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This article investigates distributed event-triggered control (ETC) of power buffers in a direct current (DC) microgrid. In order to facilitate the control design, a linear interconnected system model is derived that captures the physical coupling among power buffers. Then, a distributed ETC law regulates the stored energy and input impedance of each power buffer, and a decentralized dynamic event-triggering mechanism determines when each power buffer communicates with its neighboring buffers. This strategy eliminates the need for both continuous controller updates and continuous communication among the power buffers. The resulting closed-loop system is shown to be exponentially stable under a mild assumption on the communication network. The proposed event-triggering mechanism guarantees not only the exclusion of the Zeno behavior but also the existence of a positive minimum interevent time that can be adjusted by the control design parameters. Simulation studies validate the effectiveness of the proposed theoretical results for a multibuffer DC microgrid. [ABSTRACT FROM AUTHOR]

This article is concerned with the cluster consensus control problem for multiagent linear systems with a directed communication topology, where only relative output measurements of neighboring agents are available to each agent. Motivated by the pinning control technique, both static and dynamic intermittent output control strategies are proposed. Using Lyapunov functions, sufficient conditions are developed to ensure cluster consensus with existence-guaranteed control parameters. Both periodic and nonperiodic operations of intermittent controllers are investigated. Finally, the effectiveness of the theoretical results is demonstrated by a simulation example. [ABSTRACT FROM AUTHOR]

In this article, the set stabilization problem of Boolean control networks (BCNs) using output-feedback controllers is investigated comprehensively. First, a novel method for stabilizing BCNs, namely, the state dynamics matrix-based method, is proposed. Then, based on the above method, the time-invariant output-feedback (TIOF) and the time-variant output-feedback (TVOF) laws are derived. The necessary and sufficient conditions of both TIOF and TVOF stabilization of BCNs are also given. In addition, corresponding algorithms are provided such that all possible TIOF stabilizers and TVOF stabilizers for given BCNs can be designed. Finally, some illustrative examples are demonstrated, where the results are discussed and the effectiveness of the presented method is validated. [ABSTRACT FROM AUTHOR]

In this article, the control approach based on structured input–output finite-time stability (IO-FTS) is extended to tackle the reference tracking problem. IO-FTS was originally introduced to deal with the disturbance rejection problem. By applying the finite-time stability control to a properly augmented system, we show that it is possible to enforce a set of specific requirements on the response of the closed-loop system during the transients, taking also into account saturation constraints on the actuators. Adding IO-FTS constraints to classic-state-feedback control law leads to a feasibility problem with bilinear matrix inequality (BMI) constraints. Herein, we show how the original BMI problem can be relaxed to a linear matrix inequality (LMI) one, which comes at a price of more conservatism, but turns out to be computationally more efficient. To prove the effectiveness of the proposed approach, we consider the case of the longitudinal control of a missile. [ABSTRACT FROM AUTHOR]

Objective: This paper presents a force control scheme for brief isotonic holds in an isometrically contracted muscle tissue, with minimal overshoot and settling time to measure its shortening velocity, a key parameter of muscle function. Methods: A two-degree-of-freedom control configuration, formed by a feedback controller and a feedforward controller, is explored. The feedback controller is a proportional-integral controller and the feedforward controller is designed using the inverse of a control-oriented model of muscle tissue. A generalized linear model and a nonlinear model of muscle tissue are explored using input-output data and system identification techniques. The force control scheme is tested on equine airway smooth muscle and its robustness confirmed with murine flexor digitorum brevis muscle. Results: Performance and repeatability of the force control scheme as well as the number of inputs and level of supervision required from the user were assessed with a series of experiments. The force control scheme was able to fulfill the stated control objectives in most cases, including the requirements for settling time and overshoot. Conclusion: The proposed control scheme is shown to enable automation of force control for characterizing muscle mechanics with minimal user input required. Significance: This paper leverages an inversion-based feedforward controller based on a nonlinear physiological model in a system identification context that is superior to classic linear system identification. The control scheme can be used as a steppingstone for generalized control of nonlinear, viscoelastic materials. [ABSTRACT FROM AUTHOR]

Wide-area fault location (WAFL) refers to the estimation of fault distance on the faulted line using PMU data. A system of linear equations is formulated for WAFL, taking advantage of both voltage and current synchrophasors. This results in a generalized closed-form solution for the fault distance using the weighted least-squares method. The main contribution of the letter is the rigorous derivation of the equation weights based on the statistical distributions of the superimposed errors, i.e., the differences between the errors of the corresponding pre- and post-fault synchrophasors. The method's effectiveness, robustness against different factors, and superiority over existing methods are demonstrated by extensive simulations and comparison studies conducted on the IEEE 39-bus test system. [ABSTRACT FROM AUTHOR]

For the linear compressor system driven by a linear oscillatory machine (LOM), the piston stroke is very important to realize the high working efficiency for the whole system. In order to obtain precise and reliable piston stroke signals without using the position sensor, this article proposes a stroke estimation method for the LOM using an improved sliding mode observer (SMO). For the improved SMO, the integral sliding surface is chosen to reduce the chattering phenomenon and the steady-state error of the system, and a novel reaching law is introduced to construct the sliding control law, which can further reduce the influence of the sliding chattering. Due to the piston stroke being obtained by integrating the back electromotive force (EMF) as estimated by the improved SMO, the pure integration saturation problem will occur due to that the measured current and voltage signals always contain some dc components. In order to cope with this problem, a self-adaptive bandpass filter is designed, which can obtain a filtered signal with almost no amplitude and phase deviation by adaptively adjusting its center frequency. Simulation and experimental results confirm that the proposed improved SMO has higher stroke estimation accuracy than the back EMF integration and the conventional SMO. [ABSTRACT FROM AUTHOR]

