This study proposes an accelerated iterative learning control scheme using a fractional high-order update rule (FHUR) to improve the convergence rate for linear time-invariant systems. High- and low-order power update terms are used to handle large- and small-tracking errors, respectively, thereby accelerating convergence. Two learning mechanisms are proposed and shown to be optimal among various learning gain selections. The inherent nonlinearity in the FHUR poses significant challenges for the convergence analysis. To address this, a disturbed composite nonlinear mapping method is introduced. Using this method, the tracking errors are proven to converge either to an invariant set or to a set of limit cycles, depending on the underlying learning mechanism. Any desired tracking precision can be achieved by adjusting the parameters in the FHUR. Numerical simulations confirm that the FHUR presents a promising alternative to the commonly used proportional-type update rule for achieving accelerated convergence.

Power distribution networks, incorporating electric vehicles (EVs) and battery energy storage systems (BESSs), can provide valuable flexibility to the upstream grid. This article proposes a new mechanism for modeling and optimizing two-way flexibility exchange between the distribution system operator (DSO) and flexible loads, aiming to minimize the DSO’s total cost while satisfying the flexibility requests of the upstream market operator. The DSO first solicits the participation of flexible loads, including EVs and BESSs, which can either accept or reject the request. Considering the agreed state of charge of participating resources, a flexibility market is then formulated, incorporating the user contribution index and Karush–Kuhn–Tucker conditions. The proposed mechanism is tested under various load conditions, price tariffs, and EV penetration levels. The results demonstrate significant cost reductions for DSOs, as they purchase less energy from the upstream market operator compared to scenarios without flexibility management.

Coordination between electric vehicles (EVs) and battery systems (BSs) plays a pivotal role in enhancing the flexibility of the electricity grid by offering demand response and energy storage capabilities. This paper proposes a new method for EV-BS coordination to meet the expected flexible load (FL) in each hour of the day. The profit of the Distribution System Operator (DSO) is formulated as a mixed-integer linear programming optimization problem. Additionally, different tariff prices are used to account for the uncertainty of the flexibility price in the electricity market. According to the proposed approach, the flexibility direction is first determined by the DSO, which can be upward flexibility, downward flexibility, or no flexibility, depending on different load conditions. The electricity market is then utilized to provide the expected FLs based on the flexibility direction and the behavior of EVs and BSs, with the participation fee in the proposed program. Simulation results show that the proposed approach increases the DSOs’ profit by considering the coordination between EVs and BSs during non-flexibility hours.

Exploring multi-step forecasting in dynamic spectrum access (DSA) is vital for increasing spectrum efficiency and reliability. Accurately predicting future occupancy states can significantly advance our efforts to optimize spectrum utilization, enabling proactive management that reduces interference and enhances the performance of cognitive radio (CR) networks. For this purpose, the application of deep learning (DL) techniques for spectrum prediction has become increasingly important, as they excel at modeling complex temporal dependencies and learning from historical datasets. This paper deals with multi-step prediction in spectrum occupancy, utilizing second-order Markov models and Long-Short-Term Memory (LSTM) networks. The study systematically categorizes spectrum occupancy into six distinct categories to understand biased, random, and unbiased scenarios. Each one exhibits distinct prediction error behaviors. The findings of this study reveal the critical importance of knowledge of occupancy categories in spectrum prediction, and also their impact on LSTM networks when capturing and predicting temporal dependencies inherent in spectrum occupancy data.

Power delivery issues are increasingly evident in cyber-physical smart grid systems as energy transactions frequently overlook the physical constraints of distribution, leading to transmission congestion and compromising network security and reliability. This article presents a novel and cost-effective solution to power delivery challenges by utilizing electric vehicles (EVs) with dynamic transportation capabilities as free carriers. Unlike traditional approaches, a deep reinforcement learning (DRL)-based optimization framework is designed to effectively manage incomplete information in real-time. Our method first introduces an investment-free model that leverages existing EV routes to transport energy during congestion, operating in a “free-riding” transmission mode. This not only enhances network reliability but also curtails costs. Then, we develop a Markov decision process (MDP) for sequential decision-making of 24-h optimal control, aimed at minimizing operational losses including load shedding and battery degradation. To deal with the stochastic nature of energy requests and EV routes in the control problem, we employ a model-free DRL algorithm to tackle the challenge of incomplete information. An Actor-Critic network, combining value-based and policy-based approaches, helps discover approximately optimal strategies in a continuous action space. Finally, the simulation results numerically demonstrate the performance of the proposed method.

Abstract This paper presents a robust framework for handling a conic multiobjective linear optimization problem, where the objective and constraint functions are involving affinely parameterized data uncertainties. More precisely, we examine optimality conditions and calculate efficient solutions of the conic robust multiobjective linear problem. We provide necessary and sufficient linear conic criteria for efficiency of the underlying conic robust multiobjective linear program. It is shown that such optimality conditions can be expressed in terms of linear matrix inequalities and second-order conic conditions for a multiobjective semidefinite program and a multiobjective second order conic program, respectively. We show how efficient solutions of the conic robust multiobjective linear problem can be found via its conic programming reformulation problems including semidefinite programming and second-order cone programming problems. Numerical examples are also provided to illustrate that the proposed conic programming reformulation schemes can be employed to find efficient solutions for concrete problems including those arisen from practical applications.

The proportional type update rule (PTUR) is the most widely used iterative learning control (ILC) scheme. Recently, a fractional-power type update rule (FTUR) was proposed to accelerate PTUR. However, PTUR and FTUR converge slowly for small and large tracking errors, respectively. In this study, a multistage update rule (MSUR) is designed to accelerate PTUR and FTUR along the whole iteration axis. Under the proposed switching mechanism, PTUR and FTUR are adopted for large and small errors for fast convergence, and then PTUR is applied for zero-error tracking. The convergence of MSUR is proved by the analysis of a nonlinear recursion with perturbation. Moreover, for system information that is unknown, an extended MSUR is presented, and its zero-error convergence is proved. In addition, we discuss the influence of the parameters in MSUR on the convergence rate and propose a set of parameter selection rules to maximize the convergence rate of MSUR. Meanwhile, variable-gain and variable-power MSURs are designed to further accelerate the MSUR that only has a single gain and fractional power. Numerical simulations and experimental test verify the theoretical results.Note to Practitioners—Many engineering systems, including high-speed trains, earth-orbiting satellites, and robotic arms, complete a given tracking task over a finite time interval repeatedly. Fast convergence rate and high tracking precision are key technical requirements in these applications. Iterative learning control (ILC) has been shown as an effective control method for these tasks. However, the widely-used proportional-type update rule (PTUR) and fractional-power type update rule (FTUR) in ILC cannot meet the abovementioned requirements well. PTUR converges fast for large tracking errors but slow for small tracking errors; in contrast, FTUR delivers fast convergence rate for small tracking errors but converges slow for large errors without achieving zero-error. Combining advantages of both PTUR and FTUR rules, a novel multistage update rule (MSUR) is proposed to adaptively switch between them to deliver fast convergence and zero tracking errors. The selection of parameters and their effect on control performance are detailed for engineering applications.

This paper proposes a novel dual-mode dynamic event-triggered control framework designed specifically for nonlinear cyber-physical systems (CPS) subject to constraints and bounded disturbances. The framework addresses critical challenges in balancing system performance, computation efficiency, and communication overhead. To achieve this, two distinct control modes are developed based on the system state’s location relative to the terminal set. When the system state lies outside the terminal set, Mode 1 is activated. This mode implements an event-triggered model predictive control approach, combining a dynamic threshold with a PID-based triggering mechanism. These features notably reduce the frequency of triggering events while also lowering computation and communication costs. In contrast, Mode 2 becomes active when the system state lies within the terminal set. This mode employs an event-triggered feedback control approach aimed at further reducing communication costs while maintaining control efficiency. In addition, rigorous theoretical analysis is conducted to establish the recursive feasibility, stability, and exclusion of Zeno behavior within the proposed framework. Finally, numerical simulations are performed to validate the superiority of the proposed method.

This article proposes a novel gain selection scheme for fractional-proportional-type iterative learning control, aiming to achieve faster convergence rates while maintaining high tracking precision. The convergence of tracking errors to adjustable limit cycles is demonstrated, and a recursive computation method is provided for these limit cycles. Furthermore, the bounds of the limit cycles are estimated in detail, and both local and global convergence rates are thoroughly analyzed. A systematic performance comparison of different gain selection rules, including tracking precision and convergence rate, is conducted. Two multistage update schemes are established through combining different gain selections to accelerate convergence quantitatively, resulting in faster convergence rates compared to the common proportional-type update rule while preserving final zero-error tracking performance. Moreover, the switching iteration of the proposed multistage schemes can be independent of system matrices. Numerical simulations and experiments are presented to validate the theoretical findings.

In this article, we propose an estimator-based second-order sliding mode (SOSM) controller tailored for uncertain nonlinear systems with unknown input delay. Distinct from existing SOSM control methods, this work tackles two principal challenges: 1) the difficulty of dealing with unknown input delay, especially given the discontinuity of sliding mode controllers; and 2) the uncertainties in the nonlinear systems bounded by functions rather than widely-used constants. We begin by establishing the SOSM dynamics with input delay and uncertainties, followed by the introduction of an auxiliary compensation system. Then, we design an input delay estimator suitable for discontinuous controllers by enhancing the convex optimization method. Leveraging this, a novel estimator-based SOSM controller is constructed by adding a power integrator technique to address the input delay issue. Rigorous Lyapunov analysis is conducted to confirm the finite-time stability of the closed-loop control system. Finally, comparative simulations validate the superiority of the proposed SOSM controller.

This article studies event-triggered nonsingular terminal sliding-mode control (TSMC) for a class of nonlinear systems. First, a static event-triggering mechanism is implemented in the nonsingular TSMC design. It is shown that the sliding variable can reach the quasi-sliding-mode band and the states can converge to a neighborhood of the equilibrium dependent on the threshold of the event-triggering mechanism. Second, by taking advantage of the internal variable, a dynamic event-triggering mechanism is developed for the nonsingular TSMC design. Compared to the static event-triggered nonsingular TSMC, the designed dynamic event-triggered nonsingular TSMC strategy can reduce the number of events while maintaining the same upper bounds of quasi-sliding-mode and steady states. It is further shown that both event-triggered nonsingular TSMC systems have no Zeno behavior. Finally, simulation results are given to demonstrate the effectiveness of the theoretical results.

This article proposes a strategy to accelerate the convergence of iterative learning control (ILC) while maintaining robustness against stochastic noise. The strategy adaptively reweights the error signals used in conventional ILC schemes, casting greater influence to larger errors during input updates, thereby accelerating the correction of noisy inputs and improving overall convergence behavior. Furthermore, to mitigate the impact of noise-dominated small errors on weight computation, a saturation mechanism is introduced. A convergence theorem is established to characterize how the saturation parameters affect the asymptotic convergence of the input deviation-induced errors. Simulation and experimental results demonstrate that incorporating this strategy consistently improves convergence speed while maintaining tracking accuracy across different ILC implementations.

In a multisensor system, each sensor typically requires independent reference tracking while conflicts arise due to differing desired inputs for different sensors. This scenario presents an exemplary incompatible multiobjective tracking problem (IMOTP), which can be resolved as a multiobjective optimization problem (MOOP). We propose an iterative learning control strategy to resolve conflicts between sensors. First, we elaborate on the Pareto optimal solution (POS) set associated with the MOOP. Subsequently, we derive an update direction for Pareto improvement based on gradient-based algorithms for MOOP and establish a learning control algorithm ensuring that each update is a Pareto improvement and converges to a POS. These technical advancements effectively overcome tracking conflicts in multisensor systems. Illustrative simulations are provided to validate the theoretical results.

In this article, a novel adaptive second-order sliding mode (ASOSM) control law is constructed for a general category of sliding mode control (SMC) systems with mismatched uncertainties, including a nonvanishing external disturbance. This innovative control design proposal is accomplished through three key mechanisms. First, the new sliding mode dynamics subject to mismatched uncertainties is derived by selecting the appropriate sliding variables, which can significantly increase the uncertainties existing in the control input channel and relax the strict requirement on the relative degree assumption of the sliding variable. Second, a novel ASOSM controller, which contains some adaptive parameters generated via a three-layer nested adaptive mechanism, is constructed by utilizing the modified adding power integrator (API) approach and the adaptive control technique. Third, the practical finite-time stability of the closed-loop sliding mode system is confirmed by means of the systematic Lyapunov stability theory. The technical advancement of the developed adaptive control scheme lies in its ability to effectively deal with a more general sliding mode dynamics containing multiple uncertainties and guarantee that the practical second-order sliding mode (SOSM) is established in a finite time. Finally, simulation results, incorporating a practical application case, are provided to illustrate the effectiveness of the designed adaptive control scheme.

This article proposes an extended zero-gradient-sum (EZGS) approach for solving constrained distributed optimization with free initialization and desired convergence properties. A Newton-based continuous-time algorithm is first designed for general constrained optimization, which is adapted to handle inequality constraints by using log-barrier penalty functions. Then, a general class of EZGS dynamics is developed to address equation-constrained distributed optimization, where an auxiliary dynamics is introduced to ensure the final ZGS property from any initialization. It is demonstrated that for typical consensus protocols and auxiliary dynamics, the proposed EZGS dynamics can achieve the performance with exponential/finite/fixed/prescribed-time (PT) convergence. Particularly, the nonlinear consensus protocols for finite-time EZGS algorithms allow for heterogeneous power coefficients. Significantly, the proposed PT EZGS dynamics is continuous, uniformly bounded, and capable of reaching the optimal solution in a single stage. Furthermore, the barrier method is employed to handle the inequality constraints effectively. Finally, the efficiency and performance of the proposed algorithms are validated through numerical examples, highlighting their superiority over existing methods. In particular, by selecting appropriate protocols, the proposed EZGS dynamics can achieve desired convergence performance.

SCADA alarm management poses many challenges for power grid control room operators. Vast quantities of information must be managed and filtered by human operators to identify the specific alarm of interest so that system issues can be promptly addressed. The use of knowledge based systems and decision support systems to assist in industrial decision making is well established but comes with heavy cost burdens for development and maintenance. This work demonstrates a possible approach to addressing this cost issue by utilising large language models (LLMs) to parse SCADA alarms and extract information to construct a knowledge base in the form of a knowledge graph. This knowledge base can then be used to construct a decision support system suitable for control room operators to make plain language queries that assist in their problem solving. The decision support approach facilitates better and faster decision making assisting the effective management of electrical distribution networks by control room operators. The case study shown in this work demonstrates that LLMs show promising capacity for knowledge base constructions and have the potential to unlock easy access and insights from large volumes of SCADA alarm data.

The operation of power grids is increasingly complex and there is a growing need for data-driven fault diagnosis in smart grid dispatching. This study presents an innovative real-time machine learning framework that significantly enhances the detection and management of alarm events in power grid SCADA systems. At the heart of this framework is a novel deep neural network (DNN) architecture designed to efficiently identify alarm events. Additionally, an algorithm prioritizing the identification of historically relevant alarm events is developed, providing robust decision support. The proposed framework is constructed and underwent thorough testing with a comprehensive power grid dataset. The results demonstrate that the framework not only meets but exceeds the operational demands for real-time alarm detection, particularly during periods of alarm floodpeak. Moreover, the alarm event searching and decision support algorithm is proven to be a critical tool for power grid control room experts and operators, facilitating rapid decision-making by providing actionable insights based on historical and current data analysis. The successful integration of this machine learning framework into SCADA systems marks a significant step forward in the technological evolution of energy systems, leading to smarter, more efficient, and reliable power grid management.

This paper focuses on identifying the most influential distributed generators (DGs) to improve the control performance of islanded microgrids with switching communication networks. Based on the secondary control scheme, frequency regulation and active power sharing are achieved in microgrids by pinning only a fraction of DGs. To improve the dynamic response of microgrids under limited control resources, a method is proposed to identify the most influential DG set that guarantees the fastest synchronization speed. By analyzing the impacts of the switching frequency of communication networks, an effective threshold is calculated to ensure performance. Furthermore, the proposed results are tested on a modified IEEE 34-bus system to evaluate the performance.

