This article addresses the problem of cooperative path following for wheeled mobile robots (WMRs) under system constraints and external bounded disturbances within a switching communication network, by proposing a robust distributed model predictive control (DMPC) strategy. First, the cooperative path-following task is decoupled into two subtasks using a modified virtual structure: a cooperative task involving virtual reference robots and an individual path-following task between each actual robot and its corresponding virtual reference. A time-like path parameter is introduced to generate predefined path information for the virtual reference robot in advance, enabling dynamic formation tracking. Subsequently, discrete-time error dynamics subject to external bounded disturbances are derived for each robot, and a centralized predictive control problem is formulated as a baseline. A nominal DMPC strategy is then developed for the disturbance-free case, followed by an extension to a robust DMPC formulation that accounts for nonzero disturbances. In this context, a stability constraint is incorporated to ensure closed-loop stability without relying on neighboring agents’ real-time information. Theoretical analysis confirms the feasibility of the proposed scheme and guarantees the convergence of system trajectories to a disturbance invariant set. Finally, simulation and experimental results validate the effectiveness of the proposed strategy in cooperative path-following scenarios involving WMRs.

This paper investigates the distributed secure fusion estimation problem under stochastic nonlinear hybrid attacks. Specifically, this work analyzes a hybrid attack scenario where the attacker employs a randomized approach to launch false data injection (FDI) attacks and Denial-of-Service (DoS) attacks on the measurement information communication channel. Then, an innovative distributed secure fusion estimation model is proposed, addressing three situations: DoS attacks, FDI attacks, and the absence of attacks. Following this, an existence condition is derived for the secure fusion estimator, utilizing probability-guaranteed set-membership filtering technology, to ensure that the fusion estimation error will consistently be bounded within an expected ellipsoid with the specified probability. Subsequently, a convex optimization problem involving constrained recursive matrix inequalities is formulated to compute the secure fusion estimation weight matrices. Finally, the effectiveness of the proposed probability-guaranteed set-membership secure fusion estimation (SSFE) algorithm is demonstrated through a simulation example. Note to Practitioners—The research in this paper is dedicated to addressing the problem of fusion state estimation in practical engineering tasks such as intelligent transportation, industrial manufacturing and military defense. With the increase in application requirements and process accuracy, the majority of projects demand that the true state must be bounded within a certain range, e.g., unmanned vehicle obstacle avoidance and missile precision strikes. To overcome this challenge, probability-guaranteed set-membership filtering is introduced to ensure that the fusion estimation error is bounded with a certain probability. However, due to the expanding scope of engineering applications, the system may be deployed to perform tasks in a non-secure environment, which increases the risk of malicious attacks that may lead to functional failures as well as performance degradation. Therefore, this paper simultaneously considers the scenario where the communication channels are subjected to the stochastic hybrid attacks, which can be effectively handled by utilizing the proposed probability-guaranteed SSFE algorithm. Preliminary simulations demonstrate the feasibility of the algorithm. Since the actual system may also encounter problems such as sensor energy constraints, bandwidth resource limitations, and data processing asynchrony. Therefore, in our future work, we will focus on addressing the various limitations present in the fusion estimation system and improving their resolution.

The coexistence of high-frequency phasor measurement units (PMUs) and conventional SCADA systems raises the challenge of heterogenous-source and multi-rate measurements which significantly degrades the performance of power system dynamic state estimation. In this work, a deep reinforcement learning (DRL) assisted robust cubature Kalman filtering (CKF) scheme is proposed to handle measurements from hybrid sources and with different time scales. In specific, a multi-rate measurement function reconstruction approach is designed with an independent discretization mechanism to lift the present limitation of requiring an integer multiple relationship of the sampling rates from multiple sources in most of existing works. Embedded with this discretization mechanism, a deep reinforcement learning assisted two-parameter linear exponential smoothing method is proposed to reconstruct the slow measurement model with online adjustable estimation parameters. A generalized correntropy loss criterion is also included in the robust CKF to counter the non-Gaussian noise and the noise distribution variation caused by the reconstruction. Comparisons results demonstrate that the proposed DRL-based robust CKF method can achieve better accuracy and robustness under various operating scenarios.

This paper addresses the problem of designing probability-guaranteed set-membership secure estimation algorithms for energy-constrained multi-sensor systems with multi-rate asynchronous samplings. An event-triggered strategy (ETS) is employed to minimize data transmission overhead while maintaining estimation accuracy by transmitting only essential data. A novel measurement model is proposed to accurately characterize the operation of the multi-sensor system under ETS, taking into account both high- and low-energy transmission (HLET) modes and random denial-of-service (DoS) attacks, which impact communication energy consumption and data security. To cope with the challenges posed by uncertain sampling periods, a new fusion estimation model is established, including a redefined fusion estimation weight matrix and the formulation of a probability-guaranteed set-membership secure fusion estimation algorithm. Furthermore, a recursive optimization algorithm based on linear matrix inequalities is utilized to determine the minimum ellipsoid of the design parameters. The effectiveness of the proposed algorithm is validated through simulation studies.

This article presents a comprehensive theoretical framework for addressing the problem of secure fusion estimation in energy-constrained multisensor systems, specifically targeting hybrid attacks in multiple transmission levels. The lifespan of sensor nodes is constrained by the availability of energy supply, and all the sensors have the flexibility to choose between high-energy or low-energy levels for transmitting their measurements. Sensor data becomes vulnerable to malicious tampering when operating in a low-energy level, whereas the high-energy transmission level enables accurate data transmission. By introducing a set of Bernoulli random variables and ternary random variables, a novel measurement model is proposed to characterize scenarios involving both dual-energy transmission modes and hybrid attacks, including three statuses: safe, deception attacks, and Denial-of-Service attacks. Based on the innovation analysis approach, local secure estimators are designed to ensure that the estimation errors are minimized locally. Then, an optimal secure fusion algorithm is provided to generate the final estimated value by fusing all the local estimates. Additionally, the proposed secure fusion estimation algorithm's stability and steady-state properties are investigated. Finally, two simulation cases are conducted to provide the empirical evidence of the superior performance of the proposed approach.

Learning an autonomous dynamic system (ADS) encoding human motion rules has been shown as an effective way for human motion skills transfer. However, most existing approaches focus on goal-directed motion skills transfer, and the study on periodic motion skills transfer is rare. One popular approach for periodic motion skills transfer is learning periodic dynamic movement primitive (DMP); however, periodic DMP is sensitive to spatial disturbances due to the introduction of the phase parameters. To solve this issue, this brief presents a novel approach to learn an ADS with a stable limit cycle without introducing phase parameters. First, a data-driven Lyapunov function (energy function) is learned, such that one of its level surfaces is consistent with periodic human demonstration trajectories. Then, an ADS is learned by sequentially solving energy function-related constrained optimization problems. With a proper design of constraint functions, we can ensure that the trajectory generated by the ADS will converge to an energy function-level surface, of which the shape is similar to periodic human demonstration trajectories. Experiments are conducted to show the effectiveness of the proposed approach (PA).