This article proposes a likelihood-ratio-based method for recovering sparse reflectivity series from noisy seismic signals. Unlike the existing methods, the proposed method is specifically developed to be adaptive to the variance of the observational noise. This enhances the robustness of the reflectivity series recovering, as shown experimentally. The proposed method is briefly summarized as follows: First, the convolutional model of seismic signals is reformulated as a discrete-time linear system. In this linear system, the nonzero reflectivity impulses are treated as abrupt state changes with unknown magnitudes occurring at unknown times. Second, a Kalman filter is applied to the linear system without considering the changes. Third, the likelihood ratio as a function of both the magnitudes and occurring times is evaluated by using the Kalman filtering innovations. Fourth, the magnitudes and occurring times of the changes are estimated by alternately fixing one parameter and maximizing the ratio with the other parameter. Finally, the reflectivity series are reconstructed from the estimated magnitudes and occurring times of the changes. Experimental results verified the reliability of the reflectivity series recovery from noisy seismic traces. [ABSTRACT FROM AUTHOR]

Gossip protocols are popular methods for average consensus problems in distributed computing. We prove new convergence guarantees for a variety of such protocols, including path, clique, and synchronous pairwise gossip. These arise by exploiting the connection between these protocols and the block randomized Kaczmarz method for solving linear systems. Moreover, we extend existing convergence results for block randomized Kaczmarz to allow for a more general choice of blocks, rank-deficient systems, and provide a tighter convergence rate guarantee. Previous results for block randomized Kaczmarz assumed blocks formed a paving of the systems; our analysis, contrary to the title of our paper, generalizes to a broader class of blocks that we call coverings. We furthermore apply this analysis to inconsistent consensus models and obtain similar guarantees. An extensive empirical analysis of these methods is provided for a variety of synthetic and real networks. [ABSTRACT FROM AUTHOR]

Method of moments (MoM) is a classical numerical method for analyzing electromagnetic (EM) scattering from various objects. Generalized minimal residual (GMRES) is a kind of widely used solver for linear systems of equations generated by MoM, whose calculation complexity mainly includes two parts, matrix-vector multiplications and the orthogonalization procedure. In order to accelerate the solution of matrix equations arising in MoM, an efficient strategy for constructing nonorthonormal Krylov bases is proposed in this article, which can be introduced into standard GMRES and its variants (e.g., GMRES($m$) and quasi-GMRES) and can be easily combined with the fast approaches for MOM (e.g., FMM and ACA). The principle of the method is described in detail, and the effectiveness is verified by numerical results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new linear-equation ordered-statistics decoding (LE-OSD). Unlike the OSD, LE-OSD uses high reliable parity bits rather than information bits to recover codeword estimates, which is equivalent to solving a system of linear equations (SLE). Only test error patterns (TEPs) that create feasible SLEs, referred to as the valid TEPs, are used to obtain codeword estimates. We introduce several constraints on the Hamming weight of TEPs to limit the overall decoding complexity. Furthermore, we analyze the block error rate (BLER) and the computational complexity of the proposed approach. It is shown that LE-OSD has a similar performance to OSD in terms of BLER, which can asymptotically approach Maximum-likelihood (ML) performance with proper parameter selections. Simulation results demonstrate that the LE-OSD has a significantly reduced complexity compared to OSD, especially for low-rate codes, that usually require high decoding order in OSD. Nevertheless, the complexity reduction can also be observed for high-rate codes. In addition, we further improve LE-OSD by applying the decoding stopping condition and the TEP discarding condition. As shown by simulations, the improved LE-OSD has a considerably reduced complexity while maintaining the BLER performance, compared to the latest OSD approaches from literature. [ABSTRACT FROM AUTHOR]

In traditional power systems, the load consumption inside the distribution networks has a relatively passive behavior. Consequently, transmission system operators have commonly aggregated the load centers using static loads such as exponential load models. This is a practical approach for bulk power system stability studies. In modern systems, the impact of distributed generation units on the system dynamics can no longer be neglected. Static load models fall short to represent the highly dynamic behavior of active distribution networks. This paper proposes a methodology for equivalencing such networks while conserving the dynamics of interest for long-term stability studies. The methodology is based on Hammerstein-Wiener models that use only boundary variables measured at the distribution network point of common coupling. Therefore, unlike gray-box models, no previous knowledge about the system is required. Furthermore, the proposed methodology has a transparent relationship to linear systems and a convenient block representation that can be implemented in commercial power system analysis software using only standard elements. This allows for easy implementation, overcoming a barrier for a rather conservative sector such as power systems planning and operation. The conclusions are derived from time-domain simulations on the IEEE test system for voltage stability assessment. [ABSTRACT FROM AUTHOR]