The increasing installation of Photovoltaics (PV) cells leads to more generation of renewable energy sources (RES), but results in increased uncertainties of energy scheduling. Predicting PV power generation is important for energy management and dispatch optimization in smart grid. However, the PV power generation data is often collected across different types of customers (e.g., residential, agricultural, industrial, and commercial) while the customer information is always de-identified. This often results in a forecasting model trained with all PV power generation data, allowing the predictor to learn various patterns through intra-model self-learning, instead of constructing a separate predictor for each customer type. In this paper, we propose a clustering-based multitasking deep neural network (CM-DNN) framework for PV power generation prediction. K-means is applied to cluster the data into different customer types. For each type, a deep neural network (DNN) is employed and trained until the accuracy cannot be improved. Subsequently, for a specified customer type (i.e., the target task), inter-model knowledge transfer is conducted to enhance its training accuracy. During this process, source task selection is designed to choose the optimal subset of tasks (excluding the target customer), and each selected source task uses a coefficient to determine the amount of DNN model knowledge (weights and biases) transferred to the aimed prediction task. The proposed CM-DNN is tested on a real-world PV power generation dataset and its superiority is demonstrated by comparing the prediction performance on training the dataset with a single model without clustering.

Abstract In this paper, we consider a polynomial problem with equilibrium constraints in which the constraint functions and the equilibrium constraints involve data uncertainties. Employing a robust optimization approach, we examine the uncertain equilibrium constrained polynomial optimization problem by establishing lower bound approximations and asymptotic convergences of bounded degree diagonally dominant sum-of-squares (DSOS), scaled diagonally dominant sum-of-squares (SDSOS) and sum-of-squares (SOS) polynomial relaxations for the robust equilibrium constrained polynomial optimization problem. We also provide numerical examples to illustrate how the optimal value of a robust equilibrium constrained problem can be calculated by solving associated relaxation problems. Furthermore, an application to electric vehicle charging scheduling problems under uncertain discharging supplies shows that for the lower relaxation degrees, the DSOS, SDSOS and SOS relaxations obtain reasonable charging costs and for the higher relaxation degrees, the SDSOS relaxation scheme has the best performance, making it desirable for practical applications.

This study investigates the trajectory tracking problem for stochastic systems and proposes a novel adaptive gain design to enhance the transient convergence performance of the learning control scheme. Differing from the existing results that mainly focused on gain's transition from constant to decreasing ones to suppress noise influence, this study leverages the adaptive mechanisms based on noisy signals to achieve an acceleration capability by addressing diverse performance at different time instants throughout the operation interval. Specifically, an additional gain matrix is introduced into the adaptive gain design to further enhance transient convergence performance. An iterative learning control approach with such a gain design is proposed to realize high precision tracking and it is proven that the input error generated by the newly proposed learning control scheme converges almost surely to zero. The effectiveness of the proposed scheme and its improvement on the transient performance of the learning process are numerically validated.

In this work, we consider the safe deployment problem of multiple robots in an obstacle-rich complex environment. When a team of velocity and input-constrained robots is required to move from one area to another, a robust collision-avoidance formation navigation method is needed to achieve safe transferring. The constrained dynamics and the external disturbances make the safe formation navigation a challenging problem. A novel robust control barrier function-based method is proposed which enables collision avoidance under globally bounded control input. First, a nominal velocity and input-constrained formation navigation controller is designed which uses only the relative position information based on a predefined-time convergent observer. Then, new robust safety barrier conditions are derived for collision avoidance. Finally, a local quadratic optimization problem-based safe formation navigation controller is proposed for each robot. Simulation examples and comparison with existing results are provided to demonstrate the effectiveness of the proposed controller.

This article studies the second-order consensus problem in networked systems containing the so-called Byzantine misbehaving nodes when only an upper bound on either the local or the total number of misbehaving nodes is known. The existing results on this subject are limited to malicious/faulty model of misbehavior. Moreover, existing results consider consensus among normal nodes in only one of the two states, with the other state converging to either zero or a predefined value. In this article, a distributed control algorithm capable of withstanding both locally bounded and totally bounded Byzantine misbehavior is proposed. When employing the proposed algorithm, the normal nodes use a combination of the two relative state values obtained from their neighboring nodes to decide which neighbors should be ignored. By introducing an underlying virtual network, conditions on the robustness of the communication network topology for consensus on both states are established. Numerical simulation results are presented to illustrate the effectiveness of the proposed control algorithm.

The process industry is an industrial field of interdisciplinary nature involving electrical engineering, energy, petroleum, chemical, and metallurgy, which play a key role in the sustainable development. As a main source of CO2 emissions, the process industry will undertake a large part of the emission reduction task. In order to incorporate the impact of social factors, such as environment, society, and human to support the future process industry, the cyber-physical-social system (CPSS) framework should be considered as a promising way to enhance the transformation of the process industry. The development of CPSS technologies will fundamentally change the infrastructure of conventional industrial systems, offering a great opportunity for the greenization, high-value, and digitalization in the process industry. This article first presents the current status of the process industry. Through a CPSS framework, the current developments of the process industry as well as the main challenges and opportunities are discussed. A vision for the future process industry based on CPSS is described by focusing on three aspects, namely, the greenization and low carbon, high-value and high-end, digitalization, and intellectualization in process manufacturing. Finally, the advanced technologies and approaches in CPSS driven by artificial intelligence and industrial digitalization, which are important in achieving the sustainable development of the process industry, are outlined. The development of the comprehensive digital technologies, such as virtual reality, digital twin, blockchain, and big data, will stimulate the implementation of a ground-breaking concept formed in the CPSS framework called industrial metaverse.

Traffic accident prediction plays a vital role in Intelligent Transportation Systems (ITS), where a large number of traffic streaming data are generated on a daily basis for spatiotemporal big data analysis. The rarity of accidents and the absent interconnection information make it hard for spatiotemporal modeling. Moreover, the inherent characteristic of the black box predictive model makes it difficult to interpret the reliability and effectiveness of the deep learning model. To address these issues, a novel self-explanatory spatial-temporal deep learning model–Attention Spatial-Temporal Multi-Graph Convolutional Network (ASTMGCN) is proposed for traffic accident prediction. The original recorded rare accident data is formulated as a multivariate irregularly interval-aligned dataset, and the temporal discretization method is used to transfer into regularly sampled time series. Multiple graphs are defined to construct edge features and represent spatial relationships when node-related information is missing. Multi-graph convolutional operators and attention mechanisms are integrated into a Sequence-to-Sequence (Seq2Seq) framework to effectively capture dynamic spatial and temporal features and correlations in multi-step prediction. Comparative experiments and interpretability analysis are conducted on a real-world data set, and results indicate that our model can not only yield superior prediction performance but also has the advantage of interpretability.

In this study, we investigate the accelerated learning control schemes for point-to-point tracking systems (PTSs) with measurement noise. The asymptotic convergence of the generated input sequence has been a long-standing open issue for point-to-point tracking problems because there are infinite possible input candidates that can drive the system dynamics to track the desired reference at specified time instants. An accelerated gradient algorithm and its generalized version with a novel direction regulation matrix are proposed, with the learning gain is adaptively triggered by the practical tracking errors. The learning gain remains constant at the early stage and begins to decrease after a certain number of iterations. The input sequence generated by the proposed scheme converges to a specified limit for any fixed initial input, with the limit being closest to the initial input, in a certain sense. Numerical simulations are provided to verify the theoretical results.

The digitization of process manufacturing involves converting information and knowledge into a digital format through technologies, such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, and digital twins. This transformation promotes extension and optimization within the industrial, supply, and value chains, aiming to enhance decision-making efficiency, enable agile operations, and ensure information security and privacy. However, the current learning and operational approaches in the process industry remain rooted in traditional informatization, falling short of the vision for digital transformation. To address this gap, it is crucial to implement fusion analysis, deepen understanding, adopt autonomous learning, and enable intelligent optimization based on life-cycle data. Therefore, it is of fundamental importance to realize the transformation of process manufacturing toward digitalization and intelligentization, i.e., the use of artificial intelligence with decision-making capability, via new learning theories, methods, and algorithms.

Energy demand prediction can be obtained for different customer categories or geolocations, e.g., predicting the energy demand over different cities. Traditionally, these prediction tasks are solved independently without considering the common problem-solving knowledge sharing among them. However, addressing one task may help facilitate the training process or improve the prediction performance of another one via knowledge transfer. In this article, we propose a two-stage multitasking prediction (TS-MTP) framework to address the energy demand prediction problem over multiple locations, in which each task has a deep neural network (DNN) model as the predictor. TS-MTP includes single-tasking learning (STL) and multitasking learning (MTL) stages. The STL stage focuses on addressing each prediction task independently with a gradient descent-based optimization algorithm until the training accuracy cannot be improved, so that the optimal DNN structure parameters for each task can be achieved. In the MTL stage, for a specified target task, the knowledge, i.e., DNN connection weights and biases acquired in STL, is extracted and transferred from the source tasks and reused in the target task to help further improve its prediction accuracy. To decide the amount of knowledge to be reused, a coefficient is assigned to each source task, and particle swarm optimization is applied to obtain the optimal coefficients. The performance of TS-MTP is verified on several problem sets that are created from different step-ahead predictions. The superiority of TS-MTP is demonstrated in comparison to several state-of-the-art DNNs that are popular in the time-series prediction domain. The results show that TS-MTP can lead to a more than 35% accuracy improvement compared with the STL without knowledge transfer.

Knowledge of the Laplacian eigenvalues of a network provides important insights into its structural features and dynamical behaviours. Node or link removal caused by possible outage events, such as mechanical and electrical failures or malicious attacks, significantly impacts the Laplacian spectra. This can also happen due to intentional node removal against which, increasing the algebraic connectivity is desired. In this article, an analytical metric is proposed to measure the effect of node removal on the Laplacian eigenvalues of the network. The metric is formulated based on the local multiplicity of each eigenvalue at each node, so that the effect of node removal on any particular eigenvalues can be approximated using only one single eigen-decomposition of the Laplacian matrix. The metric is applicable to undirected networks as well as strongly-connected directed ones. It also provides a reliable approximation for the “Laplacian energy” of a network. The performance of the metric is evaluated for several synthetic networks and also the American Western States power grid. Results show that this metric has a nearly perfect precision in correctly predicting the most central nodes, and significantly outperforms other comparable heuristic methods.

Driven by government incentive policies and heightened environmental awareness by individuals, many regions around the world have seen a rapid rise in distributed energy resource (DER) penetration in electricity distribution networks. While high penetration of DER significantly helps facilitate the decarbonization in power and utitlies, it also brings unexpected operational challenges, among which voltage compliance has been a significant concern. To address this issue, the efficient load profile forecast, the operational framework and related strategies are critical challenges that need to be addressed urgently. Hence, this paper presents a cloud-edge computing-based framework to effectively operate the coupled medium-voltage (MV) and low-voltage (LV) distribution network. The high computational efficiency in cloud computing and low data latency in edge computing are presented and explored to coordinate the day-ahead and intraday operations ranging from different framework layers. Under the framework, a customer-level forecasting algorithm is employed to predict both day-ahead and real-time load profiles. Based on the prediction results, an optimization model based on unbalanced-three phase optimal power flow is proposed and solved by an efficient and accurate linearization-based approach that considers the controllability of on-load tap changers, distributed static var generator and the PV inverters. Simulations based on an extensive mocked MV-LV distribution network show the proposed forecasting method is adopted in real-time operations in terms of high accuracy and demonstrate the efficiency of the proposed optimization method in enhancing the voltage compliance in the network.

In this article, both the fixed-time distributed consensus tracking and the fixed-time distributed average tracking problems for double-integrator-type multiagent systems with bounded input disturbances are studied. First, a new practical robust fixed-time sliding-mode control method based on the time-based generator is proposed. Second, two fixed-time distributed consensus tracking observers for double-integrator-type multiagent systems are designed to estimate the state disagreement between the leader and the followers under undirected and directed communication, respectively. Third, a fixed-time distributed average tracking observer for double-integrator-type multiagent systems is designed to measure the average value of multiple reference signals under undirected communication. Note that all the proposed observers are constructed with time-based generators and can be trivially extended to that for high-order integrator-type multiagent systems. Furthermore, by combining the proposed fixed-time sliding-mode control method with the information provided by the fixed-time observers, the fixed-time controllers are designed to solve the fixed-time distributed consensus tracking and the distributed average tracking problems. Finally, a few numerical simulations are shown to verify the results.

This article studies the conflicting goals of high-precision tracking and quick convergence speed, which is a longstanding problem in the learning control of stochastic systems. In such systems, a decreasing gain sequence is necessary to ensure the asymptotic convergence of the generated input sequence to a fixed limit. However, the convergence speed is adversely affected by gain sequences of this nature. In this article, we propose a novel multistage learning control strategy to resolve this conflict, where each stage consists of several iterations. The learning gain remains constant in each stage but is reduced at the transition from a given stage to the subsequent stage. The switching iteration between two stages is determined by the tracking performance index of the contracted input error and the accumulated noise drift. Furthermore, an improved mechanism is proposed to optimize the lengths of the different stages. The asymptotic convergence of the input sequence generated by the newly proposed strategy is strictly established by thoroughly analyzing the properties of the proposed gain sequence. Numerical simulations are presented to verify the theoretical results.

The work proposes a generalized supertwisting algorithm (GSTA) and its constructive design strategy. In contrast with the conventional STA, the most remarkable characteristic of the proposed method is that the discontinuous term in the conventional STA is replaced with a fractional power term, which can fundamentally improve the performance of the conventional STA. It is shown that if the fractional power in the nonsmooth term becomes -1/2, the GSTA will reduce to the conventional STA. Under the GSTA, it will be rigorously verified by taking advantage of strict Lyapunov analysis that the sliding variables can finite-time converge to an arbitrarily small region in a neighborhood of the origin by tuning the gains and the fractional power. Finally, simulation studies are provided to demonstrate the superiority of the theoretically obtained results.

This article studies the finite-time (FT) convergence of a fast primal-dual gradient dynamics (PDGD), called FT-PDGD, for solving constrained optimization with general constraints and cost functions. Based on the nonsmooth analysis and augmented Lagrangian function, sufficient conditions are established for FT-PDGD to enable the realization of primal-dual optimization in FT. A specific class of nonsmooth sign-preserving functions is defined and analyzed for ensuring FT stability. Particularly, the matrix of linear equations is not required to have a full-row rank and the cost function is not necessary to be strictly convex. By introducing auxiliary variables for general linear inequality constraints, reduced sufficient conditions are further derived for the optimization with linear equality and inequality constraints after transformation. In addition, by the nonsmooth analysis, the switching dynamics evolved in both primal and dual variables are carefully investigated and the upper bound on the convergence time is explicitly provided. Moreover, as applications of FT-PDGD, several FT convergent distributed algorithms are designed to solve distributed optimization with separated and coupled linear equations, respectively. Finally, two case studies are conducted to show the performance of the proposed algorithms.

Electric vehicles (EVs) often consume large amounts of energy, and uncoordinated charging of many EVs may lead to grid overload, adversely impacting other customers. Electricity distributors require full visibility on the EV distribution to better manage operation planning of their distribution grid. However, they often have incomplete knowledge of EV presence in their network. Identifying EV customers (charging at home) using smart meter data is a nontrivial task for the grid network and energy scheduling. The difficulties include recognizing charging patterns, balancing the number of EV and non-EV customers during modeling, and building an efficient classification model. In this article, we propose a periodic pattern recognition method to extract useful EV charging patterns. Real world smart meter datasets are unbalanced with few EVs and majority of energy customers are those without EVs. We improve Kmedoids evaluated by dynamic time warping to obtain the representative non-EV training samples so that balanced samples over EV and non-EV customers can be obtained. We develop an ensemble classification model (ECM) by taking advantages of multiple classifiers, in which the optimization consists of obtaining the optimal subset of periodic patterns and the optimal parameters in each classifier and the optimal weights for combining classifiers. The superiority of the proposed ECM is demonstrated in comparison to several baseline models.

