting the state vector, which effectively reduces the filter dimensions. A recursive algorithm for calculating the filter gain matrix is developed. The simulation results illustrate the effectiveness of the proposed method.

This paper is concerned with the problem of designing distributed event-triggered H∞filters over sensor networks subject to heterogeneous coupling intercommunication delays. A new distributed event-triggered scheme is proposed to determine whether or not each sensor's current sampled data should be broadcasted and transmitted to its underlying neighboring nodes through the communication network. In this scheme, each sensor node is able to make its own decisions to broadcast and transmit only when its local measurement output error exceeds a designed threshold. Heterogeneous coupling delays are incorporated in the intercommunication between the specific sensor node and its interacting neighbors. A refined technique is proposed to realize the complicated decoupling among the exchanged measurement outputs in the presence of coupling intercommunication delays. Then the resulting filter error system is modeled by a new delay system subject to finite time-varying “state” delays. Based on the Lyapunov-Krasovskii functional method, a sufficient condition for distributed event-triggered H∞filter design is established, from which the desired filter parameters and the triggering parameter in the event condition can be co-designed. The filter design problem is posed in terms of linear matrix inequalities. A quarter-car suspension model is finally presented to show the effectiveness and feasibility of the developed theoretical results.

This paper is concerned with event-triggered H∞filtering for networked systems. A novel event-triggering scheme is proposed by taking network dynamics into account simultaneously. First, an information dispatching middleware is constructed to establish a novel framework for networked systems, where two modules namely information selection module and congestion avoidance module are introduced. The information selection module aims to regulate the transmission of the sampled data in terms of a predefined event-triggering condition. The congestion avoidance module is used to schedule those sampled data released by the information selection module to the filter. Second, the on-line scheduling strategy is proposed under this framework. Then the filtering error system based on network dynamics is formulated as a system with an interval time-varying delay. Third, Lyapunov-Krasovskii functional approach is employed to formulate a new sufficient condition to ensure the stability and to guarantee a prescribed H∞noise attenuation performance for the filtering error system. Based on this condition, H∞filtering parameters, network dynamic controllers and event-triggering parameters can be co-designed provided that a set of linear matrix inequalities are feasible. Finally, an example is given to illustrate the merits and effectiveness of the method proposed in this paper.

This paper is concerned with event-triggered output feedback dissipative control of network-based systems. A novel distributed discrete event-triggered control strategy, in which whether or not the sampled data should be transmitted is determined by a pre-specified event, is proposed by introducing distributed discrete event-triggered mechanisms. Under this strategy, a novel dissipative control protocol is presented, with which the closed-loop system can be transformed into a time-delay system. Then, by Lyapunov-Krasovskii functional theory, a new dissipative criterion is established such that the resulting system is (Q, S, R)-dissipative. Correspondingly, based on this condition, the design method of the output feedback dissipative controller is proposed. An illustrative example is given to show the effectiveness and feasibility of the proposed method.

This paper investigates stability and passivity of negative feedback interconnection of two passive systems, which are interconnected through communication networks. The insertion of communication networks between negative feedback interconnected passive systems inevitably induces delays and data packet dropouts. To model the network-based negative feedback interconnected system, an appropriate network scheduling method is presented to deal with time-varying network-induced delays and data packet dropouts. By constructing a novel discontinuous Lyapunov-Krasovskii functional, a less conservative sufficient condition for the network-based feedback interconnected system to be asymptotically stable is derived. Based on the stability condition, a new sufficient condition to make the negative feedback interconnected system in network environments remain passive is developed. A numerical example is provided to demonstrate the effectiveness of the design method.

In this paper, we present a novel iterative LMI approach to deal with the control problem for networked control systems (NCSs) with multiple packet dropouts. Two channel packet dropouts are simultaneously considered due to limited communication capacity. One is measurement channel packet dropout which is from the sensor to the controller. The other is control channel packet dropout which is from the controller to the actuator. The NCSs with both multiple measurement and control packet dropouts are first modeled as a stochastic parameter system which contains two independent Bernoulli distributed white sequences. A dynamic output controller is then designed to exponentially stabilize the networked system in the sense of mean square, and also achieve the prescribed H∞disturbance attenuation level. An iterative algorithm is developed to compute the optimal H∞disturbance attenuation and the controller parameters by solving the semi-definite programming problem via interior-point approach. Finally, two illustrative examples are provided to show the applicability of the proposed method.

This study addresses the robust set-membership finite-horizon filtering problem for a class of discrete time-varying systems with missing measurement and polytopic uncertainties in the presence of unknown-but-bounded process and measurement noises. A robust set-membership filter is developed and a recursive algorithm is derived for computing the state estimate ellipsoid that is guaranteed to contain the true state. An optimal possible estimate set is computed recursively by solving the semi-definite programming problem. Simulation results are provided to demonstrate the effectiveness of the proposed method.

In this paper, a continuous-time Takagi-Sugeno (T-S) fuzzy system with impulsive effects that are controlled through network is investigated. Network signal-transmission delays and signal-quantization effects are simultaneously considered. The network is with two time-varying additive delays and limited capacity. First, a quantized output-feedback networked control system (NCS) model is established to describe the impulsive NCSs through a channel with limited capacity. Then, based on the Lyapunov-Krasovskii functional approach and a parallel-distributed compensation scheme, a delay-dependent stabilization approach is developed for the impulsive NCSs, which guarantees that the closed-loop system is asymptotically stable. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.