Synchrophasor data provide unprecedented opportunities for inferring power system dynamics, such as estimating voltage angles, frequencies, and accelerations along with power injection at all buses. Aligned to this goal, this work puts forth a novel framework for learning dynamics after small-signal disturbances by leveraging Gaussian processes (GPs). We extend results on learning of a linear time-invariant system using GPs to the multi-input multi-output setup. This is accomplished by decomposing power system dynamics into a set of single-input single-output linear systems with narrow frequency pass bands. The proposed learning technique captures time derivatives in continuous time, accommodates data streams sampled at different rates, and can cope with missing data and heterogeneous levels of accuracy. While Kalman filter-based approaches require knowing all system inputs, the proposed framework handles readings of system inputs, outputs, their derivatives, and combinations thereof collected from an arbitrary subset of buses. Relying on minimal system information, it further provides uncertainty quantification in addition to point estimates of system dynamics. Numerical tests verify that this technique can infer dynamics at non-metered buses, impute and predict synchrophasors, and locate faults under linear and non-linear system models under ambient and fault disturbances. [ABSTRACT FROM AUTHOR]

Contribution: Two general rules for calculating, in the time domain, step discontinuities of voltages and currents in electric circuits, combining physical principles and basic mathematical treatment. The two general rules, resulting from a formal method of analysis, provide a straightforward way to update the states of a circuit, immediately after the occurrence of switching or impulsive excitation. Among its advantages, they can be used to simplify the numerical simulation of elaborate circuit topologies by avoiding approximate models of impulsive sources or switches. Background: In most engineering undergraduate textbooks, the effects of switching and impulsive sources on higher order electric circuits are usually studied for the particular case of linear time-invariant (LTI) circuits in the Laplace transform domain. This approach, however, can be time consuming and demands little reasoning on the physical principles of circuit operation. Moreover, it is not adequate for linear time-variant or nonlinear circuits. Intended Outcomes: Step discontinuities in electric circuits, caused by switching or impulsive sources, are identified and calculated through the method, allowing complete responses to be obtained afterward. Not only can the method be extended to linear time-variant and nonlinear circuits but it also has interesting consequences in numerical simulation of circuits. Application Design: An application exercise and a set of conceptual questions on the subject were proposed to electrical engineering students. Findings: The analysis of the educational experiment indicates that applying the method presented herein improves the students’ ability to obtain initial conditions of circuits, being a more straightforward and effective approach than using Laplace transform techniques. [ABSTRACT FROM AUTHOR]

Backward reachability analysis is essential to synthesizing controllers that ensure the correctness of closed-loop systems. This article is concerned with developing scalable algorithms that underapproximate the backward reachable sets, for discrete-time uncertain linear and nonlinear systems. Our algorithm sequentially linearizes the dynamics and uses constrained zonotopes for set representation and computation. The main technical ingredient of our algorithm is an efficient way to underapproximate the Minkowski difference between a constrained zonotopic minuend and a zonotopic subtrahend, which consists of all possible values of the uncertainties and the linearization error. This Minkowski difference needs to be represented as a constrained zonotope to enable subsequent computation, but, as we show, it is impossible to find a polynomial-size representation for it in polynomial time. Our algorithm finds a polynomial-size underapproximation in polynomial time. We further analyze the conservatism of this underapproximation technique and show that it is exact under some conditions. Based on the developed Minkowski difference technique, we detail two backward reachable set computation algorithms to control the linearization error and incorporate nonconvex state constraints. Several examples illustrate the effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

This article proposes a unified framework to investigate the leader-following and leaderless consensus problem of general linear multiagent systems under the directed communication topology only containing a spanning tree. To further reduce the use of resources for the control objective, an energy-saving control algorithm is introduced composed of double dynamic event-triggered mechanisms that work independently: one intends to control the communication of agents with their neighbors and the other to decide the update of controllers. It is shown that the control algorithm performs well compared to most existing control algorithms in terms of the communication cost between agents and the update cost of controllers. In addition, a new procedure to choose parameters is obtained by the developed Lyapunov stabilty method, with the potential of achieving less conservative parameters than related results. Finally, the effectiveness of the proposed control scheme is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

The $\mathcal {L}_{2}-\mathcal {L}_{\infty }$ filter design for linear stochastic time-varying delay systems based on the filter velocity constraint is considered in this dissertation. Different from existing $\mathcal {L}_{2}\,-\,\mathcal {L}_{\infty }$ performance analysis and filter design, a new criterion is proposed to make a more accurate judgment, which can not only guarantee the stability of the filtering error system with $\mathcal {L}_{2}-\mathcal {L}_{\infty }$ performance level but also can estimate the convergence speed of the filter. The ideal filter should has the same convergence speed as the original system, so as to ensure the rapid convergence of the error system, but also not to reduce the anti-interference ability and increase the bandwidth of the filter system. By the new filter design method, an ideal filter can be designed, which can regulate the convergence speed of the filter system to the desired effect. A simulation instance is provided to exhibit the validity of the new approach. [ABSTRACT FROM AUTHOR]

This article proposes static/dynamic event-triggered and self-triggered discrete gain scheduled control with a designable parametric minimal interevent time (MIET) to achieve semiglobal stabilization of linear systems with input constraints. First, a novel static event-triggered discrete gain scheduled control, which can improve the control performance and simultaneously save the communication resources, is proposed by utilizing the properties of the parametric Lyapunov equation (PLE). Moreover, the static self-triggered mechanism, in which the next control law updates based on the previous triggered states, is also designed to avoid the monitoring of all states. In order to further increase the interevent times (IETs), the corresponding dynamic event-triggered and self-triggered discrete gain scheduled control are designed, respectively. All the proposed algorithms can not only avoid the Zeno phenomenon but also provide a designable parametric MIET. This allows to easily find a tradeoff between the IETs and the control performance by adjusting the only design parameter. In addition, by exploiting the properties of the PLE, the designed algorithms avoid the complicated relationship between the MIET and the system matrices. In some cases, the MIET can totally avoid the relationship with the system itself and be designed as an arbitrarily large bounded constant. Finally, applications to the spacecraft rendezvous system show the effectiveness of the established algorithms. [ABSTRACT FROM AUTHOR]