This article addresses the batch-based learning consensus for linear and nonlinear multiagent systems (MASs) with faded neighborhood information. The motivation comes from the observation that agents exchange information via wireless networks, which inevitably introduces random fading effect and channel additive noise to the transmitted signals. It is therefore of great significance to investigate how to ensure the precise consensus tracking to a given reference leader using heavily contaminated information. To this end, a novel distributed learning consensus scheme is proposed, which consists of a classic distributed control structure, a preliminary correction mechanism, and a separated design of learning gain and regulation matrix. The influence of biased and unbiased randomness is discussed in detail according to the convergence rate and consensus performance. The iterationwise asymptotic consensus tracking is strictly established for linear MAS first to demonstrate the inherent principles for the effectiveness of the proposed scheme. Then, the results are extended to nonlinear systems with nonidentical initialization condition and diverse gain design. The obtained results show that the distributed learning consensus scheme can achieve high-precision tracking performance for an MAS under unreliable communications. The theoretical results are verified by two illustrative simulations.

This article proposes a novel event-triggered second-order sliding mode (SOSM) control method for uncertain nonlinear systems. First, three saturated-like functions are predesigned to construct a new switched triggering mechanism. Under the proposed triggering mechanism, an event-triggered SOSM controller is designed to ensure that the states of the SOSM system finite-time converge to a domain of the origin and never escape from the domain. Then, to avoid Zeno behavior, two positive minimum inter-execution intervals are obtained based on different triggering conditions. Finally, a simulation study is given to verify the effectiveness of the control strategy.

This article studies the point-to-point (P2P) learning and tracking problem for networked stochastic systems with fading communications by iterative learning control. The P2P tracking problem indicates that only partial positions rather than the whole reference are required to achieve high tracking precision. An auxiliary matrix is introduced to connect the entire reference and the required tracking targets. The fading communication introduces multiplicative randomness to the transmitted signals, which leads to the biased available information. A direct correction mechanism is employed using statistics of the communication channel. A learning control scheme is then proposed with a decreasing gain sequence to ensure steady convergence in the presence of various types of randomness. Two scenarios of varying initial states are considered. The convergence of the proposed scheme is strictly established. The validity of the proposed algorithm is verified by two simulation examples.

It is challenging and critical to achieve zero error consensus tracking in multiagent systems (MASs) with nonautonomous leaders (i.e., leaders with nonzero inputs). The traditional approach is to use discontinuous controllers which may cause a chattering phenomenon. How to achieve zero error consensus tracking via a chattering-free controller is still open. We propose a class of adaptive continuous controllers to achieve zero error consensus tracking for Lipschitz nonlinear MASs with a nonautonomous leader and directed communication topology. Unlike existing works that use discontinuous functions to eliminate the impacts of leaders’ inputs, we use a continuous function by introducing an exponential decay function into the denominator. First, we design a continuous controller with fixed coupling strengths and prove that zero error consensus tracking can be achieved if the coupling strengths are greater than some positive constants. Second, we design a continuous controller with dynamic coupling strengths under which fully distributed zero error consensus tracking can be achieved. Moreover, the case with undirected communication topology is studied. Finally, three examples are given to verify the theoretical results. Specifically, convergence results between the continuous controller here and that is developed via the boundary layer technique are compared. Compared with existing works, the designed adaptive continuous controllers here can not only achieve zero error consensus tracking but also is chattering free.

This article addresses the dynamic task allocation problem with limited communication and velocity. The main challenge lie in the selection of$k$fittest winner participants and the participant contention that one winner participant may be selected by multiple targets simultaneously. Existing methods take the distance between the targets and participants as the evaluation index to select winners, which may lead to futile selection since the winner participant locating at the opposite direction of the target cannot intercept the target with limited velocity. By carefully considering both the distance between the targets and participants and the motion direction of the targets, an improved evaluation index for each target is proposed and employed such that the futile selection can be avoided in the executing process of the algorithm. Moreover, an extra evaluation index for each winner participant is presented to select one winner target to overcome the participant contention. Based on these, the control protocols are developed for targets interception, and their stability is proven by the Lyapunov theory under some suitable conditions. Finally, simulation examples are presented to illustrate the effectiveness and advantages of the proposed algorithms.

In this paper, control of Battery Storage Systems (BSS) in power distribution grids with residential consumers as well as prosumers equipped with rooftop photovoltaic (PV) solar panels and Electric Vehicles (EV) is addressed. Different features of these Distributed Energy Resources (DERs), such as intermittent behaviour and the difference between the maximum generation time and the maximum demand, have caused several issues for electricity distributors in delivering high quality power. Smart control and scheduling of ESS and EVs is a promising approach to protect the grid against extra power injection from prosumers during day times while the benefit of household owners from DERs are still achieved. In this context, the performance of model-based controllers such as model predictive controllers (MPC) is compared with model-free data driven controllers (DDC) considering different complex scenarios that may happen in a distribution grid. The control objective is to minimize the difference between the net power exchanged with the main grid from the estimated average net load of prosumers. Our study on the real consumption data of about 40 residential consumers/prosumers in Victoria, Australia, demonstrates the strength of data-driven control approaches to deal with the complex environment of power distribution grids in the presence of DERs.

In this article, we consider the distributed formation navigation problem of second-order multiagent systems subject to both velocity and input constraints. Both collision avoidance and connectivity maintenance of the network are considered in the controller design. A control barrier function method is employed to achieve multiple control objectives simultaneously while satisfying the velocity and input constraints. First, a nominal distributed leader-following formation controller is proposed which satisfies the velocity and input constraints uniformly and handles switching communication graphs. A nonsmooth analysis is employed to prove the global convergence of the controller. Then, a topology-based connectivity maintenance strategy using a new notion of the formation-guided minimum cost spanning tree is proposed and the corresponding barrier function-based constraints are derived. The barrier function-based collision-avoidance conditions are also developed. All barrier function-based constraints are then combined to formulate a quadratic programming problem which modifies the nominal controller when necessary to achieve both collision avoidance and connectivity maintenance. Simulation results demonstrate the effectiveness of the proposed control strategy.

This article investigates the distributed time-varying optimization problem for second-order multiagent systems (MASs) under limited interaction ranges. The goal is to seek the minimum of the sum of local time-varying cost functions (CFs), where each CF is only available to the corresponding agent. Limited communication range refers to the scenario where the agents have limited sensing and communication capabilities, that is, a pair of agents can communicate with each other only if their distance is within a certain range. To handle such a problem, a new continuous connectivity-preserving mechanism is presented to preserve the connectivity of the considered network. Then, two distributed optimization algorithms are presented to solve the optimization problem with time-varying CFs and time-invariant CFs, respectively. Theoretical analysis and two numerical examples are provided to verify the effectiveness of the methods.

In this article, the free-will arbitrary time consensus is formulated for multiagent systems. This consensus protocol is independent of initial conditions and any other system parameters. With such a protocol, the multiagent system is shown to attain consensus as well as average consensus within the prespecified arbitrary time. Agents rendezvous can also be accomplished with the given protocol. Communication imperfections are easily handled with the designed protocol. Robust free-will arbitrary time consensus protocol is also designed. The stability of such nonlinear nonautonomous protocols is established using suitable Lyapunov functions. Simulation examples confirm the theoretical findings.

In this article, the asymptotic tracking consensus problem of higher-order multiagent systems (MASs) with general directed communication graphs is addressed via designing event-triggered control strategies. One common assumption utilized in most existing results on such tracking consensus problem that the inherent dynamics of the leader are the same as those of the followers is removed in this article. In particular, two cases that the dynamics of the leader are subjected, respectively, to bounded input and unknown nonlinearity are considered. To do this, distributed event-triggered observers are first constructed to estimate the state information of the leader. Then, local event-triggered tracking control protocols are designed for each follower to complete the goal of tracking consensus. One distinguishing feature of the present distributed observers lies in the fact that they could avoid the continuous monitoring for the states of the neighbors' observer states. It is also worth pointing out that the present tracking consensus control strategies are fully distributed as no global information related to the directed communication graph is involved in designing the strategies. Two simulation examples are finally presented to verify the efficiency of the theoretical results.

A colored traveling salesman problem (CTSP) as a generalization of the well-known multiple traveling salesman problem utilizes colors to distinguish the accessibility of individual cities to salesmen. This work formulates a precedence-constrained CTSP (PCTSP) over hypergraphs with asymmetric city distances. It is capable of modeling the problems with operations or activities constrained to precedence relationships in many applications. Two types of precedence constraints are taken into account, i.e., 1) among individual cities and 2) among city clusters. An augmented variable neighborhood search (VNS) called POPMUSIC-based VNS (PVNS) is proposed as a main framework for solving PCTSP. It harnesses a partial optimization metaheuristic under special intensification conditions to prepare candidate sets. Moreover, a topological sort-based greedy algorithm is developed to obtain a feasible solution at the initialization phase. Next, mutation and multi-insertion of constraint-preserving exchanges are combined to produce different neighborhoods of the current solution. Two kinds of constraint-preserving k -exchange are adopted to serve as a strong local search means. Extensive experiments are conducted on 34 cases. For the sake of comparison, Lin-Kernighan heuristic, two genetic algorithms and three VNS methods are adapted to PCTSP and fine-tuned by using an automatic algorithm configurator-irace package. The experimental results show that PVNS outperforms them in terms of both search ability and convergence rate. In addition, the study of four PVNS variants each lacking an important operator reveals that all operators play significant roles in PVNS.

This article aims to establish an appointed-time observer-based framework to efficiently address the resilient consensus control problem of linear multiagent systems with malicious attacks. The local appointed-time state observer is skillfully designed for each agent to estimate the agent's actual state value at the appointed time, even in the presence of unknown malicious attacks. Based on the state estimation, a new kind of resilient control strategy is proposed, where a virtual system is constructed for each agent to generate an ideal state value such that the consensus of normal agents can be achieved with the exchange of ideal state values among neighboring agents. To specify the consensus trajectory while achieving resilient consensus, the leader-follower resilient consensus is further studied, where the leader is assumed to be a trusted agent with a bounded control input. Compared with the existing results on the resilient consensus, the proposed distributed resilient controller design reduces the requirement on communication connectivity significantly, where the allowable communication graph is only assumed to contain a directed spanning tree. To verify the theoretical analysis, numerical simulations are finally provided.

In this article, we investigate the synchronization of complex networks with general time-varying delay, especially with nondifferentiable delay. In the literature, the time-varying delay is usually assumed to be differentiable. This assumption is strict and not easy to verify in engineering. Until now, the synchronization of networks with nondifferentiable delay through adaptive control remains a challenging problem. By analyzing the boundedness of the adaptive control gain and extending the well-known Halanay inequality, we solve this problem and establish several synchronization criteria for networks under the centralized adaptive control and networks under the decentralized adaptive control. Particularly, the boundedness of the centralized adaptive control gain is theoretically proved. Numerical simulations are provided to verify the theoretical results.

This article works on the consensus problem of nonlinear multiagent systems (MASs) under directed graphs. Based on the local output information of neighboring agents, fully distributed adaptive attack-free protocols are designed, where speaking of attack-free protocol, we mean that the observer information transmission via communication channel is forbidden during the whole course. First, the fixed-time observer is introduced to estimate both the local state and the consensus error based on the local output and the relative output measurement among neighboring agents. Then, an observer-based protocol is generated by the consensus error estimation, where the adaptive gains are designed to estimate the unknown neural network constant weight matrix and the upper bound of the residual error vector. Furthermore, the fully distributed adaptive attack-free consensus protocol is proposed by introducing an extra adaptive gain to estimate the communication connectivity information. The proposed protocols are in essence attack-free since no observer information exchange among agents is undertaken during the whole process. Moreover, such a design structure takes the advantage of releasing communication burden.

With fast developments in communication technologies, a large number of practical systems adopt the networked control structure. For this structure, the fading problem is an emerging issue among other network problems. It has not been extensively investigated how to guarantee superior control performance in the presence of unknown fading channels. This article presents a learning strategy for gradually improving the tracking performance. To this end, an iterative estimation mechanism is first introduced to provide necessary statistical information such that the biased signals after transmission can be corrected before being utilized. Then, learning control algorithms incorporating with a decreasing step-size sequence are designed for both output and input fading cases. The convergence in both mean-square and almost-sure senses of the proposed schemes is strictly proved under mild conditions. Illustrative simulations verify the effectiveness of the entire learning framework.

This article investigates the distributed average tracking (DAT) problem for disturbed second-order multiagent systems, where a crowd of agents is required to track the average of the multiple time-varying signals. First, a new kind of distributed average estimator is developed for each agent to estimate the average of the multiple time-varying signals in finite time. The protocol possesses the distinguished feature of robustness to initialization errors, which can recover from network alterations. Then, an observer-based finite-time tracking protocol is proposed to make each agent exactly track the average of the multiple time-varying signals in finite time in the absence of velocity measurement. By carefully analyzing the dynamic properties of the tracking error system, a suitable Lyapunov function is constructed to estimate the settling time for convergence of the tracking error system theoretically. Furthermore, an adaptive DAT protocol is proposed, which is a fully distributed protocol because it can solve the DAT problem without using any global information. Finally, two simulation examples are provided to verify the effectiveness of the methods.

This article investigates the sliding-mode control design problem for uncertain time-varying delayed systems. A novel non-negative constraints approach is proposed, which can be used to determine the stability of the controlled systems during the sliding motion. A new stability condition is obtained, which is easy to satisfy and verify. The parameters of the sliding-mode surface can be calculated by solving the non-negative constraints-based optimization problem defined in this article. Simulation examples are presented to illustrate the effectiveness and advantages of the developed strategy.

Distributed stabilization problem is studied in this article for multiple heterogeneous agents in the uncertain strong–weak competition network with exogenous disturbances, where the agents are modeled by the second-order systems with different nonlinear intrinsic dynamics, and the network uncertainty is characterized by unknown nonzero parameters, which contains three different relationships among agents: 1) cooperation; 2) strong competition; and 3) weak competition. To achieve distributed stabilization, the whole network is first divided into two parts: 1) identifiable part and 2) unidentifiable part, and a new distributed robust integral sign of the error (RISE) controller is designed for each agent, where the selection rules of the corresponding parameters are given. It is shown that the heterogeneous multiagent system (MAS) can achieve distributed stabilization no matter whether the identifiable part is structurally balanced or not. Furthermore, it is proved that the global distributed stabilization is achieved for the heterogeneous agents provided that the partial derivatives of the nonlinear intrinsic dynamics are bounded. Finally, two numerical examples are given to demonstrate the effectiveness of the designed controller.

This article addresses the problem of synchronization of impulsive networks with switching topologies. A new synchronization framework is established with an emphasis on settling time estimation. The impulsive networks consist of physical nodes and cyber modules. For physical nodes, states are changed impulsively at discrete time instants due to some switching phenomena or unexpected sudden noises. For cyber modules, two cases of switching scenarios are considered for information exchange patterns during specific time intervals. In the first case, cyber modules lose all the communication links with others, resulting in disconnected topologies. Then, a distributed controller is proposed for nodes without intrinsic nonlinear dynamics. A distinguished feature of this controller is its capability to estimate a bound for settling time, beyond which the synchronization with respect to a virtual target is guaranteed. In the second case, cyber modules lose some communication links but build other new ones with the help of a smart communication center to form connected topologies. A distributed controller is further designed for nodes in the presence of intrinsic nonlinear dynamics. Accordingly, a sufficient condition is derived to achieve synchronization with an estimated settling time bound. For both cases, the estimated bounds are able to reveal the relationship between the impulsive strength and the synchronization performance. Finally, numerical examples including a case study on a modified IEEE 34 bus test feeder are provided to demonstrate the effectiveness of the proposed controllers.

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.