To defend the cyber–physical system (CPSs) from cyber-attacks, this work proposes an unified intrusion detection mechanism which is capable to fast hunt various types of attacks. Focusing on securing the data transmission, a novel dynamic data encryption scheme is developed and historical system data is used to dynamically update a secret key involved in the encryption. The core idea of the dynamic data encryption scheme is to establish a dynamic relationship between original data, secret key, ciphertext and its decrypted value, and in particular, this dynamic relationship will be destroyed once an attack occurs, which can be used to detect attacks. Then, based on dynamic data encryption, a unified fast attack detection method is proposed to detect different attacks, including replay, false data injection (FDI), zero-dynamics, and setpoint attacks. Extensive comparison studies are conducted by using the power system and flight control system. It is verified that the proposed method can immediately trigger the alarm as soon as attacks are launched while the conventional$\chi^{2}$detection could only capture the attacks after the estimation residual goes over the predetermined threshold. Furthermore, the proposed method does not degrade the system performance. Last but not the least, the proposed dynamic encryption scheme turns to normal operation mode as the attacks stop.

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.

Automatic modulation classification (AMC) is an important technology for the monitoring, management, and control of communication systems. In recent years, machine learning approaches are becoming popular to improve the effectiveness of AMC for radio signals. However, the automatic modulation open-set recognition (AMOSR) scheme that aims to identify the known modulation types and recognize the unknown modulation signals is not well studied. Therefore, in this paper, we propose a novel multi-modal marginal prototype framework for radio frequency (RF) signals (MMPRF) to improve AMOSR performance. First, MMPRF addresses the problem of simultaneous recognition of closed and open sets by partitioning the feature space in the way of one versus other and marginal restrictions. Second, we exploit the wireless signal domain knowledge to extract a series of signal-related features to enhance the AMOSR capability. In addition, we propose a GAN-based unknown sample generation strategy to allow the model to understand the unknown world. Finally, we conduct extensive experiments on several publicly available radio modulation data, and experimental results show that our proposed MMPRF outperforms the state-of-the-art AMOSR methods.

In this paper, using the ball and beam system as an illustration, a control scheme is developed on human-robot collaboration, i.e., a two-level hierarchical framework is proposed to establish a robust human-robot collaboration (HRC) policy. On the high level, a deep reinforcement learning (DRL) algorithm is presented to plan the desired beam rotational velocity. The low level is constructed by a human-intention perception module and a robust collaboration policy design module. For the first module, a probabilistic model is fitted by using the Gaussian process regression (GPR) approach to predict human-hand velocities, and prediction results follow Gaussian distributions where mean values and variances represent predicted human-hand velocities and corresponding prediction confidences, respectively. For the second module, a robust collaboration policy is established by fusing a proactive policy and a conservative policy, where the proactive policy is used to control the robot to achieve the desired beam rotational velocity by using the predicted human-hand velocities. The conservative policy is designed to ensure the collaboration safety. The weighted parameters for fusion are adaptively tuned based on the prediction precision and confidence. Experiments are conducted on controlling ball position on a beam jointly by a human and a robot with vision data, and experimental results show the effectiveness of the designed robust collaboration policy. Note to Practitioners—Predicting human future behaviors and moderating robot behaviors accordingly is a long-standing problem for human-robot collaboration (HRC) tasks, such as assembling, transporting, etc. Existing approaches generally regard human behaviors as noises or only build simple human models without prediction confidence. This paper proposes a learning-based hierarchical framework that will derive a robust and safe HRC policy considering human behaviors, prediction confidence, and task-related optimality. The framework is validated by a representative experiment where human and robot are asked to jointly control a ball and beam system.

Relying only on inertial measurement units (IMUs) for robust state estimation is critical to vehicle safety when imaging sensors abruptly fail. In this paper, we propose to consider learning-based method as a complement to the kinematic model, and obtain ego-motion based on the nonlinear filter pipeline. To be specific, we first model the state of the IMU on the manifold such that the beliefs of prior model are propagated correctly. Then, we construct an uncertainty-aware network to simultaneously learn the integral terms in the kinematic equations, and recursively compute the rigid body position and velocity as pseudo-measurements. We additionally use a nonlinear estimator to properly fuse the model with the learned observations, whose prior information is endowed by model on the manifold, while the updating correction signals are provided by the network on pattern learning, and finally, the split covariance intersection (SCI) is utilized to reasonably handle the unknown correlated information in both. The performance of method is evaluated in terms of accuracy, robustness, extensibility and server aspects using both simulated and real-world dataset. Experimental results demonstrate a promising performance of the proposed method to traditional or learning-based ones.

In recent years, a surging number of unmanned aerial vehicles (UAVs) are pervasively utilized in many areas. However, the increasing number of UAVs may cause privacy and security issues such as voyeurism and espionage. It is critical for individuals or organizations to manage their behaviors and proactively prevent the misbehaved invasion of unauthorized UAVs through effective anomaly detection. The UAV anomaly detection framework needs to cope with complex signals in the noisy-prone environments and to function with very limited labeled samples. This paper proposesBisSiam, a novel framework that is capable of identifying UAV presence, types and operation modes.BisSiamconverts UAVs signals to bispectrum as the input and exploits a siamese network based contrastive learning model to learn the vector encoding. A sampling mechanism is proposed for optimizing the sample size involved in the model training whilst ensuring the model accuracy without compromising the training efficiency. Finally, we present a similarity-based fingerprint matching mechanism for detecting unseen UAVs without the need of retraining the whole model. Experiment results show that our approach outperforms other baselines and can reach 92.85% accuracy of UAV type detection in unsupervised learning scenarios. 91.4% accuracy can be achieved whenBisSiamis used for detecting the UAV type of the out-of-sample UAVs.

Industry 4.0 requires new production models to be more flexible and efficient, which means that robots should be capable of flexible skills to adapt to different production and processing tasks. Learning from demonstration (LfD) is considered as one of the promising ways for robots to obtain motion and manipulation skills from humans. In this article, a framework that enables a wheel mobile manipulator to learn skills from humans and complete the specified tasks in an unstructured environment is developed, including a high-level trajectory learning and a low-level trajectory tracking control. First, a modified dynamic movement primitives (DMPs) model is utilized to simultaneously learn the movement trajectories of a human operator's hand and body as reference trajectories for the mobile manipulator. Considering that the auxiliary model obtained by the nonlinear feedback is hard to accurately describe the behavior of mobile manipulator with the presence of uncertain parameters and disturbances, a novel model is established, and an unscented model predictive control (UMPC) strategy is then presented to solve the trajectory tracking control problem without violating the system constraints. Moreover, a sufficient condition guaranteeing the input to state practical stability (ISpS) of the system is obtained, and the upper bound of estimated error is also defined. Finally, the effectiveness of the proposed strategy is validated by three simulation experiments.