A data-driven strategy to estimate the optimal feedback and the value function in an infinite-horizon, continuous-time, linear-quadratic optimal control problem for an unknown system is proposed. The method permits the construction of the optimal policy without any knowledge of the model, without requiring that the time derivatives of the state are available for the design, and without even assuming that an initial stabilizing feedback policy is available. Two alternative architectures are discussed: the first scheme revolves around the periodic computation of some matrix inversions involving the Q-function, whereas the second approach relies on a purely continuous-time implementation of some dynamic systems whose trajectories are uniformly attracted by the solutions to the above algebraic equations. Interestingly, the proposed strategy essentially constitutes a (direct) data-driven implementation of the celebrated Kleinman algorithm, hence subsuming the particularly appealing features of the latter, such as quadratic monotone convergence to the optimal solution. The theory is then validated by the means of practically motivated applications. [ABSTRACT FROM AUTHOR]

In this article, an event-triggered distributed state estimation mechanism is proposed for general linear systems that comprise several subsystems. Two distributed moving horizon estimation (MHE) algorithms that can handle constraints on disturbances and noise are proposed. An event scheduler is exploited to govern the evaluation of the estimators and networked information exchange between the plant and the estimators, such that good estimates can be provided while both the usage of processors and networked communication frequency can be reduced. The estimation error provided by the event-triggered estimation mechanism is proven to be convergent and bounded. A numerical example and a chemical process example are used to verify the effectiveness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

The stabilization problem for switched linear systems with quantization and event-triggered control is addressed in this article. The state information is quantized and transmitted to the controller. An event-triggered mechanism is presented in terms of the quantizer parameter. The updating law of the dynamic quantizer is suggested for the purpose of guaranteeing the state operating within the quantization range. Meanwhile, the contour line of the Lyapunov function is developed to provide an efficient way for system performance improvement. A co-design strategy is put forward to determine the event-triggered parameter and the quantization scheme simultaneously. The asynchronization issue between the controller and the subsystem is solved by designing a suitable switching signal. The Zeno behavior is eliminated by calculating a lower bound of the event-triggered interval. In the end, a strategy for finding the initial value of the quantization parameter is discussed. Simulations are carried out to demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This article studies the zero-error feedback capacity of causal discrete channels with memory. First, by extending the classical zero-error feedback capacity concept, a new notion of uniform zero-error feedback capacity $C_{0f} $ for such channels is introduced. Using this notion a tight condition for bounded stabilization of unstable noisy linear systems via causal channels is obtained, assuming no channel state information at either end of the channel. Furthermore, the zero-error feedback capacity of a class of additive noise channels is investigated. It is known that for a discrete channel with correlated additive noise, the ordinary capacity with or without feedback is equal $\log q-\mathcal {H}_{ch} $ , where $\mathcal {H}_{ch} $ is the entropy rate of the noise process and $q $ is the input alphabet size. In this paper, for a class of finite-state additive noise channels (FSANCs), it is shown that the zero-error feedback capacity is either zero or $C_{0f} =\log q -h_{ch} $ , where $h_{ch} $ is the topological entropy of the noise process. A condition is given to determine when the zero-error capacity with or without feedback is zero. This, in conjunction with the stabilization result, leads to a “Small-Entropy Theorem”, stating that stabilization over FSANCs can be achieved if the sum of the topological entropies of the linear system and the channel is smaller than $\log q$. [ABSTRACT FROM AUTHOR]

This paper proposes a feedback linearization controller for a grid-following inverter (GFLI) that uses a conventional Phase-Locked Loop (PLL) as the synchronization unit. The proposed controller enhances the GFLI synchronization by expanding the PLL domain of attraction to the whole plane that is limited to a small region around the equilibrium point in a conventional PLL, provided that the grid impedance and voltage are known. Linearizing the overall closed-loop system, the proposed controller provides linear system attributes for the PLL, leading to an infinite domain of attraction and only one equilibrium point. Additionally, a state-feedback controller is integrated within the feedback linearization controller so that it enables the system to have an adjustable dynamic response. Finally, it is shown that the system is robust against parametric uncertainties. The performance of the proposed control design is validated in Matlab/Simulink and experiment. It is verified that the proposed controller expands the domain of attraction and enhances the system dynamic response. [ABSTRACT FROM AUTHOR]

The periodic characteristic of modular multilevel converter (MMC), which originates from the multi-harmonic couplings among internal dynamics, results in challenges to develop the linear time-invariant (LTI) model of MMC. The linearization of MMC is inherently around time-periodic equilibrium trajectory. In this paper, two different methods to assess small-signal stability of MMC are compared. Both are based on the linear time-periodic (LTP) systems theory, but one is in the time domain (TD) and the other is in the frequency domain (FD). The LTP model of MMC is derived from the linearized arm averaged model. Then, an LTI model for the LTP system is constructed in the FD through the harmonic state-space method. Small-signal stability analysis based on Floquet theory and generalized Nyquist criterion, that accompany TD and FD, are introduced and compared. It is demonstrated that models developed in both domains can effectively capture the small-signal behavior of MMC. Third and higher-order harmonic components of the states have significant effect on the impedance-based stability analysis of MMC around mid-frequency range. Analytical results are validated by electromagnetic transient simulations based on the MMC aggregated model. [ABSTRACT FROM AUTHOR]