Smart grid development and evolution requires tools that facilitate the use of data collected from smart grid devices. Smart metering analytics allows stakeholders to gain insights from smart metering data that aids in network management and regulatory compliance. Rule extraction through interim summarisation provides a viable approach to classifying daily electricity consumption load profiles for the solar PV detection problem. This work explains how this method can be applied to smart metering data and shows how the rules generated by this approach can be interpreted to provide insight into the solar PV detection problem. By providing an example approach to smart metering analytics that is interpretable this work addresses the need for white box classification models in the smart grid domain. It is shown that the application achieved up to 88.19% classification accuracy when classifying daily load profiles from our industry supplied dataset.

The emergence of low latency and high bandwidth 5G networks, alongside localized computation and data storage of edge computing are enabling real-time applications in industrial settings, such as smart grid, smart cities, and smart factories. The resolution, frequency and variety of data streams generated by such applications are not effectively processed and analysed by contemporary machine learning algorithms. This challenge is further complicated by the unlabelled and non-deterministic nature of the data streams. Hardware accelerated machine learning has been proposed to address some of these challenges but limited work has been published on unsupervised learning from unlabelled data. In this paper, we extend the hardware accelerated Self Organizing Map (SOM) algorithm by optimizing the learning rule for computational efficiency, followed by a comparative empirical evaluation with two other variants, tri-state SOM and integer SOM. We have used two datasets representative of real-time industrial applications in 5G networks and smart grids, for this evaluation.

With the increasing popularity of domestic solar PV systems there is a need for smart grid network operators to be able to identify solar PV systems attached to their networks. This need is driven by human safety, equipment safety, and regulatory compliance concerns. Given the implementation of smart metering as part of the evolution toward smart grids and the availability of smart metering data, methods that automate the identification of solar PV systems from consumption data are needed to address these concerns. This paper proposes an optimal template approach with genetic algorithm for solar PV detection, which successfully classifies solar PV and non-solar PV customers by utilising genetic algorithm optimisation to find optimal template pairs and matching observations to the closest template. This is done by using domain knowledge to specify a template parameterisation specific to the problem and using genetic algorithm optimisation to find template pairs that are optimised for accuracy.

Designing an efficient distributed economic dispatch (DED) strategy for the smart grid (SG) in the presence of multiple generators plays a paramount role in obtaining various benefits of a new generation power system, such as easy implementation, low maintenance cost, high energy efficiency, and strong robustness against uncertainties. It has drawn a lot of interest from a wide variety of scientific disciplines, including power engineering, control theory, and applied mathematics. We present a state-of-the-art review of some theoretical advances toward DED in the SG, with a focus on the literature published since 2015. We systematically review the recent results on this topic and subsequently categorize them into distributed discrete- and continuous-time economic dispatches of the SG in the presence of multiple generators. After reviewing the literature, we briefly present some future research directions in DED for the SG, including the distributed security economic dispatch of the SG, distributed fast economic dispatch in the SG with practical constraints, efficient initialization-free DED in the SG, DED in the SG in the presence of smart energy storage batteries and flexible loads, and DED in the SG with artificial intelligence technologies.

This paper proposes a distributed hierarchical control framework for energy storage systems (ESSs) in DC microgrid clusters, which achieves voltage regulation and current sharing for ESSs in each microgrid as well as the whole microgrid cluster. The primary control stage adopts a droop controller which only requires local information while the secondary control stage provides correction terms for ESSs within microgrids. The tertiary control stage samples the pinned ESSs in different microgrids with low sampling rate to provide the voltage setpoint, which ensures global current sharing among microgrid cluster. The corresponding multilayered event-triggered consensus algorithm for clusters is proposed to reduce the communication cost generated by operation of the distributed controller. Both the control framework and the consensus algorithm can be extended for satisfying higher dimensional regulation needs. The controller is validated in a DC microgrid cluster through simulation under different scenarios, and the results illustrate the effectiveness of the proposed controller.

In this paper, we propose a simple cohesive framework to find an optimal directed control network topology with minimum number of links while a prescribed decay rate is satisfied in the transient response of a distributed control system. In order to guarantee the system's decay rate to be faster than a prespecified value, a constraint on the dominant eigenvalue of the system is required to be considered. This results in a nonconvex optimization problem as eigenvalue of a parametric nonsymmetric matrix is a nonconvex, nonsmooth, and even non-Lipschitz function. Here, we present a convex equivalent optimization problem whose minimizer also solves this eigenvalue optimization problem. This optimization problem proposes a state-feedback matrix which results in a decay rate faster than a given value while input signal costs are considered. The equivalent optimization problem in combination with sparsity-promoting optimal control constitutes a combinatorial optimization problem. Using alternating direction method of multipliers, the problem is decomposed into a chain of analytically solvable subproblems which are differentiable and separable. The proposed optimization framework includes relative preference between the topology of the control network and the decay rate of the system. The simulation results show the effectiveness of the proposed framework.

We aim to solve the consensus problem of linear multiagent systems (MASs) with input saturation under directed interaction graphs in this article, where only local output information of neighbors is available for each agent. By introducing the multilevel saturation feedback control approach, a fully distributed adaptive anti-windup protocol is proposed, where a local observer, a distributed observer, as well as an anti-windup observer are separately constructed for each agent to estimate consensus error, achieve consensus for a certain internal state, and provide anti-windup compensator, respectively. A dual protocol is further presented with the distributed observer designed based on the input matrix, which gives a thorough view on the connection between the distributed observer and the anti-windup observer, and provides the opportunity to reduce the order of the controller by designing the integrated distributed anti-windup observer. Then, three types of distributed anti-windup protocols are proposed based on the integrated distributed anti-windup observer, which requires different assumptions. Specifically, the first protocol needs two-hop relay information to generate the local observer to estimate consensus error; the second protocol designs the local observer with absolute output information to estimate the state instead; while the last protocol introduces certain assumption on transmission zero of agents' dynamics to design the unknown input observer to estimate consensus error. All of the protocols are validated by strictly theoretical proof, and are illustrated by performing simulation examples.

Multisensor systems are widely applied to realize the comprehensive monitoring and control as they feature multiple individual sensors/outputs. In such systems, different sensors can receive different types of operation signals, such as pressure, temperature, and volume. The desired references for different sensors may conflict in that an input signal that can precisely track all references simultaneously does not exist yet. This gap has motivated us to consider the incompatible multiobjective tracking problem for multisensor systems with random process disturbances and measurement noises. Our primary approach is to solve the problem as a weighted optimization problem using iterative learning control (ILC). First, the best achievable trajectory based on multiple references, as well as the weighted optimal tracking index, is carefully defined and then the ILC algorithms with both fixed and decreasing steps are proposed to generate the input sequence. The output driven by the proposed algorithms has been strictly proven to converge to the best achievable trajectory in both the mean square and almost-sure senses. Extensions to a networked implementation, in which the networks between the sensors and the learning controller suffer random data dropouts, are also detailed. Illustrative simulations are provided to verify the theoretical results.

In building-microgrid communities, renewable generation and time-varying load usually cause power fluctuations, which influence the ancillary support to the main grid. Thermostatically controlled loads (TCLs) can be utilized to compensate such power variations due to their aggregated and controllable power consumptions. Meanwhile, one basic requirement for the users' side of TCLs is to realize the fair sharing of power states and comfort states. This article proposes a distributed event-based control strategy, where information of neighboring TCLs is exchanged only when a dynamic event-triggered condition is satisfied, and thus it intelligently determines the necessary transmission frequency to save communication resources. From a cybersecurity perspective, the communication network of TCLs may be subject to hybrid attacks, for example, denial-of-service (DoS) and false data-injection (FDI) attacks. During DoS attack intervals, no information can be communicated even through the event-triggered condition is satisfied. Furthermore, the control inputs may also be tampered by FDI attacks. By utilizing the Lyapunov stability and hybrid control theories, sufficient conditions regarding the attack parameters are derived such that fair sharing of power states and comfort states of all involved TCLs can be achieved exponentially. The exclusion of Zeno behaviors is proved and a corollary for ideal communication situations is also deduced. Finally, simulation examples with various attack parameters are conducted to verify the effectiveness of the main results.

Reducing time consumption of economical power allocation operation among multiple microgrid (MG) clusters can significantly enhance the balance efficiency between power supply and load demand. In this article, a novel secondary power management strategy with cluster-oriented two-layer cooperative (TLC) framework is proposed, by which both the power sharing requirement for all DGs within each MG cluster and the economical power allocation demand among multiple MG clusters can be simultaneously realized during the secondary control process. In the framework, all cluster-head distributed generators (DGs) constitute the upper control layer, which enable the economical power allocation operation among multiple MG clusters, and all noncluster-head DGs constitute the lower control layer allowing the power sharing adjustment within each MG cluster. All the power mismatches across the TLC framework are fed back in the primary control to generate the frequency/voltage nominal set-points. Sufficient conditions, in terms of control time constants of the TLC framework and connectivity of the two-layer cyber network, are derived to guarantee the stability of the entire multiple MG cluster system with both power balance and power generation constraints. Specially, both the lower and upper layer controllers are designed based on a sparse two-layer cyber network, allowing different numbers of heterogeneous DGs in each MG cluster. The effectiveness of the control methodology is verified by the simulation of a multiple ac MG cluster system in MATLAB/SimPowerSystems.

While neural adaptive control is widely used for dealing with continuous- or discrete-time dynamical systems, less is known about its mechanism and performance in hybrid dynamical systems. This article develops analytical tools to investigate the neural adaptive tracking control of the hybrid Markovian switching networks with heterogeneous nonlinear dynamics and randomly switched topologies. A gradient-descent adaptation law built on neural networks (NNs) is presented for efficient distributed adaptive control. It is shown that the proposed control scheme can guarantee a stable closed-loop error system for any positive control gain and tuning gain. The tracking error is demonstrated to be practically uniformly exponentially stable with a threshold in the mean-square sense. This study further reveals how the topological structure affects the NN function, by measuring the influence of the switched topologies on the learning performance.

A novel data-driven learning control scheme is proposed for unknown systems with unknown fading sensor channels. The fading randomness is modeled by multiplicative and additive random variables subject to certain unknown distributions. In this scheme, we propose an error transmission mode and an iterative gradient estimation method. Unlike the conventional transmission mode where the output is directly transmitted back to the controller, in the error transmission mode, we send the desired reference to the plant such that tracking errors can be calculated locally and then transmitted back through the fading channel. Using the faded tracking error data only, the gradient for updating input is iteratively estimated by a random difference technique along the iteration axis. This gradient acts as the updating term of the control signal; therefore, information on the system and the fading channel is no longer required. The proposed scheme is proved effective in tracking the desired reference under random fading communication environments. Theoretical results are verified by simulations.

In this study, the stability of a hierarchical network with delayed output is discussed by applying a kind of optimal periodic control. To reduce the number of the nodes of the original hierarchical network, an aggregation algorithm is first presented to take some nodes with the same information as an aggregated node. Furthermore, the stability of the original hierarchical network can be guaranteed by the optimal periodic control of the aggregated hierarchical network. Then, an optimal control scheme is proposed to reduce the bandwidth waste in information transmission. In the control scheme, the time sequence is separated into two parts: the deterministic segment and the dynamic segment. With the optimal control scheme, two targets are achieved: 1) the outputs of the original and aggregated hierarchical system are both asymptotically stable and 2) the nodes with slow convergent rate can catch up with the convergence speeds of other nodes.

This article presents a full mathematical proof of the characteristic modeling approach for high-order linear dynamical systems. It explores the nature of the characteristic model in rigorous mathematical forms, also showing why and how the high-order dynamics can be compressed into the lower-order characteristic model. The relationships between high-order linear continuous dynamical systems, discrete-time characteristic model coefficients, and sampling-time intervals are investigated.

In this paper, characteristic model-based modeling and control approaches for complex dynamical networks based on sampled data are studied. It shows that the characteristic model, in which underline network topological structures are simplified, can provide a straightforward and implicit description for network dynamics. The induced parameter estimation method can further make the model adaptive and purely data-driven. Moreover, a control law based on this model is also proposed to govern the network dynamics. Finally, the theoretical results are verified through numerical simulations of modeling and stabilizing a dynamical network.

Distributed consensus tracking problem of networked agent systems with directed topologies under denial-of-service (DoS) attacks is investigated. The considered networked agent network consists of a physical layer with fixed physical links and a cyber layer with cyber control units. Both the communication network connecting these two layers and the cyber communication network within the cyber layer may subject to malicious DoS attacks. These two types of attacks have different impacts on the networked system; the former one affects the timely update of control inputs, and the latter influences the connection weights of the cyber communication graph. First, for DoS signals occurring in the communication network which transmits the state and control input information between the two layers, the distributed control protocol based on the event-triggered scheme and locally deployed estimators are designed. Efficient algorithms for selecting event-triggered control parameters and Zeno-free triggered parameters are given to ensure the mean-square consensus. The relationships between the system's parameters and the features of DoS attacks are successfully revealed. Second, corresponding theoretical analysis is derived for consensus tracking of the networked systems when DoS attacks are launched within the cyber layer. Conditions concerning the length of repairing time and indexes of DoS attacks are given by utilizing hybrid control theory. At last, the effectiveness of the obtained results is demonstrated by performing simulations on multirobot systems.

One fundamental yet challenging issue in security control for resilient complex networks is to construct distributed control laws for the networks to perform various cooperative tasks in the presence of failures and attacks, where resilient indicates that the complex networks are exposed to the environment with cyber uncertainties and malicious adversaries. This is particularly important in today's critical infrastructure networks since most of them are vulnerable to attacks in the era of the Internet. Inspired by this observation, this paper focuses on synchronization control for resilient complex networks subject to cyber and physical attacks, where the states of nodes being attacked may change abruptly (i.e., the synchronization error may suffer impulsive disturbances), and some nodes as well as their corresponding connections may not work in some instances. Suppose that a smart control center is equipped in the considered network to detect the attacks in real time. Furthermore, the nodes and communication channels are assumed to be recovered through some repair work after detecting the attacks. On the theoretical side, by using the M-matrix theory, we get a few sufficient criteria to guarantee the achievement of secure synchronization against attacks on both nodes and communication links. On the algorithmic side, security control algorithm and architecture are proposed to select the coupling strength and the feedback gain matrix to realize synchronization. Finally, we perform two simulation examples to validate our theoretical results.

A great deal of attention from various scientific communities has been recently drawn to complex network systems (CNSs), with many profound results established in this active research field. This article provides a state-of-the-art survey on coordination and control of CNSs with switching network topologies, with emphasis on relationships between the switchings among different topology candidates and the network controllability, and between the switchings among different topology candidates and the emergence of coordination behaviors (including synchronization, consensus, and containment) of such CNSs. First, some fundamental properties of CNSs and the essentials of analytical methodologies for the stability of the fixed point of switched dynamical systems are briefly reviewed. Then, network controllability and the emergence of coordination behaviors of CNSs with switching topologies and the corresponding analytical approaches are discussed in detail, where some of the existing results along these topics are presented in a tutorial-like fashion. This article ends by presenting some interesting future research topics on the coordination and control of CNSs with switching topologies.

This article is concerned with the problem of global finite-time output stabilization for a class of second-order nonlinear systems with both uncertain nonlinear dynamics and unknown external disturbance. Specifically, at the first step, without considering the external disturbance, a global finite-time state feedback controller is proposed to dominate uncertain nonlinear dynamics of the second-order system. To address the more challenging case where only the system output is available, a novel design idea of the nonseparation principle is employed. By treating the unknown external disturbance as a generalized state, a finite-time convergent extended state observer is constructed to estimate the unmeasured state and the unknown external disturbance. Based on this observer, an output-based composite controller with finite-time convergence is developed. The global uniform finite-time stability of the overall closed-loop system is proven based on the Lyapunov method. Simulation results of the inverted pendulum system demonstrate the superiority of the proposed control method in terms of both convergence performance and disturbance rejection performance.