Learning a stable autonomous dynamic system (ADS) encoding human motion rules has been shown as an effective way for demonstration learning. However, the stability guarantee may sacrifice the demonstration learning accuracy. This article solves the issue by learning a stability certificate, represented by a neural energy function, on the demonstration set. We propose a polarlike space analysis approach to derive parameter constraints to guarantee the unique-minimum property of the neural energy function, which is essential for it to be a cogent stability certificate. Then, the neural energy function is learned to capture the demonstration preferences via constrained optimization algorithms. With the learned neural energy function, a globally asymptotically stable ADS with predefined position constraint is further formulated. We also quantitatively analyze the generalization ability of the learned ADS by utilizing the substantial flexibility of the neural energy function. The effectiveness of the proposed approach is validated on the LASA dataset and two representative robotic experiments.

Covariance intersection (CI) fusion is one of the most popular methods for combining estimates when the correlations among local estimation errors are unknown. Considering practical communication constraints, CI fusion tends to be performed in hierarchical and sequential forms, i.e., hierarchical CI (HCI) fusion and sequential CI (SCI) fusion. However, existing HCI and SCI fusion are sensitive to some uncertainties, i.e., the hierarchy structure and the fusion order, which make their fusion performances unreliable. To solve this problem, this article proposes hierarchy-structure-independent HCI fusion and fusion-order-independent SCI fusion by analogy with batch CI fusion, which can avoid possible negative effects caused by the aforementioned uncertainties. Finally, two simulations verify the effectiveness and advantages of the proposed methods.

This article addresses the problem of cooperation and coordination transportation for decoupling nonholonomic mobile manipulators (NMMs) in a workspace with obstacles. We propose a distributed model predictive control (MPC) approach for a team of NMMs to transport a target object while satisfying significant constraints and limitations, such as the feasible state and control input constraints, parameter synchronization constraints, and obstacles within the workspace. First, under the framework of the decoupling dynamics, an auxiliary dynamics model for task-space end-effectors and null-space mobile bases is obtained by the nonlinear feedback technique based on the Euler–Lagrange description of the NMMs. Using the modified virtual structure method, the cooperation and coordination transportation problem for NMMs is simplified as two independent synchronization tracking control problems for task-space end-effectors and null-space mobile bases. A distributed constrained optimization problem is established by taking the parameter synchronization and system constraints into the cost function. A general projection neural network (GPNN) approach is employed to solve the optimization problem and obtain the optimal control input. Moreover, a sufficient condition that guarantees the stability of the closed-loop system is further developed. Simulation results show that the proposed cooperation and coordination transportation strategy is feasible and effective.

This paper proposes a quadratic programming (QP)-based variable impedance control (VIC) algorithm to solve contact-rich trajectory tracking problems with impedance, position and velocity constraints. To the best of our knowledge, the impedance constraints which are significant to ensure the worst contact compliance have never been considered in other previous works. To handle the impedance constraints of the VIC algorithm, a novel impedance model where the impedance parameters are directly served as the control input is established. The impedance-constrained VIC design problem is then formulated as a QP problem which can be efficiently solved. To handle the position and velocity constraints, a complementary force is introduced into the novel impedance model. The complementary force will appear to prevent the constraints violation when the robot approaches the constrained area. The design problem of the complementary force is also transformed into a QP problem. Combing these two QP solutions, the VIC algorithm with both impedance, position and velocity constraints can be obtained. Finally, various experiments are conducted to show the effectiveness of the proposed QP-based constrained VIC algorithm.

This article studies the distributed dimensionality reduction fusion estimation problem with communication delays for a class of cyber-physical systems (CPSs). The raw measurements are preprocessed in each sink node to obtain the local optimal estimate (LOE) of a CPS, and the compressed LOE under dimensionality reduction encounters with communication delays during the transmission. Under this case, a mathematical model with compensation strategy is proposed to characterize the dimensionality reduction and communication delays. This model also has the property of reducing the information loss caused by the dimensionality reduction and delays. Based on this model, a recursive distributed Kalman fusion estimator (DKFE) is derived by optimal weighted fusion criterion in the linear minimum variance sense. A stability condition for the DKFE, which can be easily verified by the exiting software, is derived. In addition, this condition can guarantee that the estimation error covariance matrix of the DKFE converges to the unique steady-state matrix for any initial values and, thus, the steady-state DKFE (SDKFE) is given. Note that the computational complexity of the SDKFE is much lower than that of the DKFE. Moreover, a probability selection criterion for determining the dimensionality reduction strategy is also presented to guarantee the stability of the DKFE. Two illustrative examples are given to show the advantage and effectiveness of the proposed methods.

In this article, pinning control has been applied to stabilize a kind of stochastic network with finite states called probabilistic logical networks (PLNs). First, the solvability of pinning controllers, including the selection of pinning nodes and the corresponding control design, is addressed to guarantee the stabilization of PLNs. Then, based on the complete matrices set, one necessary and sufficient condition is obtained for PLNs to be stabilized by exact p nodes. In addition, an algorithm is presented for obtaining the minimal number of pinning nodes and how exact they are. As an application, our algorithm is used to calculate the minimal number of pinning nodes for the stabilization of logical networks (LNs). Examples are given to illustrate the process of choosing pinning nodes and show the efficiency of the obtained results.

This article is concerned with the distributed Kalman filtering problem for interconnected dynamic systems, where the local estimator of each subsystem is designed only by its own information and neighboring information. A decoupling strategy is developed to minimize the impact of interconnected terms on the estimation performance, and then the recursive and distributed Kalman filter is derived in the minimum mean-squared error sense. Moreover, by using Lyapunov criterion for linear time-varying systems, stability conditions are presented such that the designed estimator is bounded. Finally, a heavy duty vehicle platoon system is employed to show the effectiveness and advantages of the proposed methods.

Currently, numerical optimization methods are used to solve distributed optimal power allocation (OPA) problems for islanded microgrid (MG) systems. Most of them are developed based on rigorous mathematical derivation. However, the complexity of such optimization algorithms inevitably creates a gap between theoretical analysis and real-time implementation. In order to bridge such a gap, in this article we provide a new distributed learning-based framework to solve the real-time OPA problem. Specifically, inspired by the human-thinking scheme, distributed deep neural networks (DNNs) together with a dynamic average consensus algorithm are first employed to obtain an approximate OPA solution in a distributed manner. Then a distributed balance generation and demand algorithm is designed to fine-tune it to obtain the final optimal feasible solution. In addition, it is theoretically proved that the proposed DNN can well approximate one existing OPA algorithm (Guo et al. 2018), where quantitative numbers of at most how many hidden layers and neurons are provided. Several experimental case studies show that our proposed distributed learning framework can achieve similar optimal results to those obtained by using typical existing distributed numerical optimization methods while it is superior in terms of simplicity and real-time capability.