In this study, the state formation control issue of general linear networked multi-agent systems (MASs) is considered. Combining self-triggered strategy with general event-triggered strategy, a novel fully distributed asynchronous mixed self- and event-triggered control strategy is proposed, which can make the MASs achieve the prescribed state formation structure. The control strategy proposed in this study does not depend on any global network information. Therefore, no matter how large scale of the network is, the control strategy is feasible. Meanwhile, the control strategy uses the sampled state information at triggering instants instead of the real-time state information, which efficiently eliminates continuous communication among agents. Different from the existing studies, the event-triggered detector starts to work if and only if the next self-triggering instant comes in the control strategy proposed in this article. Thus, the control strategy can save more communication resources between sensor and event-triggered detector within the same inter-event interval compared with the previous event-triggered strategies. In addition, the control strategy designs a exponential decay term in the triggering function to rule out the unexpected Zeno behavior and reduce the triggering number. Finally, the numerical simulation result of multi-robot formation is given, which demonstrates the feasibility and performance of the proposed control strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article deals with the fully distributed dual-terminal dynamic event-triggered bipartite output containment control of heterogeneous linear multiagent systems (HLMASs) subject to actuator faults. First, a class of fully distributed dynamic event-triggered observers is proposed over the directed signed communication network for each follower to estimate the leaders’ system matrices and (symmetric) combination states, which are merely available to a small fraction of followers. Then, based on the estimations of leaders’ system matrices, adaptive algorithms are employed to solve the regulator equations. Furthermore, the fully distributed observer-based dynamic event-triggered controllers are proposed by integrating the adaptive gains to compensate for actuator faults. Dual-terminal dynamic event-triggered mechanisms (ETMs) are addressed to exclude not only continuous control updates but also continuous communication among agents. Correspondingly, some mild criteria and the rigorous proofs are established to ensure the implementation of bipartite output containment through the Lyapunov stability theory. Moreover, the strictly positive lower bounds of intervals between two adjacent event-triggered time instants are derived, which indicates that Zeno behavior is ruled out in the dual-terminal dynamic ETMs. Finally, numerical simulations are performed to demonstrate the theoretical analysis. [ABSTRACT FROM AUTHOR]

This article investigates the distributed optimal cooperation problems for multiple high-order systems, in which the dynamics of each agent is allowed to be subject to unknown nonlinearities. To eliminate the effect caused by unknown nonlinearities, a nonlinearity estimator is developed based on agents’ states, which successfully reconstructs the nonlinear dynamics if the unknown nonlinearities are bounded. And to minimize the sum of multiple local nonlinear cost functions with Lipschitz-type gradients, a couple of static and adaptive state-dependent algorithms are designed, respectively, where each agent may only have access to its own local cost function. It is challenging to solve such an optimal cooperation problem as the performance of the whole multiagent network is evaluated by the sum of all local performance functions. In order to fulfill the goal of cooperative optimization, a state-dependent distributed optimal cooperation algorithm is proposed first. By utilizing tools from the Lyapunov stability theory and convex optimization analysis, it is proven that the considered distributed optimal cooperation problem for high-order nonlinear systems can be solved by the proposed optimal cooperation algorithm if the state-dependent parameters are suitably selected. It is noted that the selections of the state-dependent parameters depend on some global information of the multiagent systems. Furthermore, by incorporating the proposed optimal cooperation algorithm with adaptive parameters strategy, the optimal cooperation problem is solved in a fully distributed manner. Finally, a numerical simulation is shown to verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

A model predictive controller based on optimized pulse patterns is proposed that is suitable for higher-order linear systems, such as converters with $LC$ filters. The controller manipulates the switching times of an optimized pulse pattern. The switching time modifications are approximated by the strengths of impulses, which are based on a small-signal linearization around the nominal switching instants. With these, the evolution of the state variables over the prediction horizon can be described by a set of linear differential equations. An objective function penalizes the predicted tracking error of the controlled variables, such as the converter currents, filter capacitor voltages, and grid currents, over a prediction horizon. Thanks to the use of impulse strengths, the underlying optimization problem is a convex quadratic program, which can be solved in real time to determine the switching time modifications of the pulse pattern to be applied by the controller. [ABSTRACT FROM AUTHOR]

Binary quasi-reversible BCH codes whose defining set contains consecutive elements from negative to positive integers have received considerable attention in recent years due to their efficient decoding suitable for a wide range of applications. This paper combines the concept of the weight and quasi-reversible structures to introduce two subclasses of BCH codes: odd-like/even-like quasi-reversible BCH codes. The step-by-step decoding of these codes is developed as follows: First, the weight evaluation of a received polynomial is able to judge whether the number of errors is odd or even, which helps to simplify the decoding processes. Second, based on Chiò’s pivotal condensation process which can be easily implemented in a parallel computing architecture, the determinant calculation of the band matrix instead of Peterson’s matrix in column-echelon form is faster. Third, a newly proposed non-monic error-locator polynomial is sparser than the conventional ones. As a consequence, the theoretical analysis and experimental results validate potential benefits in requiring fewer finite field additions and multiplications used in the decoding of binary odd-like/even-like quasi-reversible BCH codes up to half the minimum distance when compared with the narrow-sense BCH codes with small error-correcting capability. [ABSTRACT FROM AUTHOR]