Multi-modal journey planning, which allows multiple modes of transport to be used within a single trip, is becoming increasingly popular, due to a vast practical interest and the increasing availability of data. In real-life situations, transport networks often involve uncertainty, and yet, most approaches assume a deterministic environment, making plans more prone to failures such as significant delays in the arrival or waiting for a long time at stations. In this paper, we tackle the multi-criteria stochastic journey planning problem in multi-modal transportation networks. We consider the problem as a probabilistic conditional planning problem, and we use Markov Decision Processes to model the problem. Journey plans are optimised simultaneously against five criteria, namely: travel time, journey convenience, monetary cost, CO2, and personal energy expenditure (PEE). We develop an NSGA-III-based solver as a baseline search method for producing optimal policies for travelling from a given origin to a given destination. Our empirical evaluation uses Melbourne transportation network using probabilistic density functions for estimated departure/arrival time of the trips. Numerical results demonstrate the effectiveness of the proposed method for practical purposes and provide strong evidence in favour of contingency planning for journey planning problem.

This paper proposes several hierarchical controller-estimator algorithms (HCEAs) to solve the coordination problem of networked Euler-Lagrange systems (NELSs) with sampled-data interactions and switching interaction topologies, where the cases with both discontinuous and continuous signals are successfully addressed in a unified framework. The HCEAs comprise two main layers (i.e., a control layer and an estimator layer) and one optional layer (i.e., a filter layer), in which the coordination problem is tackled in the main layers and the transient response can be optionally smoothed in the filter layer. For stabilizing the corresponding cascade closed-loop systems, several sufficient conditions on the upper bound of the aperiodic sampling intervals and the lower bound of the control parameters are established. In addition, the HCEAs are extended to address the task-space coordination problem of networked heterogeneous robotic systems, which shows the versatility of the HCEAs. Finally, comparison studies and simulation results are provided to demonstrate the effectiveness, significance, and advantages of the presented algorithms.

Delayed impulsive controllers are proposed in this paper to enable the agents in a class of second-order multiagent systems (MASs) to achieve state consensus, based, respectively, on the relative full-state and partial-state sampled-data measurements among neighboring agents. It is a challenging task to analyze the consensus behaviors of the considered MASs as the dynamics of such MASs will be subjected to joint effects from delay-dependent impulses, aperiodic sampling, and switchings among different communication graphs. A novel analytical approach, based upon the discretization method, state augmentation, and linear state transformation, is developed to establish the sufficient consensus criteria on the range of the impulsive intervals and the control parameters. Remarkably, it is found that consensus in the closed-loop MASs can be always ensured by skillfully selecting the control parameters as long as the nonuniform delays and the impulsive intervals are bounded. A numerical example is finally performed to validate the effectiveness of the proposed delayed impulsive controllers.

Faster convergence is always sought in many applications. Designing fixed-time control has recently gained much attention since, for this type of control structure, the convergence time of the states does not depend on initial conditions, unlike other control methods providing faster convergence. This paper proposes a new distributed algorithm for second-order consensus in multiagent systems by using a full-order fixed-time convergent sliding surface. The stability analysis is performed using the Lyapunov function and bi-homogenous property. Moreover, the proposed control is smooth and free from any singularity. The robustness of the proposed scheme is verified both in the presence of Lipschitz disturbances and uncertainties in the network. The proposed method is compared with a state-of-the-art method to show the effectiveness.

In proof-of-work (PoW)-based blockchain networks, the miners contribute their distributed computation in solving a crypto-puzzle competition to win the reward. To secure stable profits, some miners organize mining pools and share the rewards from the pool in proportion to each miner's contribution. However, some miners may exhibit malicious behaviors which cause a waste of distributed computation resource, even posing a threat on the efficiency of blockchain networks. In this paper, we propose a new game-theoretic framework to incentivize miners mining honestly and help to bring about a higher total welfare of blockchain networks. We first formulate the mining process as a noncooperative iterated game. We then propose a mechanism in terms of zero-determinant strategies (ZD strategies) to encourage the cooperative mining and improve the efficiency of mining in PoW-based blockchain networks. In addition, we theoretically analyze the maximum system welfare of the target pool through the method of optimization. Numerical illustrations are also presented to support our theoretical results.

Semantic segmentation of point cloud data, an efficient 3-D scattered point representation, is a fundamental task for various applications, such as autonomous driving and 3-D telepresence. In recent years, deep learning techniques have achieved significant progress in semantic segmentation, especially in the 2-D image setting. However, due to the irregularity of point clouds, most of them cannot be applied to this special data representation directly. While recent works are able to handle the irregularity problem and maintain the permutation invariance, most of them fail to capture the valuable high-dimensional local feature in fine granularity. Inspired by fuzzy mathematical methods and the analysis on the drawbacks of current state-of-the-art works, in this article, we propose a novel deep neural model, Fuzzy3DSeg, that is able to directly feed in the point clouds while maintaining invariant to the permutation of the data feeding order. We deeply integrate the learning of the fuzzy neighborhood feature of each point into our network architecture, so as to perform operations on high-dimensional features. We demonstrate the effectiveness of this network architecture level integration, compared with methods of the fuzzy data preprocessing cascading neural network. Comprehensive experiments on two challenging datasets demonstrate that the proposed Fuzzy3DSeg significantly outperforms the state-of-the-art methods.

The rapid growth in autonomous industrial environments has increased the need for intelligent video surveillance. As a predominant element of video surveillance, recognition of complex human movements is important in a wide range of surveillance applications. However, the current state-of-the-art video surveillance techniques use supervised deep learning pipelines for human activity recognition (HAR). A key shortcoming of such techniques is the inability to learn from unlabeled video streams. To operate effectively in natural environments, video surveillance techniques have to be able to handle huge volumes of unlabeled video data, monitor and generate alerts and insights derived from multiple characteristics such as spatial structure, motion flow, color distribution, etc. Furthermore, most conventional learning systems lack memory persistence capability which can reduce the influence of outdated information in memory-guided decision-making resulting in limiting plasticity and overfitting based on specific past events. In this article, we propose a new adaptation of the Growing Self-Organizing Map (GSOM) to address these shortcomings by 1) adopting two proven concepts of traditional deep learning, hierarchical, and multistream learning, applied into GSOM self-structuring architecture to accommodate learning from unlabeled video data and their diverse characteristics, 2) address overfitting and the influence of outdated information on neural architecture by implementing a transience property in the algorithm. We demonstrate the proposed model using three benchmark video datasets and the results confirm its validity and usability for HAR.

Rapid developments in urbanization and autonomous industrial environments have augmented and expedited the need for intelligent real-time video surveillance. Recent developments in artificial intelligence for anomaly detection in video surveillance only address some of the challenges, largely overlooking the evolving nature of anomalous behaviors over time. Tightly coupled dependence on a known normality training dataset and sparse evaluation based on reconstruction error are further limitations. In this article, we propose the incremental spatiotemporal learner (ISTL) to address challenges and limitations of anomaly detection and localization for real-time video surveillance. ISTL is an unsupervised deep-learning approach that utilizes active learning with fuzzy aggregation, to continuously update and distinguish between new anomalies and normality that evolve over time. ISTL is demonstrated and evaluated on accuracy, robustness, computational overhead as well as contextual indicators, using three benchmark datasets. Results of these experiments validate our contribution and confirm its suitability for real-time video surveillance.

This article proposes a novel approach to design sliding-mode control (SMC) for an induction motor (IM) in the presence of operational constraints. Different from the traditional techniques in SMC, the proposed method assumes the existence of a constant input disturbance and incorporates it in the switching current control law. Effectively, this leads to integral action through disturbance estimation together with an antiwindup mechanism naturally occurring when the control signal reaches its operational limits. The finite-time convergence of the SMC law is established through a Lyapunov analysis. Experimental evaluations are performed on an industrial-sized IM, where the current dynamics of the motor are controlled using SMC, and a velocity proportional-integral (PI) controller is used for the outer-loop control system. Experimental results reveal that the proposed sliding-mode current control systems provide much improved closed-loop control performance over the traditional SMC system. Further comparative experimental studies with well-designed PI current controllers provide insight into the characteristics of the proposed current control systems.

The increasing penetration of photovoltaic (PV) generators has led to a reduction in the effectiveness of existing strategies for restoring the power distribution network. This article proposes a dynamic robust restoration (DRR) framework for the recovery of outage power considering uncertain PV outputs and demands. This framework is presented in two subsequent steps. In the first step, optimal decisions regarding the network configurations are generated. The second step then computes the modified dynamic Distflow equations and constraints under consideration of the worst operating conditions over the associated uncertainty sets with the aim of maximizing the recovery of outage power. The DRR model is formulated as a bilevel mixed-integer linear programming problem. A decomposition algorithm in a master-sub structure is used to solve the resulting system. The results of case studies show that the proposed DRR model yields obvious advantages over the existing deterministic dynamic restoration model in terms of robustness against system uncertainties.

Many networked systems display some kind of dynamics behaving in a style with both continuous and impulsive communications. The cooperation behaviors of these networked systems with continuous connected or impulsive connected or both connected topologies of communications are important to understand. This paper is devoted to the synchronization of the networked system with continuous and impulsive hybrid communications, where each topology of communication mode is not connected in every moment. Two kind of structures, i.e., fixed structure and switching structures, are taken into consideration. A general concept of directed spanning tree (DST) is proposed to describe the connectivity of the networked system with hybrid communication modes. The suitable Lyapunov functions are constructed to analyze the synchronization stability. It is showed that for fixed topology having a jointly DST, the networked system with continuous and impulsive hybrid communication modes will achieve asymptotic synchronization if the feedback gain matrix and the average impulsive interval are properly selected. The results are then extended to the switching case where the graph has a frequently jointly DST. Some simple examples are then given to illustrate the derived synchronization criteria.

This study investigates the problem of adaptive decentralized tracking control for a class of interconnected nonlinear systems with input quantization, unknown function, and time-delay, where the time-delay and interconnection terms are supposed to be bounded by some completely unknown functions. An adaptive decentralized tracking controller is constructed via the backstepping method and neural network technique, where a sliding-mode differentiator is presented to estimate the derivative of the virtual control law and reduce the complexity of the control scheme. On the basis of Lyapunov analysis scheme and graph theory, all the signals of the closed-loop system are uniformly ultimately bounded. Finally, an application example of an inverted pendulum system is given to demonstrate the effectiveness of the developed methods.

The group consensus problems of heterogeneous multiagent networks with input time delays are investigated in this paper. In this complex networks, the agents' dynamics are modeled by the first-order and the second-order multiagent systems, where a novel dynamic group consensus protocol is designed through the competitive relationship among the agents. By using matrix theory and the frequency-domain method, some algebraic criteria are theoretically established for reaching a group consensus in the following two cases: 1) with the identical and 2) different input time delays. Meanwhile, the conservative assumptions existed in the relevant literatures are absolutely relaxed, i.e., the in-degree balance and the geometric multiplicity of zero eigenvalue of Laplacian matrix are at least two. Finally, the effectiveness of our results is illustrated by numerical examples.

In this paper, a robust neighbor-based distributed cooperative control strategy is proposed for dc cyber-physical microgrids, considering communication delays and slow switching topologies. The proposed robust control strategy can synchronize the voltages of a dc microgrid to the desired value while achieving the optimal load sharing for minimizing distributed energy resources' (DERs) generation cost to achieve their economic operation at the same layer via a sparse communication network considering communication delays and slow switching topologies synchronously. The continuous interaction of physical-electrical and cyber networks generally exacerbates the occurrence of communication delays. Moreover, the arbitrary switching topologies could destroy the system's transient characteristics at the switching time instants. To further quantify these impacts on the system stability, the communication delay and average switching dwell-time-dependent control conditions for the proposed control strategy are proved based on the Lyapunov-Krasovskii theory. Some sufficient conditions for the exponential stability of the cyber-physical delayed-switching system are developed, which guarantees the robustness of the proposed strategy against the communication delays and dynamically changing interaction topologies. The proposed control protocols are shown to be fully distributed and implemented through a sparse communication network. Finally, several cases on a modified IEEE 34-bus test network are investigated which demonstrate the effectiveness and performance of the results.

This paper is concerned with the problem of designing a control strategy to guarantee stability in probability for high-order stochastic systems. The control hinges on the sliding-mode scheme. We first prove that the control design can stabilize a fast terminal sliding-mode function. We further show that the stable sliding mode is able to assure the stability in probability for the high-order stochastic system. The resulting system may account nonvanishing diffusion terms, which is regarded as a novelty. The derived control strategy has implications for applications-experiments are carried out to control the pitch angle of a one-degree-of-freedom aerial device. The corresponding experimental results demonstrate the benefits and effectiveness of our approach.

In this paper, the global pinning synchronization problem is studied for a complex dynamical network to follow a dynamic target system. A distinguished feature of the present network model is that the target system may have some predesigned control inputs. This implies that, when pinning synchronization is guaranteed, the states of the nodes within such a pinning-controlled dynamical network may approach a specified trajectory which does not satisfy the system equation of the uncoupling individual node system within the network. The practical constraint that the external control inputs acting on the target system are unknown to any node in the considered network poses a big challenge in solving such a pinning synchronization problem. The designed scheme for achieving pinning synchronization is executed in two steps. Specifically, the first step is to select some nodes to pin such that the augmented interaction topology has at least one directed spanning tree rooted at the node describing the target system, while the second step is to construct a coupling law to synchronize all the states of nodes within the network. Moreover, two kinds of discontinuous coupling laws with static and adaptive coupling gains are, respectively, proposed to achieve pinning synchronization. Meanwhile, by utilizing nonsingular ${M}$ -matrix theory and Lyapunov stability analysis for nonsmooth system, some efficient criteria are established for guaranteeing synchronization in the pinning-controlled networks. Numerical simulations on pinning synchronization of networking Chua's circuit systems are finally given to verify the analytic results.

The finite-iteration tracking for singular coupled systems with packet-dropping learning controllers is discussed in this paper. The singular coupled systems are constructed to describe systems with some algebraic constraints, and iterative learning controllers are then designed to achieve the finite-iteration tracking of singular systems. The definition of the finite-iteration tracking is first proposed and the settling iteration is explicitly calculated. Moreover, the iterative learning controllers are designed to consider the case of packet losses. Simulation results are given to elaborate the correctness of the given theorems.

In this paper, a controller with multilayer structure is proposed to regulate the thermostatically controlled loads (TCLs), so that power sharing and comfort states consensus can be achieved. Since TCLs have great potential to reduce the fluctuations caused by photovoltaic in the building microgrid community, the control of a cluster of TCLs has practical significance. The multilayer structure can capture the nature of inner and inter communications among the building microgrids. The self-triggered mechanism is adopted to avoid continuous data transmission. The controller is validated through study of two different building microgrid communities with different number of TCLs.

Since their inception, a variety of auction designs have been utilized to increase the performance of electricity markets both within the academic literature and employed in real-world electricity markets. A feature most electricity market designs found in the literature have in common is the use of static sealed-bid auctions. Yet, consulting the auction theoretical literature shows that dynamic auctions utilizing revealed-bids could hold valuable benefits over static sealed-bid auctions, such as better price discovery, increased transparency and auction outcomes closer to equilibrium states. In this work, a descending-clock auction designed for power markets is proposed which employs revealed-bids. The model considers line capacity limits, generator capacity constraints as well as the power balance constraint and allows bidders to adapt future bids to these constraints. It is shown, that the proposed model encourages sincere bidding, which constitutes a weakly dominant strategy and - if followed by all bidders - constitutes an ex-post perfect equilibrium.

An increasing number of electric vehicles (EVs) make transition energy request from gasoline to electricity possible. As a result, the EVs play a new major role in the smart grid system. Along with the rapid development of energy storage technology, the battery stations constructued for EVs can also provide power to many other applications at lower cost, compared with the power generator, in peak load hours. To achieve such a goal, an efficient collaboration among EVs and battery stations is a new challenge. In this paper, the features of EV charging and battery charging/discharging problems are formulated using a bilevel programming model. The objective is to minimize the EV charging cost, with the maximal battery station operation revenue. The simulation shows the rescheduled charging activities can shift to avoid peak load while the peak load can be shaved by battery discharging.