In order to ensure that the power system frequency and tie-line power remain at the nominal value when the load fluctuates, while reducing the release number of decentralized sensor, this work presents a novel adaptive event-triggered scheme for the load frequency regulation via designing the decentralized dynamic output feedback controller (DOFC), where the communication delay issue is also considered due to communication constraints. Distinct from the existing ones, the proposed adaptive event-triggered strategy automatically tunes the threshold according to the local extremum of the system output signal, which can significantly reduce the number of unnecessary signal transmissions to ensure system performance. First, the proposed adaptive event-triggered transmission scheme is integrated with the decentralized DOFC and communication delays under the framework of a linear time-delay system. Then, the asymptotic stability of the closed-loop power system is analyzed through the Lyapunov stability theory and a procedure is given for the design of decentralized dynamic output load frequency controllers by solving some linear matrix inequalities (LMIs). Finally, a three-area power system is used to verify the effectiveness and usefulness of the proposed results.

This article presents a novel dynamic event-triggered scheme for the load frequency regulation with periodic denial-of-service (DoS) attacks and deception attacks via decentralized output-based control algorithm. Compared with the existing event-triggered strategy, the proposed one automatically changes the parameters of the triggered condition by detecting the frequency trend of the DoS attack to change the release frequency, which can ensure the stability of the power system while increasing the probability of effective transmission subject to DoS attack and thus, reducing network bandwidth usage. First, the proposed dynamic event-triggered strategy combined with the decentralized output-based controller is presented in a unified framework to deal with deception attacks and DoS attacks in the multiarea power system. Then, we utilize the Lyapunov stability theory to analyze the exponential stability in the mean-square sense and the robustness of the power system. By solving a set of linear matrix inequalities (LMIs), a procedure is given for the design of output-based load frequency controllers. Finally, a three-area power system is exploited as a simulation to verify the effectiveness of the proposed results.

This article tries to provide a new alternative approach to solve the economic dispatch (ED) problem in a smart grid system. Such a problem has been widely studied recently with several advanced numerical optimization algorithms being proposed. However, most of these numerical algorithms may suffer from high computational cost for on-line optimization. In this article, we aim to address this problem by proposing a learning-based optimization strategy. The key idea is to regard the optimization strategy of the ED problem as an unknown mapping relationship. With the help of traditional ED optimization algorithms to obtain the ground truth, we employ a deep neural network (DNN) to learn the ED optimization strategy and use it for online ED. In particular, our main contribution in this article is to theoretically show that one popular ED algorithm, i.e.,$\lambda $-iteration algorithm, can be accurately approximated by a well-constructed DNN with finite network size. Moreover, dynamic units status of dispatchable generators is also considered and can be well solved by our proposed approach. Furthermore, several simulation case studies implemented on a 3-unit power system and an IEEE-30 bus power system validate the effectiveness of our proposed method.

As large-scale power systems are more and more closely integrated with remote transmission technologies, they are also affected by malicious factors in the cyber and physical layers when bringing convenience. In this article, we propose a novel adaptive event-triggered strategy and apply to the multi-area power system to deal with the load frequency control (LFC) problem with network-induced delay and stochastic sensor faults based on the discrete-time sliding mode control (DSMC) technique. Compared with existing event-triggered strategies, the proposed event-triggered strategy dynamically adjusts the threshold according to system state fluctuations, which can improve the system's tolerance for sensor faults and reduce the number of transmitted packets. Firstly, a dynamic LFC model combining network-induced delay, sensor faults, adaptive event-triggered strategy and DSMC is proposed by using the analysis method of time-delay system. Then we devise an appropriate discrete-time sliding surface for each subsystem in the networked power systems. The Lyapunov stability theory is used to analyze the asymptotic stability and robustness of each subsystem, and the decentralized controller design method is derived. Finally, some simulation examples are introduced to confirm the effectiveness of the proposed adaptive event-triggered DSMC approach.

In this paper, we study the stabilization and finite-time stabilization of probabilistic Boolean control networks (PBCNs). A complete family of reachable sets is defined first, based on which, feedback stabilization conditions are obtained. Then a way to find all possible state feedback controllers are presented for the stabilization of PBCNs accordingly. Moreover, it has been stated that the approach in this paper can also be applied to finite-time stabilization via some changes in the construction of set sequence. Finally, an evolutionary networked game is given as an example to illustrate the efficiency of the obtained results.

This article is concerned with the distributed networked fusion estimation problems for unstable systems with limited communication capacity, where the system and measurement noises are unknown but bounded. To overcome the unboundedness of measurement information for unstable systems, it is proposed to quantize the innovations that are sent to the fusion center over bandwidth constrained channels. By using the bounded recursive optimization idea, the design problems of local stable estimators and distributed fusion criterion under the quantized innovations are converted into two different convex optimization problems that can be easily solved by the standard software. The target tracking system is employed to demonstrate the effectiveness of the proposed methods.

This article addresses the distributed secure platoon control of connected vehicles with denial-of-service (DoS) attack phenomena, which may occur at some sampling time instant. First, a switched time-delay system model is introduced, which captures the time-varying sampling and the DoS attack phenomena simultaneously. Then, sufficient conditions are obtained based on the Lyapunov stability theory, the Jensen’s Inequality method, and the topology matrix decoupling technique, such that the vehicle platoon system under consideration achieves an exponential tracking performance. In this article, the presented system design conditions establish several quantitative relationships between attack parameters and system performance. Moreover, the critical values of attack frequency (AF) and the sampling interval (SI) are also derived, respectively. Finally, both of the simulation and experiment studies on a network of four vehicles are introduced to validate the design.

This paper investigates the modeling and stabilization problem for cyber-physical systems (CPSs) under time-delay attacks. First of all, the attack-induced delays are divided into multiple equilong subintervals and each subinterval is separated into a nominal part and an uncertain part. Then, the system is considered to dwell in different subsystems when the attack-induced delays fall into different subintervals. In this way, the CPS is modeled as a discrete-time switched system with norm-bounded uncertainties. Moreover, the mode-dependent controller is designed to defend the time-delay attacks and guarantee the exponential stability of the closed-loop CPS, which is switched according to the attack-induced delays and implemented combined with the packet-based control strategy. Finally, the experiments of a networked inverted pendulum control system are given to demonstrate the effectiveness of the proposed method.

This paper studies the alarm response problem of false data injection (FDI) attacks for nonlinear physical dynamical process in cyber-physical systems. Considering the real-time attack detecting, multi-sensor fusion strategy is used to enhance the reliabilty which can also potentially improve the detection speed. Multiple finite-level logarithmic quantizers are used for estimators to reduce the size of data packages containing residual message due to the limited bandwidth. Then the optimal weight for each local estimator is derived by solving a predefined convex optimal problem. By using the proposed fusion method, a more accurate evaluation threshold is obtained, which further improves the performance of alarm response. At last, a simulation example of civil aircraft is used to illustrate the effectiveness of the proposed method.