On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and the variety of demands make this task more challenging than ever. First, the rapid increase in unlabeled data means supervised learning is becoming less suitable in many cases. Second, a large portion of time series data have complex seasonality features. Third, on-line anomaly detection needs to be fast and reliable. In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data. Further, we propose a novel metric, Local Trend Inconsistency (LTI), and an efficient detection algorithm that computes LTI in a real-time manner and scores each data point robustly in terms of its probability of being anomalous. We have conducted extensive experimentation to evaluate our algorithm with several datasets from both public repositories and production environments. The experimental results show that our scheme outperforms existing representative anomaly detection algorithms in terms of the commonly used metric, Area Under Curve (AUC), while achieving the desired efficiency. [ABSTRACT FROM AUTHOR]

This work addresses the topic of magnetic linear position detection, a common method used in modern industries to determine linear displacement by magnetic means. One major shortcoming of this method is the inherent airgap instability, which puts strong constraints on mechanical construction tolerances and limits resolution and sensitivity. We propose a method to improve the airgap stability by adding a second magnet, which makes the field and by extension the system output (locally) independent of the distance from the source. It is shown that the measurement error for 1-D systems can be reduced by a factor of ~14 and for 2-D systems by a factor of ~5 by application of this method for a realistic example. [ABSTRACT FROM AUTHOR]

The striking growth of powerful computing resources allows time-efficient solution of computationally demanding problems. In particular, advances in high-performance computing have made stochastic approaches to real-world applications more practical. The belief propagation (BP) algorithm is a probabilistic method typically used in information theory and artificial intelligence. This article exploits the probabilistic message passing attribute of BP for solving stochastic linear systems that naturally arise from finite element formulation of stochastic partial differential equations (PDEs), establishing an explicit connection between the two fields for the first time. The accuracy of the algorithm is validated by comparison to the well-known Monte Carlo method. [ABSTRACT FROM AUTHOR]

In order to achieve the purpose of extending the speed range and reducing power electronics devices for the permanent-magnet linear motor drive system, a half-centralized open-end winding (OEW) topology is studied for rail transit applications in this paper. In this topology, compared with the traditional OEW topology, the number of voltage-source-inverters (VSIs) can be reduced from four to three to drive two OEW movers and all these VSIs share a common dc bus voltage. In order to improve the steady-state performances, a four-vector phase model predictive voltage control (FV-PMPVC) is proposed in this paper. Due to the spatial distribution of phase voltage vector (PVV), an orthometric synthesis principle (OSP) is proposed to calculated the duration time for the vector synthesis, which reduces the computation burden and enhances the robustness. Comparing the existing control method, FV-PMPVC can get similar dynamic performances but better steady-state performances. Additionally, the half-centralized topology using FV-PMPVC can have same speed range while less VSIs are required. The effectiveness of FV-PMPVC is verified by experimental results. [ABSTRACT FROM AUTHOR]

In this article, an event-triggered control (ETC) law is proposed for auto-scaling of Web servers hosted on a private cloud. The Web server systems are modeled as a discrete-time linear time-invariant (LTI) system where the incoming Web requests act as disturbance inputs. Dealing with such disturbance inputs adds an additional challenge in the design and real-time implementation of the ETC. To address this challenge, we first design an ETC law to ensure ${\mathscr {D}}_{R}$ stability of the nominal system. Second, we derive the conditions which ensure input to state stability of the closed-loop system, under the event-triggered implementation, with respect to disturbance inputs. Then, sufficient conditions are derived, which ensure that the interevent times are nontrivial. Finally, to show the efficacy of the proposed approach, we validate our results on an experimental testbed of a Web server system hosted on a private cloud and provide comparative results. [ABSTRACT FROM AUTHOR]

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 of uniform detectability are 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. [ABSTRACT FROM AUTHOR]

This article is concerned with the development of a new event-triggered parity space fault detection (FD) scheme. A linear discrete-time system model with varying sampling periods is presented for handling the problem of event-triggered FD and a new parity relation is established. Based on this, an event-triggered residual generator is constructed and the generated residual is completely decoupled from event-triggered transmission error. The design of the parity matrix is formulated into an optimization problem and an optimal solution of the parity matrix is obtained by using singular value decomposition. The issue of residual evaluation is also considered in the event-triggering implementation. The novelties of this article are twofold. First, a new event-triggered parity relation is obtained and the parity space-based residual signal achieves complete decoupling with the event-triggered transmission error. Second, the calculation of the parity matrix is independent of event parameters. So the design of the parity space-based residual generator and event generator can be carried out independently. Finally, a simulation example is considered to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A dynamic wireless power transfer (DWPT) system using the conventional magnetoinductive (MI) waveguide suffers from periodic power nulls resulting from standing waves. Although numerous methods have been developed to mitigate the nulls, additional active circuitry was required, increasing the complexity. In this article, the MI waveguide with alternate magnetic coupling polarities between successive resonator cells is proposed for a true nulls-free DWPT system with no use of active components. Two configurations of the alternately coupled MI (ACMI) waveguide are developed—one with the incorporation of a quadrature-phase termination for a linear waveguide, and the other with the adoption of a circular waveguide. Extensive analysis of the generalized ACMI waveguide is provided, and the developed configurations are theoretically verified to maintain power transfer efficiency, independent of the free-positioned load. Practical implementation of DWPT systems using the linear and circular ACMI configurations with seven resonator cells at 13.56-MHz operation are experimentally demonstrated to exhibit no power transfer nulls and achieve up to fivefold reduction in efficiency variation, as compared to their MI counterparts. Comparisons with theoretical calculation are given for verification of the analysis integrity. An experimental model of a batteryless DWPT system is also demonstrated using the circular waveguide as accompanied by a video. [ABSTRACT FROM AUTHOR]