Industrial applications are increasingly adopting conversational agents (chatbots) for tasks ranging from primitive conversation interfaces to intelligent human assistants. In this emerging field of research, there has been a strong drive towards modelling human-like characteristics and behaviors in chatbots. However, only a limited number of research endeavors have focused on a chatbot for automatic characterization of end-user emotions. In order to address this limitation, we propose an artificial intelligence based cognitive model for emotion awareness in chatbots using Markov chains, word embedding, and Natural Language Processing. The proposed model is able to extract emotions from conversations, detect emotion transitions over time, predict real-time emotions and intelligently profile human participants based on their distinct emotional characteristics. We conducted experiments using a real-world end-user dataset to demonstrate the functionality of the proposed model. Results from experiments confirm the plausibility of this model for emotion awareness in industrial conversational agents.

Recognition of complex human activities is a prominent area of research in intelligent video surveillance. The current state-of-the-art techniques are largely based on supervised deep learning algorithms. The inability to learn from unlabeled video streams is a key shortcoming in supervised techniques in most current applications where large volumes of unlabeled video data are utilized. Furthermore, the dominant focus on persistence in traditional machine learning algorithms has induced two limitations; the influence of outdated information in memory- guided decision making, and overfitting of acquired knowledge on specific past events, weakening the plasticity of the learning system. To address the above requirements, we propose a new adaptation of the Growing Self Organizing Map (GSOM), formed in a hierarchical two-stream learning pipeline to accommodate unlabeled video data for human activity recognition, which facilitates plasticity by implementing a transience property, without losing the stability of the learning system. The proposed model is evaluated using two benchmark video datasets, confirming its validity and usability for human activity recognition.

Macrotasking crowdsourcing systems (MCSs), such as Google Helpouts and Elance have emerged as an effective paradigm for improving human intelligence and activity to solve a wide variety of tasks. Requesters often post tasks to the MCS and competitive workers solve the tasks to earn the reward. However, rational and selfish workers in the MCS aim to strategically maximize their own benefit by exhibiting malicious behaviors, thereby decreasing the efficiency of systems. Herein, we present a novel game-theoretic mechanism to incentivize the competitive and selfish workers to provide high-quality solutions in the MCS. We first formulate the crowdsourcing problem as a multiplayer iterated game with incomplete information, where each worker has certain private information (such as solution quality), but does not know what other workers do. Subsequently, we propose an incentive mechanism in terms of zero-determinant (ZD) strategies aiming to improve the social welfare of the MCS, which serves to incentivize the competitive selfish workers toward high-quality solutions. Moreover, we find the conditions for reaching the maximum social welfare of the MCS. Numerical illustrations demonstrate a high and stable social welfare of the MCS with the proposed ZD strategies mechanism.

Anomaly detection for cloud servers is important for detecting zero-day attacks. However, it is very challenging due to the large amount of accumulated data. In this paper, a new mathematical model for modeling dynamic usage behavior and detecting anomalies is proposed. It is constructed using state summarization and a novel nested-arc hidden semi-Markov model (NAHSMM). State summarization is designed to extract usage behavior reflective states from a raw sequence. The NAHSMM is comprised of exterior and interior hidden Markov chains. The exterior controls the propagation of raw sequences of system calls and, conditional on it, the interior one controls the summarized observation process from the transition less usage behavior reflective states. An anomaly detection algorithm is derived by integrating state summarization and NAHSMM. During training the algorithm is assisted by a forensic module to tune the behavioral threshold. Experimental data is collected using IXIA Perfect Storm in conjunction with the commercial security-test hardware platform cyber range. To evaluate the reliability of the proposed model, first, its accuracy and training costs are compared with those of existing machine-learning models and then its scalability and resistance capabilities are tested. The results indicate that this model could be used as a method for detecting anomalies in cloud servers.

In this paper, we investigate the output containment control problem for a network of heterogeneous linear multiagent systems. The control target is to drive the outputs of the followers into the convex hull spanned by the leaders. To this end, we first derive a necessary condition imposed on both system dynamics and network topology from the viewpoint of internal model principle. Then, based on the necessary condition, we utilize a dynamic controller to drive the outputs of the leaders and followers to track the reference trajectories to achieve containment exponentially. We consider a general network topology which only contains a united spanning tree. Both fixed and dynamic network topologies are taken into consideration. Then, the optimal control problem for containment is further studied. An optimal control law is constructed from an algebraic Riccati equation, which is proved to be a stabilizing one as well. Finally, a reinforcement learning algorithm is introduced to solve the optimal control problem on line without the knowledge the system dynamics. Simulations are given at last to validate our theoretical findings.

In this paper, a distributed average tracking (DAT) problem is studied for Lipschitz-type of nonlinear dynamical systems. The objective is to design DAT algorithms for locally interactive agents to track the average of multiple reference signals. Here, in both dynamics of agents and reference signals, there is a nonlinear term satisfying a Lipschitz-type condition. Three types of DAT algorithms are designed. First, based on state-dependent-gain design principles, a robust DAT algorithm is developed for solving DAT problems without requiring the same initial condition. Second, by using a gain adaption scheme, an adaptive DAT algorithm is designed to remove the requirement that global information, such as the eigenvalue of the Laplacian and the Lipschitz constant, is known to all agents. Third, to reduce chattering and make the algorithms easier to implement, a couple of continuous DAT algorithms based on time-varying or time-invariant boundary layers are designed, respectively, as a continuous approximation of the aforementioned discontinuous DAT algorithms. Finally, some simulation examples are presented to verify the proposed DAT algorithms.

Matching power transfer between microgrids (MGs) enables maximum utilization of distributed energy resources (DERs), this paper proposes a cluster-oriented cooperative control strategy for multiple ac MG clusters, under which the power sharing among multiple MG clusters can be achieved by an intercluster scheme, whereas the frequency/voltage of all DERs within each MG cluster can also be regulated by an intracluster scheme. By pinning one or some cluster-head DERs from each MG cluster to constitute an intercluster communication network, the intercluster control layer can generate the frequency/voltage references based on the power mismatch among multiple MG clusters. In the intracluster control layer, the pinned DERs propagate these references to their neighbors in an MG cluster, and the frequency/voltage nominal set-points for each DER in the primary control process can be adjusted based on the frequency/voltage errors across the intracluster communication networks. Since the evolutions of intra- and intercluster dynamics may involve different time scales, the upper bound for the ratio of the associated intra- and intercluster time constants is finally derived to guarantee the stability of the whole multi-MG-cluster system. In special, both the intra- and intercluster controllers are designed based on their own sparse cyber networks, allowing different numbers of heterogeneous DERs in each MG cluster. The effectiveness of the control methodology is verified by the simulation of an ac multi-MG-cluster system in MATLAB/SimPowerSystems.

This paper develops a novel distributed iterative event-triggered control scheme for a master-slave-organized dc microgrid network with limited communication bandwidth. The proposed scheme can synchronize the voltage of multiple distributed energy resources (DER) to their desired value. Moreover, the optimal load sharing for their economic operation (e.g., minimize the total generation cost) can be achieved through a low bandwidth communication network. The designed controllers are fully distributed and only triggered at their own event time, which effectively reduces the frequency of controller updates compared with continuous-time feedback control. Eventually, each DER only requires the local voltage and current measurement from its own and some nearest (but not all) neighbors at given event-triggered time through limited-bandwidth communication links. The Lyapunov technique is employed to derive the event-triggered conditions that guarantee the stability. Furthermore, the lower bound of the interevent intervals is captured by the proposed iterative algorithm to exclude Zeno behaviors. Different cases in MATLAB/SimPowerSystems are investigated and results demonstrate the effectiveness and the performance of the proposed approach.

The safety and stability of modern power systems are undergoing various challenges, introduced by the integration of fluctuating renewable generation. In this paper, we present a hierarchical model predictive power dispatch and control strategy for a class of modern power systems with price-elastic controllable loads (CLs) in energy Internet. In the upper-level optimization, a generalized multiperiod economic dispatch (GMPED) problem is organized within an electricity market environment aiming at maximizing the social welfare. Specifically, the price-elastic CLs are aggregated in controllable load aggregators (CLAs) to participate in the market competition. A novel utility function of the price-elastic CLAs is proposed for market demand response. By solving GMPED, the power setpoints of plants over the further periods are produced, as well as the real-time price for the optimal response of price-elastic CLAs. In the second-level operation, two types of model predictive control-based controllers for both the supply and demand sides are designed for power tracking control by considering the model of the power system and aggregated thermostatically controlled loads. Finally, two case studies are performed on the IEEE 14- and 39-bus system, respectively, which shows that the system-frequency deviation and system cost are reduced significantly with the proposed methods.

Distribution networks with high penetration of distributed generation yield complicated and uncertain power flow, which makes most existing faulted line identification methods not adaptable for industrial applications. Driven by this motivation, a novel single-phase-to-ground (SPTG) faulted line identification method is proposed based on hybrid state estimator (HSE). The first step of the method is to present an HSE for power distribution networks using power flow measurements mixed phasor measurement units. Then, an SPTG fault on a power line is treated as an event that suddenly increases one virtual bus in the monitored network, so as to form the extended bus admittance matrix and augmented HSE based on the specific network topology. In this way, the faulted line identification could be obtained by computing parallel estimated results transversally. Robustness and effectiveness of the proposed HSE and the HSE-based SPTG faulted line identification method are validated by means of a cyber-physical system (a cosimulation platform), where two typical three-phase power distribution networks are considered to simulate with its hybrid measurement system.

Multi-modal journey planning, which allows multiple modes of transport to be used within a single trip, is becoming increasingly popular, due to a strong practical interest and an increasing availability of data. In real life situations, transport networks often involve uncertainty, and yet, most approaches assume a deterministic environment, making plans more prone to failures such as major delays in the arrival or waiting for a long time at stations. In this paper, we tackle the multi-objective stochastic journey planning problem in multi-modal transportation networks. The problem is modeled as a Markov decision process with two objective functions: expected arrival time and journey convenience. We develop a GA-based MDP solver as a baseline search method for producing optimal policies for traveling from a given origin to a given destination. Our empirical evaluation uses Melbourne transportation network using probabilistic density functions for estimated departure/arrival time of the trips. Numerical results suggest that the proposed method is effective for practical purposes and provide strong evidence in favor of switching from deterministic to non-deterministic planning.

An online estimation algorithm of State-of-Charge (SoC) of Lithium-ion (Li-ion) batteries based on terminal sliding mode (TSM) observer technique is proposed. A first-order RC equivalent circuit model is utilized to describe the dynamical behaviors of Li-ion batteries. A sliding mode observer is developed to track the states of the Li-ion batteries and the control signal of the observer is used to estimate the SoC of a Li-ion battery accurately. The proposed observer is robust to internal parameter uncertainties of the battery model, and the environment changes. Compared with the traditional sliding mode observers, the proposed sliding mode observer has the continuous control signal, which can be used for the SoC estimation algorithm directly. The proposed method has been verified by the estimation results and the effectiveness has benn demonstrated.

Multimodal sensory fusion is a fundamental requirement for autonomous robots to form an unambiguous and meaningful representation of their surroundings. In this paper, we propose a multisensory self-organizing neural architecture for multimodal fusion using unsupervised machine learning. Inspired by biological evidence from the organization of the human sensory system, the proposed architecture consists of self-organizing neural layers for learning individual modalities. We have incorporated scalable computing for self-organization, so the processing can be scaled to support large datasets and short computation times. The lateral associative connections capture the co-occurrence relationships across individual modalities for cross-modal fusion in obtaining a multimodal representation. Experiments are conducted on an audio-visual dataset consisting of utterances to evaluate the quality of multimodal fusion over individual unimodal representations. Multimodal representation achieves significant improvements over the unimodal representations. These results indicate the proposed architecture is capable of forming effective multimodal representations in short computation times from congruent multisensory stimuli.

This paper proposes a two-channel periodic event-triggered observed-based repetitive control strategy to track a periodic reference signal subject to limited communication capacity. Under the designed two-channel periodic event-triggering framework, within any two consecutive event-triggering instants in each event-triggering channel, not only the output measurements to the state observer but also the observer states to the repetitive controller structure are kept unchanged via a zero-order hold (ZOH) which can substantially alleviate the communication burden when the output measurements and the observer states do not change significantly. By employing the input delay approach, the overall system consisting of the physical plant, the state observer, the repetitive controller, and the two-channel periodic event-triggering mechanisms is modeled as a closed-loop time-varying delay system. Sufficient conditions in terms of linear matrix inequalities are derived to ensure the closed-loop system to be asymptotically stable with a prescribed H∞attenuation performance level for an exogenous disturbance input. The controller gains, observer gains, and the event-triggering parameters are synthesized by using a matrix decomposition technique. A numerical example is provided to evaluate the proposed design approach.

Driver behavior recognition is an essential determinant for effective coordination and communication between autonomous and human-driven vehicles. Effective coordination will ensure traffic flow optimization, collision avoidance and hazard detection. Humans exhibit a diverse array of driving behaviors and respond differently to task demands and capabilities of each driving situation. These behaviors can be captured from driving data generated by the vehicle, such as acceleration, brake position, steering wheel position, and transformed into driver behavior characterizations using unsupervised incremental machine learning. In this paper, we extend the Driver Demands and Capabilities Model for intelligent change detection. This model is implemented as an unsupervised incremental machine learning algorithm for behavior change detection, and differentiation between abrupt behavior change and repeating behavior change. Experiments were conducted using the first openly available dataset of annotated DAVIS driving recordings accompanying driving data. Results demonstrate and confirm the capabilities of the proposed model and algorithm for detecting driver behavior change, distinguishing between abrupt and repeat behavior changes. Detected changes can be communicated to all vehicles within the immediate vicinity for effective coordination and improved situational understanding.

In this note, the asymptotic consensus tracking problem is addressed for uncertain multi-agent systems (MASs) with undirected communication topologies and a high-dimensional leader, where the uncertainties may contain unmodeled dynamics and external disturbance which are prior unknown. To remove the effect of high-dimensional leader, an observer based compensation controller is firstly designed. A neural-adaptive based feedback controller is then designed. Note that the feedback term contains a discontinuous controller which is used to eliminate the effect of imprecise approximation of neural network. Furthermore, if the leader is assumed to be globally reachable, it is shown that asymptotic consensus tracking is achieved in MAS by choosing appropriate control parameters. The obtained theoretical result is finally validated by simulation.

This paper introduces a discrete time sliding mode by using a periodic intermittent control with multirate fast output feedback technique for the robust stabilization of discretized linear time invariant systems. In discrete time periodic intermittent control technique, the control input is applied to the system for the first few sampling time interval and then the control is not applied for next few sampling time interval, this control cycle is repeated. The proposed periodic intermittent control drives the system to discrete time sliding mode to achieve the robust stabilization of discretized linear time invariant system. The proposed theory is demonstrated by a simulation example on industrial plant emulator.

Demand response (DR) can provide a cost-effect approach for reducing peak loads while renewable energy sources (RES) can result in an environmental-friendly solution for solving the problem of power shortage. The increasingly integration of DR and renewable energy bring challenging issues for energy policy makers, and electricity market regulators in the main power grid. In this paper, a new two-stage stochastic game model is introduced to operate the electricity market, where Stochastic Stackelberg-Cournot-Nash (SSCN) equilibrium is applied to characterize the optimal energy bidding strategy of the forward market and the optimal energy trading strategy of the spot market. To obtain a SSCN equilibrium, sampling average approximation (SAA) technique is harnessed to address the stochastic game model in a distributed way. By this game model, the participation ratio of demand response can be significantly increased while the unreliability of power system caused by renewable energy resources can be considerably reduced. The effectiveness of proposed model is illustrated by extensive simulations.