Existing distributed localization methods rarely consider the location privacy preservation problem, which however is nonnegligible. Regarding location privacy, typical solutions rely on a curious-but-honest model, requesting that all participants follow the rule. Different from the existing studies, both honest and dishonest models are considered in this article. We first propose a privacy-preserving distributed localization algorithm (PP-DILOC) by adopting a noise-adding mechanism under the curious-but-honest model. The performance of localization and privacy preservation of PP-DILOC are both theoretically analyzed. Then, in the presence of dishonest nodes, we propose a resilient PP-DILOC (RPP-DILOC), where a time-varying relax factor and an adversary detection procedure are added into PP-DILOC. Theoretical results provide sufficient conditions for the convergence of RPP-DILOC. The privacy levels and the localization performance in the absence/presence of dishonest nodes are evaluated through numerical and experimental results.

This paper is concerned with the distributed estimation problem in sensor networks subjected to unknown attacks. Network attacks are considered to exist in two classes of channels: 1) communication channels from the plant to sensors and 2) communication channels among sensors. The status of an attack is viewed as a stochastic phenomenon, and the transmitted information will be affected when the attacker successfully carries out an attack on the related data packet. Based on the sensors' own measurements and their neighbors' local information, a novel distributed estimation model against two-channel stochastic attacks is presented. A sufficient condition on the existence of the desired distributed H∞estimators is derived and the distributed estimator gains are designed by solving a linear matrix inequality. Two illustrative examples are provided to demonstrate the effectiveness of the new design techniques.

This paper studies the position synchronization control problem for networked multiaxis motion systems (NMAMSs). First, a position synchronization error model is established for the multiaxis motion system, and the uncertainty induced by the network-induced delay is modeled as an additive disturbance of the system. Second, the delay-induced uncertainty and the external disturbances such as load torque variation are lumped together as a total disturbance in the system model. Based on the established position synchronization error model, a generalized extended state observer (GESO) is designed to estimate the lumped disturbance and system states simultaneously. Then, the GESO-based synchronization controller is designed to achieve the objective of position synchronization and disturbance rejection, and the effect of the network-induced delay in the synchronization performance is significantly reduced. Moreover, an input-to-state stability condition is presented for the position synchronization system. Finally, experiments on a four-motors position synchronization control platform are presented to demonstrate the effectiveness and superiority of the proposed method.

This article is concerned with the multisensor fusion estimation for target tracking with range-only wireless sensor networks. By employing a nonlinear transformation and a measurement fusion, the nonlinear distance measurements are transformed into a linear measurement with respect to the position of the target, which avoids the instability problem of nonlinear filtering. However, after the transformation, the new measurement noises are no longer Gaussian and cross uncorrelated. Taking the unmodeled disturbances into account, as well as the new noise properties, an adaptive factor is introduced by hypothesis test based on the posterior residual to improve the estimation performance, where only the root of a quadratic equation is required to be solved. Finally, both simulations and experiments of a target tracking example are presented to show the effectiveness of the proposed methods.

This paper is concerned with the set-membership estimation problem for complex networks subject to unknown but bounded attacks. Adversaries are assumed to exist in the nonsecure communication channels from the nodes to the estimators. The transmitted measurements may be modified by an attack function with added noise that is determined by the adversary but unknown to the estimators. A novel set-membership estimation model against unknown but bounded attacks is presented. Two sufficient conditions are derived to guarantee the existence of the set-membership estimators for the cases that the attack functions are linear and nonlinear, respectively. Two strategies for the design of the set-membership estimator gains are presented. The effectiveness of the proposed estimator design method is verified by two simulation examples.

This paper studies the extended state observer (ESO)-based distributed model predictive control (DMPC) approach to deal with multiple mobile robot formation with unknown disturbances. The distributed control problem with path parameters synchronization and disturbance rejection is formulated for formation system according to the tracking error dynamic model, where the reference paths are parameterized. A local distributed controller is designed by using DMPC strategy for each mobile robot in the absence of disturbance by including parameter synchronization constraints in the quadratic performance index as coupling terms. The DMPC optimization problem is solved by using Nash-optimization iteration strategy with the maximum number of iteration constraint. To improve the ability of anti-jamming, a feedforward compensation controller is designed by using ESO method, where the ESO is designed by pole assignment. The convergence of the proposed iterative algorithm is given. Furthermore, the input-to-state stability property of the proposed composite controller, combining a feedforward compensation controller and local distributed controller, is analyzed for the closed-loop system. Finally, the validity of the proposed algorithm is verified by two simulation examples.

This paper presents a hierarchical estimation method for maneuvering target tracking in event-triggered wireless sensor networks. First, several process noise covariances are chosen to characterize the dynamic characteristic of the target in the presence of maneuvers, and a bank of decentralized extended information filters (DEIFs) are used to generate state estimates of the target. Second, the estimates from the DEIFs are combined by covariance intersection (CI) to obtain an improved state estimate while still maintaining a consistent estimate. Thus, the DEIF and the CI methods form complementary advantages by satisfying the requirement of the consistency in the hierarchical estimation framework. Finally, both simulations and experiments of a target tracking example demonstrate that the proposed method is more suitable for applications to the maneuvering target tracking and it achieves a more satisfactory performance than the conventional DEIF method.

This paper studies the distributed dimensionality reduction fusion estimation problem for a class of cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. The problem is modeled under the resource constraints (i.e., bandwidth or energy) for the defender and attacker. Based on a new attack and compensation model, a recursive distributed Kalman fusion estimator (DKFE) is designed for the addressed CPSs. Though the optimization objects of the defender and attacker are opposite, the corresponding optimization problems are established based on different available information. In this case, an explicit form of suboptimal dimensionality reduction is given against DoS attacks, while an effective attack strategy is proposed for the attacker. A stability condition is derived such that the mean square error of the designed DKFE is bounded. Two illustrative examples are given to show the effectiveness of the proposed methods.

State estimation plays an essential role in the monitoring and supervision of cyber-physical systems (CPSs), and its importance has made the security and estimation performance a major concern. In this case, multisensor information fusion estimation (MIFE) provides an attractive alternative to study secure estimation problems because MIFE can potentially improve estimation accuracy and enhance reliability and robustness against attacks. From the perspective of the defender, the secure distributed Kalman fusion estimation problem is investigated in this paper for a class of CPSs under replay attacks, where each local estimate obtained by the sink node is transmitted to a remote fusion center through bandwidth constrained communication channels. A new mathematical model with compensation strategy is proposed to characterize the replay attacks and bandwidth constrains, and then a recursive distributed Kalman fusion estimator (DKFE) is designed in the linear minimum variance sense. According to different communication frameworks, two classes of data compression and compensation algorithms are developed such that the DKFEs can achieve the desired performance. Several attack-dependent and bandwidth-dependent conditions are derived such that the DKFEs are secure under replay attacks. An illustrative example is given to demonstrate the effectiveness of the proposed methods.