A conversion matrix approach to solving network problems involving time-varying circuit components is applied to the method of moments for electromagnetic scattering analysis. Detailed formulations of this technique’s application to the scattering analysis of structures loaded with time-varying circuit networks or constructed from general time-varying media are presented. The computational cost of the method is discussed, along with an analysis of compression techniques capable of significantly reducing the computational cost for partially loaded systems. Several numerical examples demonstrate the capabilities of the technique and validate it against conventional methods of modeling time-varying electromagnetic systems, such as finite difference time domain and transient circuit co-simulation. [ABSTRACT FROM AUTHOR]

We present a new numerical scheme to solve efficiently scattering problems involving an elongated and flat heterogeneous dielectric material assumed to be invariant along a direction of space. The technique consists of compressing the integral operators of an integro-differential formulation with a so-called quantized tensor train (QTT) algorithm whose use is rather original in this context. We show that it allows to compute and store operators with a notably small memory footprint while having at the same time a fast matrix-vector product (MVP) leading to a competitive method compared with the more classical $\mathcal {H}$ -matrix approach. [ABSTRACT FROM AUTHOR]

Virtual Metrology (VM) is a method for monitoring semiconductor fabrication processes that estimates key device properties using sensor information recorded during processing. While useful, two common problems hinder virtual metrology performance. First, changes in the modeled system over time, i.e., concept drifts, decay model performance such that a static model no longer matches the underlying system once it has drifted. Second, limited sensor information, i.e., observability errors, further limit model performance, as an inability to infer the full state of the tool prevents optimal modeling. With these two sources of error in mind, we propose a system framework to better understand these systems. This framework relates recipe, chamber, sensor and wafer values, and incorporates both concept drift and observability errors. This framework synthesizes example system data that can then be used to develop more robust modeling techniques. Additionally, we use this synthetic system to propose a linear modeling technique using Bayesian methods that adapts to concept drift, and is less prone to overfitting. We test this proposed model on synthesized as well as manufacturing virtual metrology data, and demonstrate its superiority over traditional linear methods. [ABSTRACT FROM AUTHOR]

Sliding innovation filter (SIF) has recently been introduced as a robust strategy for estimation of linear systems. The SIF has been extended to nonlinear systems via analytical linearization. However, as the performance of the extended SIF (ESIF) degrades in the presence of severe nonlinearities, this article has initially developed a derivative-free cubature SIF (CSIF) that uses statistical linearization for the error propagation. In addition, the SIF gain has been reformed to incorporate the innovation covariance matrix, thus reducing the estimation error. Furthermore, the adaptive fading factor has been employed to strengthen the robustness and convergence properties of the CSIF against abrupt changes of state variables. Simulation results of the proposed estimation algorithm named the strong tracking innovation filter (STIF) have been compared to those of the ESIF and the CSIF in different conditions of the modeling error in the dynamic system and statistical characteristics of the system inputs. This comparison has demonstrated the superior performance of the STIF in terms of the convergence rate, estimation accuracy, and the chattering elimination. Integration of the STIF into a sliding mode controller for the concurrent estimation and control of a Mars lander has reconfirmed the robustness and accuracy of the proposed STIF. [ABSTRACT FROM AUTHOR]

For the problem of intercepting a maneuvering target with a desired terminal impact angle, we propose a guidance law in this article based on a nonlinear relative virtual guidance model in which the origin is attached to the target. In this way, the original maneuvering target is transformed to a stationary one. We introduce the idea of polynomial guidance in the range domain into the relative virtual guidance model, then the acceleration command is obtained considering the terminal line-of-sight angle constraint and the terminal virtual look angle constraint. After linearization, the proposed law turns to a similar structure with the trajectory shaping guidance law, which is the optimal guidance law in the linear system. Finally, the performance of the proposed guidance law is demonstrated through numerous nonlinear simulations. [ABSTRACT FROM AUTHOR]

This article is concerned with the distributed confidentiality fusion estimation problem for cyber-physical systems in the presence of eavesdroppers. A novel active contamination strategy is proposed to guarantee the confidentiality of local state estimates (LSEs) that are transmitted to the fusion center (FC) over communication channels. Here, the LSEs are actively contaminated by the contaminating vectors, which are related to the weighting fusion process. Meanwhile, the selecting matrices that denote whether the components are contaminated are, respectively, designed for linear and nonlinear systems by maximizing the mean square errors of eavesdropper’s estimator. Under this contamination strategy, the confidentiality of systems can be effectively guaranteed when the eavesdropper tries to obtain the real state by fusing the contaminated estimates, because the estimation error covariance of the eavesdropper is large. At the same time, the corresponding compensation strategy is employed in the FC to compensate the performance loss caused by the proposed contamination method. Finally, two illustrative examples are exploited to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This article investigates stabilization problems of networked event-triggered switched systems under multiasynchronous switching. Different from the existing asynchronous literature, a novel problem is considered in this article, i.e., the event-triggering scheme and controller both have independent switching delays relative to the system, which is called multiasynchronous switching. An adaptive event-triggering scheme with switching structure is utilized to achieve better adjustment in triggering frequency. A systematic framework for analyzing multiasynchronous switching stability and solving controller gains is established. Different Lyapunov functionals are employed, and new tight bound conditions on average dwell time are constructed. Besides, an active packet loss approach for handling packets disorder issues caused by large transmission delays is presented. Since the activation instants of the system, triggering scheme, and controller are mutually staggered with the data updating instants of the actuator, a novel analytical method aiming at the coupling effect is proposed. Finally, the validity of the adopted method in this article is demonstrated. [ABSTRACT FROM AUTHOR]