This paper is concerned with the set-membership filtering (SMF) problem for discrete-time nonlinear systems. We employ the Takagi-Sugeno (T-S) fuzzy model to approximate the nonlinear systems over the true value of state and to overcome the difficulty with the linearization over a state estimate set rather than a state estimate point in the set-membership framework. Based on the T-S fuzzy model, we develop a new nonlinear SMF estimation method by using the fuzzy modeling approach and the S-procedure technique to determine a state estimation ellipsoid that is a set of states compatible with the measurements, the unknown-but-bounded process and measurement noises, and the modeling approximation errors. A recursive algorithm is derived for computing the ellipsoid that guarantees to contain the true state. A smallest possible estimate set is recursively computed by solving the semidefinite programming problem. An illustrative example shows the effectiveness of the proposed method for a class of discrete-time nonlinear systems via fuzzy switch.

Variability in the analogue components of integrators in cascaded 2-1 sigma-delta modulators causes imperfect cancellation of first stage quantization noise, and reduced signal-to-noise ratio in analogue-to-digital converters. Design of robust matching filters based on low-frequency weighted convex optimization over uncertain linearized representations are mathematically very complex and computationally intensive, and offer little insight into the solution. This letter describes a design method based on formal optimization of a low-frequency uncertain linearized model of the modulator, and leads to a simple intuitive result which can shed light on the more complex models. Simulation results confirm the optimal properties of the filter.

This paper studies the fixed-order controllers design problem for multi-input-multi-output (MIMO) systems. Polynomial methods are employed to design a controller that guarantees all the closed-loop poles reside within given D-stability regions. An Hinfinoptimization approach is proposed to minimize the interaction between different channels of the MIMO system. Sufficient conditions for the existence of such a fixed-controller is established by using the linear matrix inequalities (LMIs) approach.

The reliable Hinfincontrol problem for time-varying delayed discrete-time systems is proposed for the case of a simultaneous presence of sensor failures. A more practical general mode of sensor is presented to investigate sensor failures. Based on a linear matrix inequality (LMI) technique, an observer-based reliable controller is designed, such that, both in normal and fault cases of sensor, the closed-loop system is asymptotically stable and has an Hinfindisturbance attenuation boundness. A numerical example is presented to demonstrate the applicability and effectiveness of the proposed approach.

Uncertainty in the integrators of 2-1 sigma-delta modulators causes imperfect cancellation of first stage quantization noise, and reduces signal-to-noise ratio in analogue-to-digital converters. Design of robust matching filters based on convex optimization over uncertain linearized state-space representations gives complicated models and high-order designs. This letter describes a polynomial design method leading to simpler multilinear models and fixed-order filters. The modulators are cast as a polynomial polytope, and filters satisfying an Hinfinbound arise from solving linear matrix inequalities (LMIs). Results at low frequency show the proposed filter outperforming the nominal one, with a performance close to the estimated optimum.

In this letter, an error square constrained filtering problem is considered for systems with both non-Gaussian noises and polytopic uncertainty. A novel filter is developed to estimate the systems states based on the current observation and known deterministic input signals. A free parameter is introduced in the filter to handle the uncertain input matrix in the known deterministic input term. In addition, unlike the existing variance constrained filters, which are constructed by the previous observation, the filter is formed from the current observation. A time-varying linear matrix inequality (LMI) approach is used to derive an upper bound of the state estimation error square. The optimal bound is obtained by solving a convex optimization problem via semi-definite programming (SDP) approach. Simulation results are provided to demonstrate the effectiveness of the proposed method.

In this paper, the robust H infinity control problem is considered for a class of networked systems with random communication packet losses. Because of the limited bandwidth of the channels, such random packet losses could occur, simultaneously, in the communication channels from the sensor to the controller and from the controller to the actuator. The random packet loss is assumed to obey the Bernoulli random binary distribution, and the parameter uncertainties are norm-bounded and enter into both the system and output matrices. In the presence of random packet losses, an observer-based feedback controller is designed to robustly exponentially stabilize the networked system in the sense of mean square and also achieve the prescribed H infinity disturbance-rejection-attenuation level. Both the stability-analysis and controller-synthesis problems are thoroughly investigated. It is shown that the controller-design problem under consideration is solvable if certain linear matrix inequalities (LMIs) are feasible. A simulation example is exploited to demonstrate the effectiveness of the proposed LMI approach.

In this letter, we consider the robust finite-horizon filtering problem for a class of discrete time-varying systems with missing measurements and norm-bounded parameter uncertainties. The missing measurements are described by a binary switching sequence satisfying a conditional probability distribution. An upper bound for the state estimation error variance is first derived for all possible missing observations and all admissible parameter uncertainties. Then, a robust filter is designed, guaranteeing that the variance of the state estimation error is not more than the prescribed upper bound. It is shown that the desired filter can be obtained in terms of the solutions to two discrete Riccati difference equations, which are of a form suitable for recursive computation in online applications. A simulation example is presented to show the effectiveness of the proposed approach by comparing to the traditional Kalman filtering method.

In this paper, we consider the robust finite-horizon filter design problem for a class of discrete time varying systems with both stochastic nonlinearities and deterministic uncertainties. The description of the stochastic nonlinearities is quite general, which comprises the state-multiplicative noises and the random sequences whose powers depend on either the sector-bound nonlinear function of the state or the sign of a nonlinear function of the state. The norm bounded parameter uncertainties are allowed to enter both the system and the output matrices. We aim to design a robust filter that guarantees an optimized upper bound on the state estimation error variance, for all stochastic nonlinearities and admissible deterministic uncertainties. The existence conditions for the desired robust filters are first derived, and the filter parameters are then determined in terms of the solutions to two recursive Riccati-like difference equations. A numerical example is presented to show the applicability of the proposed method.