This paper investigates the disturbance decoupling problem (DDP) of Boolean control networks (BCNs) by event-triggered control. Using the semi-tensor product of matrices, algebraic forms of BCNs can be achieved, based on which, event-triggered controllers are designed to solve the DDP of BCNs. In addition, the DDP of Boolean partial control networks is also derived by event-triggered control. Finally, two illustrative examples demonstrate the effectiveness of proposed methods.

This paper studies the observer based fault tolerant tracking control problem for linear multiagent systems with multiple faults and mismatched disturbances. A novel distributed intermediate estimator based fault tolerant tracking protocol is presented. The leader's input is nonzero and unavailable to the followers. By applying a projection technique, the mismatched disturbances are separated into matched and unmatched components. For each node, a tracking error system is established, for which an intermediate estimator driven by the relative output measurements is constructed to estimate the sensor faults and a combined signal of the leader's input, process faults, and matched disturbance component. Based on the estimation, a fault tolerant tracking protocol is designed to eliminate the effects of the combined signal. Besides, the effect of unmatched disturbance component can be attenuated by directly adjusting some specified parameters. Finally, a simulation example of aircraft demonstrates the effectiveness of the designed tracking protocol.

The distributed filtering problem is addressed in this paper for the discrete-time Takagi–Sugeno (T–S) fuzzy systems with incomplete measurements. The system under consideration includes various network-induced uncertainties, e.g., sensor saturation, quantization error, communication delay, and packet dropouts. Specifically, all these uncertainties are assumed to occur in a stochastic way. In addition, the measurement scheduling issue is also addressed such that only a portion of measurements are broadcasting due to the communication constraints. The main focus is on the design of distributed filters based on the information received locally and from the neighborhood such that the desired estimation performance is guaranteed in terms of the decay rate and disturbance attenuation level. Based on the Lyapunov stability theory, the existence condition for such filters is first proposed and the filter gain parameters are then determined by solving an optimization problem. A simulation example is finally presented to illustrate the effectiveness of the new filtering techniques.

During the past few years, finite control set predictive current control (PCC) method has attracted more and more attention in research and industry applications. However, PCC method could be improved by considering two points. First, the current reference used in the cost function of PCC control scheme is usually produced by a proportional and integration speed controller. Confront with load torque and time-varying system parameters, it is a better solution to design a disturbance estimation based feed-forward compensated controller. In this way, the current reference could be generated faster and more accurate. Second, the PCC method is model-based method which means the accuracy of the model parameters are essential. In real system, time-varying parameters existed almost always. This paper investigates a generalized proportional integral observer based PCC approach for dealing with load torque disturbance, time-varying parameter uncertainties. The effectiveness of the proposed method has also been confirmed by simulation and a lab-constructed experimental prototype.

The containment control problem for generally linear multiagent systems with aperiodic sampling intervals and measurement size reduction is considered in this paper. Under the assumption that the sampling interval changes from a finite set, an improved protocol is proposed, such that a larger sampling interval can be obtained to achieve containment. By using the properties of Laplacian matrix and the newly developed protocol, the containment control problem is transformed into the stability problem of a discrete-time switched linear system. A sufficient condition is obtained that ensures all the followers converge to the convex hull formed by the state of leaders, and such a sufficient condition is presented in terms of linear matrix inequalities, which are independent of the node of network. To further reduce the communication among agents, a switching-type measurement size reduction scheme is introduced. An optimization problem is proposed for the corresponding controller design. Finally, two simulation studies are conducted to show the effectiveness and advantage of the proposed control algorithms.

This paper is concerned with the asynchronous state estimation for a class of discrete-time switched complex networks with communication constraints. An asynchronous estimator is designed to overcome the difficulty that each node cannot access to the topology/coupling information. Also, the event-based communication, signal quantization, and the random packet dropout problems are studied due to the limited communication resource. With the help of switched system theory and by resorting to some stochastic system analysis method, a sufficient condition is proposed to guarantee the exponential stability of estimation error system in the mean-square sense and a prescribed performance level is also ensured. The characterization of the desired estimator gains is derived in terms of the solution to a convex optimization problem. Finally, the effectiveness of the proposed design approach is demonstrated by a simulation example.

De novo protein structure prediction aims to search for low-energy conformations as it follows the thermodynamics hypothesis that places native conformations at the global minimum of the protein energy surface. However, the native conformation is not necessarily located in the lowest-energy regions owing to the inaccuracies of the energy model. This study presents a differential evolution algorithm using distance profile-based selection strategy to sample conformations with reasonable structure effectively. In the proposed algorithm, besides energy, the residue-residue distance is considered another measure of the conformation. The average distance errors of decoys between the distance of each residue pair and the corresponding distance in the distance profiles are first calculated when the trial conformation yields a larger energy value than that of the target. Then, the distance acceptance probability of the trial conformation is designed based on distance profiles if the trial conformation obtains a lower average distance error compared with that of the target conformation. The trial conformation is accepted to the next generation in accordance with its distance acceptance probability. By using the dual constraints of energy and distance in guiding sampling, the algorithm can sample conformations with lower energies and more reasonable structures. Experimental results of 28 benchmark proteins show that the proposed algorithm can effectively predict near-native protein structures.

In this paper, we consider a periodic estimation problem in sensor networks with a shared communication channel. The transmission constraint is inevitable in a single-channel-based sensor network if the sensors are heterogeneous or deployed far away from each other. A novel stochastic competitive transmission strategy is presented to deal with the transmission constraint, such that the sensors communicate with the fusion center (FC) in a strict asynchronous manner. A periodic mixed storage strategy combing the zero-input and the hold-input mechanisms is presented to describe periodic updating of the stored information in the sensors' buffers. A recursive Kalman filtering algorithm is derived for the FC to periodically generate estimates of state variables describing an object by using a linear continuous-time stochastic model. Two simulation examples are presented to show the effectiveness of the proposed results.

This paper is concerned with the energy-efficient distributed filtering in sensor networks, and a unified switched system approach is proposed to achieve this goal. For the system under study, the measurement is first sampled under nonuniform sampling periods, then the local measurement elements are selected and quantized for transmission. Then, the transmission rate is further reduced to save constrained power in sensors. Based on the switched system approach, a unified model is presented to capture the nonuniform sampling, the measurement size reduction, the transmission rate reduction, the signal quantization, and the measurement missing phenomena. Sufficient conditions are obtained such that the filtering error system is exponentially stable in the mean-square sense with a prescribed H∞ performance level. Both simulation and experiment studies are given to show the effectiveness of the proposed new design technique.

This paper studies the distributed robust fusion estimation problem with stochastic and deterministic parameter uncertainties, where the covariance of the Gaussian white noise is unknown, and the covariances of the random variables in the stochastic uncertainties are in a bounded set. By using the discrete-time stochastic bounded real lemma and the matrix analysis approach, each local robust estimator is derived to guarantee an optimal estimation performance for admissible uncertainties, and then necessary and sufficient condition for the distributed robust fusion estimator is presented to obtain an optimal weighting fusion criterion. Note that the local robust estimation problem and the distributed robust fusion estimation problem are both converted into convex optimization problems, which can be easily solved by standard software packages. The advantage and effectiveness of the proposed methods are demonstrated through state monitoring for target tracking system and stirred rank reactor system.