Disturbance rejection and time-delay compensation are important for control systems in practice. This article presents an observer-and-predictor-based method to reject an unknown exogenous disturbance for an input-delay system in which only delayed output is available. First, the equivalent-input-disturbance method is employed to reconstruct the system state and to estimate the disturbance. A prediction of the disturbance is calculated to reduce the effect of a phase lag caused by the delays. A new predictive scheme is designed for feedback control. Next, the system is divided into two subsystems for analysis and design: one for reference tracking and the other for disturbance rejection. Sufficient conditions are derived to guarantee the system’s stability. An analysis of the method reveals that the disturbance rejection can be viewed as a patch to the predictor-based feedback control. Finally, two case studies are used to show the validity and superiority of the method. [ABSTRACT FROM AUTHOR]

The constraints of communication channel resources and control energies always lie in real networks, which bring large error and cause adverse affects on system performance, ultimately. To address these limitations, in this article, a distributed event-triggered impulsive control strategy for the consensus problem of fractional-order multiagent networks (FOMANs) is investigated. By collecting the local instantaneous access information, the distributed tracking algorithm with a novel triggering condition is proposed, which is available for both linear and nonlinear systems. Combining with general quadratic Lyapunov stability method and discontinuous control theory, the sufficient conditions of consensus in terms of gain parameter and impulsive interval are derived. Furthermore, the minimal length of interevent is proven to be strictly greater than zero, which guarantees the nonexistence of Zeno-behavior in the closed-loop system. In addition, the implementation of the fractional order algorithm, the quantitative relationship between fractional order $\alpha $ or parameter $a$ in the triggering function, and the number of event-triggered impulsive instants are discussed. At the end of this article, two illustrative examples with numerical and circuit simulations are provided to validate the effectiveness of above results. [ABSTRACT FROM AUTHOR]

This article considers the distributed adaptive fault-tolerant control problem for heterogeneous linear multiagent systems with actuator faults and nonuniform time-varying communication delays. First, novel distributed switching observers are proposed to estimate the system matrix and the state of the exosystem. The observers allow each agent to share its information with its underlying neighbors only at sampled instants of time, thus making a distinct difference from the existing results that require the exosystem matrix to be accessible to all agents at every continuous instant of time. Second, a sufficient condition is derived such that the observer error systems are exponentially stable under the influence of communication delays among interacting agents. Third, new distributed adaptive fault-tolerant controllers, which promise fewer adaptive parameters, are presented to compensate for the actuator faults. It is shown that the developed controllers are capable to effectively and efficiently solve the cooperative fault-tolerant output regulation problem with reduced computational complexity. Finally, three illustrative examples, including an inverted pendulum system, an electronic double-integrator circuit system, and an AC microgrid system, are presented to show the validity and efficiency of the developed method. [ABSTRACT FROM AUTHOR]

This article proposes a novel attack detection approach based on zonotopes for linear parameter-varying (LPV) systems with unknown-but-bounded (UBB) noises. The following three types of attacks are considered: 1) denial-of-service (DoS) attacks; 2) replay attacks (RAs); and 3) false-data-injection (FDI) attacks. In order to reduce the conservativeness, a free-weighting matrix is introduced, which can be computed by solving an optimization problem. Moreover, the radius of the intersection zonotope can be guaranteed to be limited as well. Furthermore, it is not necessary to acquire the knowledge about the specific type of attack in advance. Finally, a numerical example is given to illustrate the validity of the given method. [ABSTRACT FROM AUTHOR]

There has been tremendous research on the design of image regularizers over the years, from simple Tikhonov and Laplacian to sophisticated sparsity and CNN-based regularizers. Coupled with a model-based loss function, these are typically used for image reconstruction within an optimization framework. The technical challenge is to develop a regularizer that can accurately model realistic images and be optimized efficiently along with the loss function. Motivated by the recent plug-and-play paradigm for image regularization, we construct a quadratic regularizer whose reconstruction capability is competitive with state-of-the-art regularizers. The novelty of the regularizer is that, unlike classical regularizers, the quadratic objective function is derived from the observed data. Since the regularizer is quadratic, we can reduce the optimization to solving a linear system for applications such as superresolution, deblurring, inpainting, etc. In particular, we show that using iterative Krylov solvers, we can converge to the solution in few iterations, where each iteration requires an application of the forward operator and a linear denoiser. The surprising finding is that we can get close to deep learning methods in terms of reconstruction quality. To the best of our knowledge, the possibility of achieving near state-of-the-art performance using a linear solver is novel. [ABSTRACT FROM AUTHOR]