This paper addresses the problem of finding the most synchrony vulnerable node in complex networks, i.e. the node which removal has the maximum influence on synchronizability of the network. In large-scale networks, brute search techniques are often not computationally cost effective in identifying the most vulnerable node(s). Here, considering the eigenratio of the Laplacian matrix of a graph as the synchronizability metric, we propose a measure in order to approximately rank nodes based on their impact on the synchronizability. This metric is cost effective since it needs a single eigen-decomposition of the Laplacian matrix of the connection graph. Simulation results show that the proposed metric is accurate enough in predicting the most vulnerable node in synthetic networks with scale-free, Watts-Strogatz and Erdös-Rényi structures.

This paper describes pinning synchronization of a complex dynamical network consisting of N identical nodes. The nodes are interconnected by a time-varying non-linear coupling terms, which has a general type with some constraints. Many non-linear coupling forms can be modeled as the one considered in this work. The network synchronization is achieved by using non-linear switching control. The stability of the synchronization is proven mathematically using Lyapunov analysis. It is shown that the proposed controller performs well in the presence of disturbances. Finally, simulation examples of Lorenz oscillator networks are given to verify the theoretical results. The simulations show that the proposed switching control outperforms classical linear control by providing not only faster synchronization, but also better robustness against external disturbances.

This paper studies a class of heterogeneous delayed impulsive neural networks with memristors and their collective evolution for multisynchronization. The multisynchronization represents a diversified collective behavior that is inspired by multitasking as well as observations of heterogeneity and hybridity arising from system models. In view of memristor, the memristor-based impulsive neural network is first represented by an impulsive differential inclusion. According to the memristive and impulsive mechanism, a fuzzy logic rule is introduced, and then, a new fuzzy hybrid impulsive and switching control method is presented correspondingly. It is shown that using the proposed fuzzy hybrid control scheme, multisynchronization of interconnected memristor-based impulsive neural networks can be guaranteed with a positive exponential convergence rate. The heterogeneity and hybridity in system models, thus, can be indicated by the obtained error thresholds that contribute to the multisynchronization. Numerical examples are presented and compared to demonstrate the effectiveness of the developed theoretical results.

This paper is concerned with the robust sliding mode control (SMC) problem for a class of T-S fuzzy systems subject to both matched and mismatched uncertainties. Different from the conventional T-S fuzzy SMC design approach, the quantized states rather than states themselves, are utilized for the control design. By the combination of the proposed zooming-out/zooming-in adjustment policy of the quantizer sensitivity, the quantized state feedback fuzzy sliding mode control scheme is developed to ensure the stabilization of the T-S fuzzy systems. Finally, some simulation results are presented to illustrate the effectiveness of the proposed approach.

Combined heat and power (CHP) is an important distributed generation type for the microgrids (MGs) with both thermal and electricity demand. In this paper, a multiparty energy management framework with electricity and heat demand response is proposed for the CHP-MG. First, in order to decide the electricity and thermal prices, an optimization profit model of a microgrid operator (MGO) is formulated including the cost of gas, the income of energy sold to the consumers, and the income of surplus electricity feed to the utility grid. The CHP system is operated in a hybrid mode by dynamically selecting the following-thermal-load mode and the following-electric-load mode. Moreover, for the building energy consumers, an optimization model is formulated containing the utility of electricity consumption, the expenditure of purchasing electricity/heat, and the comfortable degree of indoor temperature. The trading process between the MGO and consumers is designed as a one-leader$N$-follower Stackelberg game, and the existence and uniqueness of the Stackelberg equilibrium is proved. Finally, the case study of a CHP-MG system containing six building users is provided to show the effectiveness of the proposed method.

This paper proposes a distributed hierarchical cooperative control strategy for a cluster of islanded microgrids (MGs) with intermittent communication, which can regulate the frequency/voltage of all distributed generators (DGs) within each MG as well as ensure the active/reactive power sharing among MGs. A droop-based distributed secondary control scheme and a distributed tertiary control scheme are presented based on the iterative learning mechanics, by which the control inputs are merely updated at the end of each round of iteration, and thus, each DG only needs to share information with its neighbors intermittently in a low-bandwidth communication manner. A two-layer sparse communication network is modeled by pinning one or some DGs (pinned DGs) from the lower network of each MG to constitute an upper network. Under this control framework, the tertiary level generates the frequency/voltage references based on the active/reactive power mismatch among MGs while the pinned DGs propagate these references to their neighbors in the secondary level, and the frequency/voltage nominal set points for each DG in the primary level can be finally adjusted based on the frequency/voltage errors. Stability analysis of the two-layer control system is given, and sufficient conditions on the upper bound of the sampling period ratio of the tertiary layer to the secondary layer are also derived. The proposed controllers are distributed, and thus, allow different numbers of heterogeneous DGs in each MG. The effectiveness of the proposed control methodology is verified by the simulation of an ac MG cluster in Simulink/SimPower Systems.

The economic dispatch problem is investigated in this paper for a class of smart grids subject to unknown communication uncertainties. Compared with existing works related to economic dispatch where the dispatch algorithms are carried out by a centralized controller, a new kind of distributed dispatch algorithms are developed to achieve optimal dispatch of electric power by appropriately sharing the load among different generating units while guaranteeing consensus among incremental costs. An adaptive weight-adjustment technique is suggested that enables the dispatch algorithms to choose the communication weights among neighboring generating units which yield consensus of incremental costs under both cases with or without capacity limitations. The achievement of such a consensus leads to optimal dispatch of electronic power and secures the system performance against unknown communication uncertainties. Meanwhile, it is proved that the power demand and supply of the considered smart grids will be kept in a balanced state during the dispatch process. The interesting issue of how to assign the power outputs among generating units to balance the power demand and supply of the considered smart grids is also addressed. Finally, the numerical results of several case studies have been provided to verify the effectiveness of the proposed algorithms.

The resource allocation problem is studied and reformulated by a distributed interior point method via a -logarithmic barrier. By the facilitation of the graph Laplacian, a fully distributed continuous-time multiagent system is developed for solving the problem. Specifically, to avoid high singularity of the -logarithmic barrier at boundary, an adaptive parameter switching strategy is introduced into this dynamical multiagent system. The convergence rate of the distributed algorithm is obtained. Moreover, a novel distributed primal-dual dynamical multiagent system is designed in a smart grid scenario to seek the saddle point of dynamical economic dispatch, which coincides with the optimal solution. The dual decomposition technique is applied to transform the optimization problem into easily solvable resource allocation subproblems with local inequality constraints. The good performance of the new dynamical systems is, respectively, verified by a numerical example and the IEEE six-bus test system-based simulations.

In this brief, sustaining and intermittent run-to-run controllers are designed to achieve the stability of singular discrete-time neural networks with state-dependent coefficients. The controllers are designed for two reasons: 1) it is very difficult and almost impossible to only measure the in situ feedback information for the controllers and 2) the controllers may not always exist at any time. The stability is then established for singular discrete-time neural networks with state-dependent coefficients. Finally, numerical simulations are shown to illustrate the usefulness of the obtained criteria.

This paper proposes a sampled data-based modeling approach for complex network systems. A compression method, known as characteristic modeling, is used to construct microscopic models from macroscopic observations. Based on this model, a novel control method is also developed to achieve network synchronization. The proposed approach can reduce both the complexity of microscopic dynamics and overall networks. Its application to pinning control design validates the effectiveness of this approach.

Weighted centroid localization (WCL) based on received signal strength (RSS) measurements is an attractive low-complexity solution that enables cognitive radios (CRs) to have a geolocation awareness of the radio environment. In this paper, we propose a new analytical framework to accurately calculate the performance of WCL based on the statistical distribution of the ratio of two quadratic forms in normal variables. In particular, we derive an exact expression for the cumulative distribution function (CDF) of the two-dimensional location estimation in the presence of independent and identically distributed (i.i.d.) as well as correlated shadowing. Numerical results confirm that the analytical framework is able to predict the performance of WCL capturing all the essential aspects of propagation as well as CR network spatial topology.

The second-order Kuramoto oscillator models (KM) with diffusive and sinusoidal coupling were considered intensively in power networks. In this paper, the synchronization of an extended second-order Kuramoto oscillator model is studied using a parameterized approach. It is shown that the synchronization of the extended second-order KM is equivalent to the synchronization of a simple extended first-order KM. Sufficient conditions for synchronizability are established for mean-field type couplings. Simulation studies in oscillator networks as well as in power networks with transmission losses are given to illustrate the effectiveness of the theoretical results.

In this paper, the stability problem of formation of multi-agents subject to failures in their communication links is addressed. The objective of the formation control problem is to maintain the inter-agent distances to be constants over time using a distributed control algorithm implemented in each agent. Previous research results showed that a distributed gradient-based control can locally asymptotically stabilize an undirected formation. However, in the case of failures in the inter-agent communication network, the degrees of freedom for some nodes might become uncontrollable and, consequently, the formation starts deviating from the desired conditions due to uncertainties and noise. In this paper, it is proved that in a faulty formation system, if there still exists a path between the agents on the both sides of the failed link, the gradient-based control signal can recover the formation without adding any new link to the network. Based on this feature, an algorithm for recovering the formation from the fault is developed. Simulation results show that the proposed recovery algorithm can tolerate small values of delays in data communications.

In this paper, we propose an optimization methodology to find the optimal topology for the data communication network in distributed frequency control of power system. In order to implement a distributed cooperative control scheme, local controllers often share their data over a data communication network. Structure of this network has a major role in determining stability of secondary cooperative frequency control of a microgrid; and thus the structure can be optimized to have the best performance. Although distributed control signals may be delayed or dropped during their transmission, confident margin of the stability can reduce side effects of these inherent problems and makes power system more reliable. In this situation, the challenge is to find the best topology of data communication network giving the highest margin of the system stability. In this paper we define the problem of finding the best topology as an optimization problem. An eigenvalue perturbation analysis approach is used to approximate sensitivity of the stability performance of secondary cooperative control to adding/removing data communication links. Then, the optimization problem is solved using a simulated annealing optimization strategy. Our numerical simulations on sample networks show that the proposed rewiring-based optimization can successfully find the network structure with (near)-optimal stabilizability performance.

Cascading failure in power grids might lead to a catastrophic black out in power systems. One resolution to deter the power grid from failure is load shedding. There are different types of load shedding in the literature which mostly cut off some loads from the grid to preserve the rest of the network's connectivity and functionality. However sometimes it is not the case and disconnecting some feeders from the power grid is impractical due to their vitality for the economy or the society. In this paper, as soon as a transmission line gets overloaded due to any rise in overall load, since its breakdown can trigger a cascade of failures, we propose a method to rank the loads to be reduced in order to prevent that particular line from failure. After the loads are ranked, the top ranked load will be a candidate to get reduced. If this action wasn't enough or feasible, the second top ranked load is chosen and so on. Even a set of loads with different percentage of load reduction can be picked. As the pre-failure data, we apply the results from load flow analysis or the last working state of the power grid. These data is applied to calculate the specific transmission line's sensitivity to changes in different loads in power grids. The results show that this method is much more efficient when the classic methods suffer from divergence and setting malfunctioning.

The proliferation of Internets of Things (IoT) technologies in both industrial and non-industrial settings has led to the accumulation of Big Data sets. Analysis of these high-volume, high-velocity datasets require advanced processing techniques that incorporate parallel and distributed computations. In this paper, we present a novel distributed self-adaptive neural-network algorithm, the Distributed Growing Self-Organizing Map (DGSOM) algorithm to address the growing need for unsupervised machine learning of Big Data sets on distributed computing environments. The algorithm was tested on a Big Data set of sensor recordings of human activity collected from wearable devices, 2.8 million records. Results indicate that the distributed algorithm significantly reduces execution time compared to its serial counterpart. Moreover, the self-adaptive nature and controlled growth of the algorithm demonstrates data-driven structure adaptation and multi-granular pattern analysis. Overall, the proposed algorithm addresses the need for pattern discovery and visualization from Big Data sets generated by IoT devices which are increasingly commonplace in industrial scenarios.

This paper develops a distributed cooperative control scheme for DC microgrids with time-varying coupling delays in noisy environments. The proposed distributed cooperative control, consisting of distributed primary control and secondary control, will regulate the voltage of microgrids to the desired values and through a sparse communication network with asymmetric communication delays and noise disturbances. Distributed cooperative controllers are designed into the secondary control stage for DC microgrids, in which manner the criteria for the stability analysis and delays boundedness to maintain the system stable are derived. With the proposed algorithms, control derivation of voltage produced during the primary control stage can be well remedied even if the communication delays and noise disturbances may exist. The effectiveness of the proposed control methodology is verified by the simulation of a DC microgrid system in MATLAB/SimPowerSystems.

In this paper, a framework of convex optimization algorithm with a fixed time convergence rate is investigated. Given a strongly convex optimization problem, two control algorithms are developed to solve the problem within a fixed time of which the upper bound is theoretically obtained. Moreover, the fixed time convergence rate based algorithms are extended into the distributed manner which is applied to two typical distributed optimization problems including the resource allocation problem and the coordination optimization problem. Laplacian graph matrix is employed to the weighted gradient based and the coordination based distributed optimization algorithms. By developing the characteristic of the objective function, the upper bound of the fixed time convergence is derived. Two numerical examples are given to verify the main results.

There has been a desirable trend in recent years towards Electric Vehicles (EVs) contributing less air pollution and noise pollution than internal combustion engine vehicle. EV charging/discharging problem brings a new challenge to the power operation and control. In this paper, the charging/discharging problem is modelled by noncooperative game theory, the payoff function of this model not only maximizes the revenue of discharging activity, but also minimizes overall generation cost by decreasing electricity price on peak hours. The result of simulation illustrates that charging behaviors can shift the charging demand from peak hours to off-peak hours, while discharging behaviors can shave the peak loads in the parking periods.

This paper presents the cascaded PI-continuous second order sliding mode control for induction motor in the presence of operational constraints. The inner-loop Sliding Mode Control (SMC) is designed to control the current dynamics of the motor while the outer-loop control is the PI control of speed. The main advantages of the proposed method are that the PI control provides reference to inner-loop SMC with constraints according to the system requirements in terms of maximum current and speed limits. Moreover the inner-loop dynamics of the motor being more non-linear, SMC design has more importance in terms of robustness and disturbance rejection capability. The proposed cascade PI with SMC is chattering-free control action with fixed-time convergence. The performance of the developed controller is validated and compared by carrying out real-time experimental studies. Experimental results demonstrate remarkable robust tracking performance of the controller in terms of transient response speed and steady-state accuracy.

IoT systems deployed in industrial and smart factory settings generate large volumes of data at high velocity. Context awareness is mandatory for knowledge discovery and actionable insights from such high-velocity, high-volume IoT data streams. Changes to the context of a data stream are represented in the underlying data distribution. Research in concept drift aims to detect and adapt to such changes in a data distribution. Concept drift detection can be extended to suit ad hoc Big Data streams generated by IoT systems, by introducing the cognitive principles of learning. This paper proposes an unsupervised incremental learning algorithm for detection and adaption of concept drift based on the cognitive principles of learning. It executes in automated time windows, detects concept drift using movement in space and determines the type of concept drift using movement in time. The algorithm was applied to a Big Data set representing an IoT system for urban vehicular movement and traffic. Results confirm that the proposed algorithm generates context-awareness by detection and adaptation to concept drift in high-volume, high-velocity IoT systems.

The world is witnessing a remarkable increase in the usage of video surveillance systems. Besides fulfilling an imperative security and safety purpose, it also contributes towards operations monitoring, hazard detection and facility management in industry/smart factory settings. Most existing surveillance techniques use hand-crafted features analyzed using standard machine learning pipelines for action recognition and event detection. A key shortcoming of such techniques is the inability to learn from unlabeled video streams. The entire video stream is unlabeled when the requirement is to detect irregular, unforeseen and abnormal behaviors, anomalies. Recent developments in intelligent high-level video analysis have been successful in identifying individual elements in a video frame. However, the detection of anomalies in an entire video feed requires incremental and unsupervised machine learning. This paper presents a novel approach that incorporates high-level video analysis outcomes with incremental knowledge acquisition and self-learning for autonomous video surveillance. The proposed approach is capable of detecting changes that occur over time and separating irregularities from re-occurrences, without the prerequisite of a labeled dataset. We demonstrate the proposed approach using a benchmark video dataset and the results confirm its validity and usability for autonomous video surveillance.