This paper investigates the problem of sensor-network-based distributed control for large-scale networked control systems, in which the communication constraint and topology switching problems are addressed. In the considered system, a discrete-time interconnected process is controlled by a network of sensors, controllers, and actuators that collect information about the plant, and apply control actions to manage the plant dynamics. The motivation for this paper is that the communication between the plant network and the controller network can be exploited for the system-wide purpose. To deal with the communication constraint problem, strategies such as the event-based communication and logarithmic quantization are applied. Furthermore, in a networked environment, the real-time information of the topology switching is not always available at the controller network side, hence a bunch of synchronous/asynchronous controllers are designed such that the closed-loop system is exponentially stable and achieves a prescribed H∞disturbance attenuation level. Finally, an illustrative example on a benchmark continuous stirred-tank reactor system is presented to demonstrate the effectiveness of the proposed new control technique.

This paper is concerned with the state estimation problem for a class of nonlinear cyber-physical systems (CPSs) where the nonlinear dynamical physical process is approximated by a Takagi-Sugeno fuzzy model. The physical plant is measured by a set of wireless sensors and the sensors communicate with the remote estimator via a communication channel. In the considered CPS, the randomly occurring sensor saturation, signal quantization, packet dropouts as well as the medium access constraint are studied in a unified framework. We develop a sufficient condition such that the filtering error system is asymptotically stable in the mean-square sense and also with a prescribed H∞performance level. The filter gain parameters are determined by solving a convex optimization problem. Finally, the simulation study on the networked truck-trailer system is presented to show the effectiveness of the proposed estimator design.

This paper is concerned with the leader-follower consensus of multiagent systems with wireless communications with the main objective of reducing the power consumption. First, by assuming that the sampling period jumps from one to another only from a given set, a new stochastic sampling approach is introduced to reduce the sampling frequency of each agent. Then, only 1-D of the sampled data is selected, and transmitted to its neighboring agents. Finally, each agent is scheduled to communicate with others intermittently. A unified Markovian system model is proposed to capture the above stochastic sampling, measurement selection scheme and intermittent transmission process, and such a novel protocol can significantly reduce the power consumption. Based on the Lyapunov stability theory and the Markovian jump system approach, the distributed consensus-based controller gain is obtained by solving an optimization problem. The advantage of the proposed consensus protocol is verified by two simulation examples. The simulation results explicitly show our result is more energy-efficient than that of existing one.

Differential evolution is a fast, robust, and simple population-based stochastic search algorithm for global optimization, which has been widely applied in various fields. However, there are many mutation strategies in DE, which have their own characteristics. Therefore, choosing a best mutation strategy is not easy for a specific problem. Different mutation strategies may be appropriate during different stages of the evolution. In this paper, we propose a DE with multi-stage strategies (DEMS). In DEMS, the evolution process of DE is divided into multiple stages according to the average distance between each individual in the initial population. Each stage has its own strategy candidate pool which includes multiple effective strategies. At the beginning of each generation, the average distance between each individual is first calculated to determine the evolution stage. Then for each target vector in the current population, a mutation strategy is randomly selected from the strategy candidate pool with respect to the stage to produce a offspring vector. Numerical experiments on 15 well-known benchmark functions and the CEC 2015 benchmark sets show that the proposed DEMS is significantly better than, or at least comparable to several state-of-the-art DE variants, in terms of the quality of the final solutions and the convergence rate.

This paper is concerned with the sequential fusion estimation for mobile sensor node localizations with received signal strength measurements in mobile wireless sensor networks (MWSNs). The modeling errors induced by the communication uncertainties are considered and the process noise covariance is assumed to follow a uniform distribution. A sequential fusion estimation method based on a novel square root cubature Kalman filter is presented, where the process noise covariance is generated randomly. Moreover, a lower bound of the distribution is given to improve the stability and performance of the estimator. An E-puck robot-based MWSN experiment platform is designed, and both simulations and experiments show that the proposed sequential fusion estimation method help simplify the determination of the process noise covariance while maintaining a satisfactory estimation performance.

Future smart grid (SG) has been considered a complex and advanced power system, where energy consumers are connected not only to the traditional energy retailers (e.g., the utility companies), but also to some local energy networks for bidirectional energy trading opportunities. This paper aims to investigate a hybrid energy trading market that is comprised of an external utility company and a local trading market managed by a local trading center (LTC). The existence of local energy market provides new opportunities for the energy consumers and the distributed energy sellers to perform the local energy trading in a cooperative manner such that they all can benefit. This paper first quantifies the respective benefits of the energy consumers and the sellers from the local trading and then investigates how they can optimize their benefits by controlling their energy scheduling in response to the LTC's pricing. Two different types of the LTC are considered: 1) the nonprofit-oriented LTC, which solely aims at benefiting the energy consumers and the sellers; and 2) the profit-oriented LTC, which aims at maximizing its own profit while guaranteeing the required benefit for each consumer and seller. For each type of the LTC, the optimal trading problem is formulated and the associated algorithm is further proposed to efficiently find the LTC's optimal price, as well as the optimal energy scheduling for each consumer and seller. Numerical results are provided to validate the benefits of the hybrid energy trading market and the performance of the proposed algorithms.

This study investigates the signal estimation problem in noisy sensor networks with quantised communications. The sensors are subject to random sensor failures, and synchronously take noisy measurements to produce local estimates by using a Kalman filtering scheme at each sampling instant. A quantiser is considered to be embedded in each sensor, and the probabilistic quantisation strategy is adopted to reduce the energy consumption. In between two sampling instants, each sensor collects quantised local estimates from its neighbours and runs a consensus‐based fusion algorithm to generate a fused estimate. The process noises and measurement noises are considered to be spatially uncorrelated, a recursive equation is presented to calculate the estimation error covariance matrix and an upper bound is derived for the estimation performance index. Moreover, a sufficient condition for the convergence of the upper bound of the estimation performance index is also presented. Two types of optimisation problems are constructed for cases of infinite and finite recursions, respectively, where the former one focuses on minimising the derived upper bound of the estimation performance index, and the latter one aims to minimise the energy consumption subject to a constraint on the estimation performance. Illustrative examples are provided to demonstrate the effectiveness of the proposed theoretical results.

This letter investigates the hierarchical fusion estimation for clustered sensor networks. The sensors within the same cluster are connected to a local estimator, and all the local estimators are linked with a fusion center. The fusion center and the local estimators are not required to be synchronous. During each estimation interval, the sensors are allowed to communicate with the local estimator several times. A minimum variance estimation algorithm is presented for each cluster to aperiodically generate local estimates. A covariance intersection fusion strategy is presented for the fusion center to generate fused estimates by using asynchronous local estimates and previous fused estimates, without knowing the cross-covariances among the local estimation errors.

This paper is concerned with the signal to interference and noise ratio (SINR) estimation for wireless networks with SINR constraints and packet losses. A new SINR estimation method is proposed by using moving horizon estimation (MHE), where the SINR estimation system is described as a stochastic parameter system model. By choosing a stochastic cost function, the SINR estimator is obtained by solving a regularized least-squares problem. Considering the coupling of state variable and process noise, a one-step MHE algorithm is presented to solve the constrained SINR optimization problem by using LOQO algorithm. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.

This paper presents a switched system approach to solving the distributed sampled-data$\mbi{H_\infty }$filtering problem for sensor networks with nonuniform sampling periods. The sensor network is considered to be a peer-to-peer network without an estimation center. The measurements are sampled with nonuniform sampling periods, and each sensor in the network collects the sampled measurements only from its neighbors and runs a distributed$\mbi{H_\infty }$filtering algorithm to generate estimates. A stochastic switched system model is proposed to describe the aperiodic sampled-data filtering system with random packet losses. A sufficient existence condition for the distributed$\mbi{H_\infty }$filters is derived by using the average dwell time method, and it is shown that the obtained condition critically depends on the sampling periods and the packet loss probabilities. The design of the filters is accomplished by solving a convex optimization problem, and the designed filters guarantee that the filtering system is mean-square exponentially stable and all the filtering errors satisfy an average$\mbi{H_\infty }$noise attenuation level. An illustrative example is finally given to show the effectiveness of the proposed results.

This paper is concerned with the H∞filtering for networked systems with multiple time-varying transmissions and random packet dropouts. We design a remote H∞filter for these two networked issues such that the filtering error system is exponentially stable and achieves a prescribed H∞performance level. A switched system approach is used to model the multiple time-varying transmission process, and a set of stochastic variables are employed to describe the random packet dropout phenomenon. By the switched system theory and some stochastic analysis methods, a sufficient condition for the existence of the H∞filter is derived in terms of linear matrix inequalities (LMIs). Moreover, the filter gains are determined by solving an optimization problem. Two numerical examples are given to illustrate the effectiveness of the proposed design method.

This note aims to point out one typographical error and one calculation error in the above paper (ibid., vol. 23, no. 5, pp. 827-834, May 2012).

This paper is concerned with the design of networked multi-sensor fusion estimation system (NMFES). The Kalman filtering problem is considered for the NMFES with random observation delays, packet dropouts and missing measurements caused by sensor failures. For each observation subsystem, the sensor failure phenomenon is described by a Bernoulli distributed white sequence with a known conditional probability, and the packet dropout phenomenon and randomly delayed measurements are described by multiple binary random variables. Without resorting to the augmentation technique, an optimal recursive fusion filter for NMFES is obtained in the linear minimum variance sense by using the innovation analysis method. The dimension of the designed filter is the same to the original system, which can help reduce computation costs as compared with the augmentation method. Moreover, the performance of the designed Kalman filter is dependent on the missing rates of the measurements, the upper bounds of random delays and the occurrence probabilities of delays. Finally, the effectiveness of the proposed results is demonstrated by an illustrative example.

For the networked multi-agent system with arbitrary-order time-delayed agent dynamics, the parametric H∞design method of the decentralized PID controller is proposed in this paper. The closed-loop framework representation is first given for the multi-agent system with the decentralized PID controller imposed on each agent. Based on this close-loop framework, the H∞performance criterion of the entire system is transformed into several local H∞performance constraints of the subsystem which is related to the eigenvalues of the Laplacian matrix. Thus, the design problem of the decentralized H∞PID controller is converted to the stabilization problem of the PID controller simultaneously for a family of complex quasipolynomials. Then, two parametric approaches are given to determine the region of the PID control parameters that can guarantee the stability of the complex quasipolynomial. Finally, the decentralized H∞PID controller is derived by finding the intersection of the stabilizing PID regions for all resultant quasipolynomials.

An error-driven adaptive model-based control system, for optimizing machine or assembly plant performance and operation under normal and fault conditions, is proposed. In such complex system it is imperative to differentiate between a system failure and a sensor failure or between process noise and measurement noise. In this paper, we present a comprehensive approach based on a hierarchical, multilevel control techniques. The approach is designed to provide sensor measurement validation, associates a degree of integrity with each measurement, identifies faulty sensors, and estimates the actual system states and sensor values in spite of faulty measurements. Using Virtual Machine Model concept, the method is achieved in three steps: state prediction, fault detection & sensor measurement and system online update or correction. A combination of flexible least square algorithm and adaptive Kalman filtering method are implemented to learn and predict system behavior. The experimental results show that the proposed model and algorithms can efficiently identify faulty components, reduce noise errors injected by sensors/system and thus providing self healing. The Virtual Machine Model (VMM) architecture described in this paper has proved to have several advantages over traditional models, the proposed model allows easy application provisioning, upgrades and maintenance, it provides fault tolerance, speedy disaster recovery and high availability platform.

This paper presents a distributed Demand and Response algorithm for smart grid with the objective of optimizing social-welfare. Assuming the power demand range is known or predictable ahead of time, our proposed distributed algorithm will calculate demand and response of all participating energy demanders and suppliers, as well as energy flow routes, in a fully distributed fashion, such that the social-welfare is optimized. During the computation, each node (e.g., demander or supplier) only needs to exchange limited rounds of messages with its neighboring nodes. It provides a potential scheme for energy trade among participants in the smart grids. Our theoretical analysis proves that the algorithm converges even if there is some random noise induced in the process of our distributed Lagrange-Newton based solution. The simulation also shows that the result is close to that of centralized solution.

This brief deals with the estimator design problem for discrete-time switched neural networks with time-varying delay. One main problem is the asynchronous-mode switching between the neuron state and the estimator. Our goal is to design a mode-dependent estimator for the switched neural networks under average dwell time switching such that the estimation error system is exponentially stable with a prescribed l2 gain (in the H∞ sense) from the noise signal to the estimation error. A new Lyapunov functional is constructed that may increase during the mismatched switchings. New results on the stability and l2 gain analysis are then obtained. The admissible estimator gains are computed by solving a set of linear matrix inequalities. The relations among the switching law, the maximal delay upper bound, and the optimal H∞ disturbance attenuation level are established. The effectiveness of the proposed design method is finally illustrated by a numerical example.

In a data collection sensor network, how to maximize the network lifetime through topology control remains an open research problem. Previous work has studied this problem by aiming to build a max-lifetime data collection tree, however, tree-based data collection does not necessarily yield maximum network lifetime. In this paper, we consider collaborative multipath data delivery and formulate the lifetime maximization problem as a max-fair-flow problem, then study how to collaboratively adjust the transmission power of sensor nodes to achieve the maxfair-flow, thus maximizing the network lifetime. We give both theoretical proofs and simulations to validate its correctness and performance.