A distributed control law based on Model predictive Control (MPC) scheme is proposed for secondary frequency control in a deregulated market, which utilizes Demand Response (DR) along with Automatic generation Control (AGC). The proposed strategy of combining DR and AGC is termed as Load frequency Control (LFC) in the paper. The main contribution of the paper focuses on developing a model for LFC, which combines DR as well as Governor Response (GR) as manipulated variables. The new model is then used in an embedded integrator based distributed MPC algorithm to optimally choose between the GR and DR for the frequency regulation within system's constraints and cost. The algorithm is tested on a system with two areas interconnected by means of a tie line and shows that by choosing DR the frequency response not only improves but also the cost of frequency regulation reduces.

Distributed Generation (DG) is a sustainable alternative energy paradigm that allows flexible customer-participated demand response management, however when coupled with battery storage in a carbon costed policy setting true reduction of greenhouse gas emissions may not necessarily be rewarded. This paper examines the role of policy settings using an established multi-agent simulation framework that captures emerging complex responses that originate from individual household energy use behaviors. Case studies demonstrate with uninformed policy settings being chosen, undesirable over-generation may cause technical issues with unwanted energy profile responses as well as undesirable over-investment in the wrong electricity assets may cause increased electricity costs for households.

Distributed practical node-to-node state consensus problem is studied in this paper for a class of two-layer multi-agent systems. It is supposed that there are two layers, i.e., the leaders' layer and followers' layer, in the considered multi-agent systems. Unlike most existing results on distributed consensus of multi-agent systems, the control objective in this paper is to make the states of each follower located on followers' layer track those of its corresponding leader located on leaders' layer. Furthermore, the network topologies of the leaders and the followers may be heterogeneous. Based on the assumption that the states of leaders are uniformly bounded, some sufficient criteria for node-to-node practical consensus are obtained by using differential equation theory.

Recently, the state estimation issue of a TCP/IP network has attracted much attention from different communities. In this paper, a terminal sliding-mode observer (TSMO) is proposed based on a fluid-flow model of a TCP/IP network to estimate traffic flow states. A novel control strategy is proposed to fasten the convergence of the estimation error for average congestion window (ACwnd). Furthermore, a continuous control strategy is directly used to estimates the flooding rate of additional traffic flow (ATF). The efficacy of the proposed TSMO is verified by a numerical simulation implementations via the networking simulator NS-2.

This paper proposes a full-order terminal sliding-mode based energy saving control for the grid-connected converter of induction motor systems. DC-link voltage and currents controllers of the converter are designed respectively using the full-order terminal sliding manifold and the related control strategies. The chattering in the sliding-mode control is attenuated, and continuous control signals are obtained. The energy-saving operation of the induction motor systems is achieved because the regenerative energy of the induction motor is fed back to the power grid with unity power factor. The simulation results validate the proposed method.

This paper proposes a gain margin technique based continuous sliding-mode control method for position servo systems of induction motors. A systematic design method for position and current controllers is developed to implement the robust control of induction motors in the field orientation control system. In the proposed controllers design, the boundaries of disturbances and parameter perturbations in the control system can be determined in advance based on the upper-bounds of uncertainties in the induction motor model. The gain margin technique is utilized to regulate the gain of the switching control. The chattering phenomenon are attenuated by using a continuous sliding-mode control method, so continuous control signals can be generated and directly applied in servo systems of induction motors. The simulation results show that the proposed method can implement high-performance position control of induction motors with improvement of steady-state and dynamic performances, control precision and robustness.

Smart grids (SGs) are electric networks that use innovative and intelligent monitoring, control, communication, and self-healing technologies to deliver better connections and operations for generators and distributors, flexible choices for prosumers, and reliability and security of electricity supply. SGs are a complex cyber-physical system by their very nature, and this has impacted the way energy is generated, transported and used. In our 2016 paper [1], we examined the SG concept in the context of cyber-physical systems (CPSs), and outlined the challenges ahead alongside with fast development of advanced technologies such as Internet of Things, cloud computing, big data and complex networks.

This paper investigates the finite-time control for robust tracking consensus problems of multiagent systems with an uncertain leader for situations where the state of the considered active leader may not be measured and the directed network topology is time-varying. Based on the neighbor-based state-estimation rule and a new Lyapunov stability analysis method, a continuous and nonlinear distributed tracking protocol using only relative position information is designed, under which each agent can follow the leader in finite time if the input (acceleration) of the leader is known, and the tracking errors can converge to a bounded region in finite time if the input of the leader is unknown. In particular, a special continuous distributed tracking protocol with bounded control inputs is introduced to track the active leader in finite time. Numerical simulations are also given to illustrate the effectiveness of the theoretic results.

This paper is concerned with the distributed consensus tracking problem of uncertain multiagent systems with directed communication topology and a single high-dimensional leader. Compared with existing related works, the dynamics of each follower in the present framework are subject to unmodeled dynamics and unknown external disturbances, which is more practical in various applications. Furthermore, the dimensions of leader's dynamics may be different with those of the followers' dynamics. Under the mild assumption that each follower can directly or indirectly sense the output information of the leader, a distributed robust adaptive neural network controller together with a local observer are designed to each follower to ensure that the states of each follower ultimately synchronize to the leader's output with bounded residual errors under a fixed topology. By appropriately constructing some multiple Lyapunov functions, the derived results are further extended to consensus tracking with switching directed communication topologies. The effectiveness of the analytical results is demonstrated via numerical simulations.

In this paper, the new decoupled distributed sliding-mode control (DSMC) is first proposed for second-order consensus in multiagent systems, which finally solves the fundamental unknown problem for sliding-mode control (SMC) design of coupled networked systems. A distributed full-order sliding-mode surface is designed based on the homogeneity with dilation for reaching second-order consensus in multiagent systems, under which the sliding-mode states are decoupled. Then, the SMC is applied to the decoupled sliding-mode states to reach their origin in finite time, which is the sliding-mode surface. The states of agents can first reach the designed sliding-mode surface in finite time and then move to the second-order consensus state along the surface in finite time as well. The DSMC designed in this paper can eliminate the influence of singularity problems and weaken the influence of chattering, which is still very difficult in the SMC systems. In addition, DSMC proposes a general decoupling framework for designing SMC in networked multiagent systems. Simulations are presented to verify the theoretical results in this paper.

The classical power allocation/reallocation faces difficult challenges in a future power grid with a great many distributed generators and fast power fluctuations caused by high percentage of renewable energy. To perform power reallocation fast in a future power grid with a large number of participants and disturbances, a hierarchical distributed scheme based on a partition framework is proposed. In the proposed scheme, the power grid is naturally partitioned into a certain number of regions, and the total energy demand in the power grid with disturbances is automatically estimated rather than given in advance. Besides, the centralized local optimizations in regions and the distributed global optimization among regions are coupled to solve the power reallocation problem, in which each region performs as a single agent. Thus, the agents in the proposed scheme are much fewer than the purely distributed ones, hence the communication load is greatly relieved and the reallocation process is significantly simplified. Effectiveness of the proposed scheme is verified by the cases.

Multienergy microgrids are a promising solution to improve overall energy (electricity, cooling, heating, etc.) efficiency. In this paper, a new optimal energy trading strategy is developed considering the risk from uncertain energy supply and demand in a set of individual multienergy microgrids. According to the historical data about energy supply of each microgrid, an aggregator aims to maximize each microgrid's profit while minimizing the risk of overbidding for renewable energy resources trading based microgrids. A novel two-stage stochastic game model with Cournot Nash pricing mechanism and the conditional value-at-risk criterion is proposed to characterize the payoff function of each microgrid. The sample average approximation (SAA) technique is employed to approximate the stochastic Nash equilibrium of the game model. The existence of the SAA Nash equilibrium is investigated and the corresponding Nash equilibrium seeking algorithm is also realized in a distributed manner. The proposed method is validated by numerical simulations on real-world data collected in Australia, and the results show that the SAA Nash equilibrium based strategy can effectively reduce the risk of not meeting the demand and improve the economic benefits for each microgrid.

For microgrids with photovoltaic (PV) prosumers, the effective energy sharing management (ESM) is important for the operation. In this paper, a Stackelberg game approach for ESM is proposed. First, according to feed-in-tariff of PV energy, a system model of ESM is introduced, which includes the profit model of microgrid operator (MGO) and the utility model of PV prosumers. Moreover, an hour-ahead optimal pricing model of ESM is proposed. The model is designed based on Stackelberg game, where the MGO acts as the leader and all participating prosumers are considered as the followers. With the proof of equilibrium and uniqueness of the Stackelberg equilibrium, the MGO is obligated to coordinate the sharing of PV energy with maximization of the own profit, while the prosumers are autonomous to maximize their utilities with demand response availability. Finally, a billing mechanism is designed to deal with the uncertainty of PV energy and load consumption. By using the collected data from realistic PV-roofed buildings, the effectiveness of the model is verified in terms of the profit of MGO, the utilities of prosumers, and the net energy of the microgrid.

This paper presents a robust distributed secondary control (DSC) scheme for inverter-based microgrids (MGs) in a distribution sparse network with uncertain communication links. By using the iterative learning mechanics, two discrete-time DSC controllers are designed, which enable all the distributed energy resources (DERs) in an MG to achieve the voltage/frequency restoration and active power sharing accuracy, respectively. In special, the secondary control inputs are merely updated at the end of each round of iteration, and thus, each DER only needs to share information with its neighbors intermittently in a low-bandwidth communication manner. This way, the communication costs are greatly reduced, and some sufficient conditions on the system stability and robustness to the uncertainties are also derived by using the tools of Lyapunov stability theory, algebraic graph theory, and matrix inequality theory. The proposed controllers are implemented on local DERs, and thus, no central controller is required. Moreover, the desired control objective can also be guaranteed even if all DERs are subject to internal uncertainties and external noises including initial voltage and/or frequency resetting errors and measurement disturbances, which then improves the system reliability and robustness. The effectiveness of the proposed DSC scheme is verified by the simulation of an islanded MG in MATLAB/SimPowerSystems.

This brief investigates the quantized iterative learning problem for digital networks with time-varying topologies. The information is first encoded as symbolic data and then transmitted. After the data are received, a decoder is used by the receiver to get an estimate of the sender's state. Iterative learning quantized communication is considered in the process of encoding and decoding. A sufficient condition is then presented to achieve the consensus tracking problem in a finite interval using the quantized iterative learning controllers. Finally, simulation results are given to illustrate the usefulness of the developed criterion.

In this paper we propose a novel algorithm for mining contrast patterns using a distributed, map-reduce like framework. Contrast patterns describe differences between contrasted data sets and have previously been used for building highly accurate classifiers. However, mining for contrast patterns is a computationally expensive task and existing algorithms are designed to run in a sequential manner on a single machine. Consequently, existing approaches are unable to handle dense, high volume and high dimensional databases. Our algorithm addresses this problem by partitioning the search-space for contrast patterns into small, independent units. These units can be mined in parallel, providing a scalable solution for mining large data sets. Using three different real-world data sets we test an implementation of our algorithm on a Spark cluster. Results of these tests indicate that our algorithm achieves a high-degree of parallelism and scalability.

In this paper, the robust fixed-time consensus problem for multiagent systems with nonlinear dynamics and uncertain disturbances under a weighted undirected topology is investigated. Some nonlinear control protocols are proposed under which fixed-time consensus in the considered multiagent systems can be ensured. Compared with the initial-condition based finite-time consensus, it is theoretically shown that any prescribed convergence time for the achievement of consensus can be guaranteed within fixed time regardless of the initial conditions. Furthermore, the achievement of consensus is shown to be robust against bounded uncertain disturbances affecting the agents. Finally, some numerical examples are provided to illustrate the performance and effectiveness of the theoretical results.

This paper proposes a control framework, called pulse-modulated intermittent control, which unifies impulsive control and sampled control. Specifically, the concept of pulse function is introduced to characterize the control/rest intervals and the amplitude of the control. By choosing some specified functions as the pulse function, the proposed control scheme can be reduced to sampled control or impulsive control. The proposed control framework is applied to consensus problems of multiagent systems. Using discretization approaches and stability theory, several necessary and sufficient conditions are established to ensure the consensus of the controlled system. The results show that consensus depends not only on the network topology, the sampling period and the control gains, but also the pulse function. Moreover, a lower bound of the asymptotic convergence factor is derived as well. For a given pulse function and an undirected graph, an optimal control gain is designed to achieve the fastest convergence. In addition, impulsive control and sampled control are revisited in the proposed control framework. Finally, some numerical examples are given to verify the effectiveness of theoretical results.

This paper investigates the fuzzy control issue for uncertain active suspension systems via dynamic sliding-mode method. The Takagi-Sugeno fuzzy approach is adopted on the background of the varying masses to describe the prescribed nonlinear system in order to achieve the design targets via the method of sector nonlinearity. This paper employs the dynamic sliding-mode scheme to control nonlinear active suspension systems. In the proposed sliding-mode control scheme, the sliding surface function is formed linearly with the system states and control inputs. Then, a fuzzy dynamic term is utilized to construct the sliding-mode feedback controller. In existing results, the sliding mode is achieved and maintained with no consideration of the system perturbations. Thus, sufficient conditions are proposed to make the sliding surface reachable with the existence of the system perturbations to make the augmented system stable. Finally, simulation results are presented to verify the effectiveness of the proposed schemes.

This paper deals with a consensus tracking problem for multiagent systems (MASs) with Lipschitz-type nonlinear dynamics and directed switching topology. Unlike most existing works where the relative full state measurements of neighboring agents are utilized, it is assumed that only the relative output measurements of neighboring agents are available for coordination. To achieve consensus tracking in the considered MASs, a new class of observer-based protocols is proposed. By appropriately constructing some topology-dependent multiple Lyapunov functions, it is theoretically shown that distributed consensus tracking in the closed-loop MASs equipped with the designed protocols can be ensured if each possible topology contains a directed spanning tree rooted at the leader and the dwell time for the switchings among different topology is less than a derived positive quantity. Interestingly, it is found that the communication topology for observers' states may be independent with that of the feedback signals. The derived results are further extended to the case of directed switching topology with only average dwell time constraints. Finally, the effectiveness of the analytical results is demonstrated via numerical simulations.

In the above paper[1], there are errors regarding the description of(4), and misquotes in Algorithm 1 and 2. In Algorithm 1, the first equation referenced should be (5) and not (40). In Algorithm 2, the first equation referenced should be (40) and not (5). The correction for(4)is as follows:\begin{equation*} \mathcal {L}^{(\sigma (t))}=\left [{\begin{array}{cc} \widetilde {\mathcal {L}}^{(\sigma (t))}& \mathrm {a}^{(\sigma (t))}\\ \mathrm {0}_{N}^{T}& 0 \end{array}}\right ] \tag{4}\end{equation*}where$\widetilde {\mathcal {L}}^{(\sigma (t))}\in \mathbb {R}^{N\times N}$,$\mathrm {a}^{(\sigma (t))}=-[a_{1(N+1)}^{(\sigma (t))},a_{2(N+1)}^{(\sigma (t))},\cdots ,~a_{N(N+1)}^{(\sigma (t))}]^{T}\in \mathbb {R}^{N}$, and$\mathcal {A}^{(\sigma (t))} = [a_{ij}^{(\sigma (t))}]_{(N+1) \times (N+1)}$is the adjacency matrix of$\mathcal {G}^{(\sigma (t))}$.

In this paper we study stability of a power system consisting distributed generation units. A complete model for the power grid including dynamics of loads as well as interchange of power is taken into account. A cooperative secondary control scheme takes into account internal dynamic of each node and is implemented to improve the stability of the system. In this paper, a condition for the stability of power grid in the presence of communication network is achieved. We support the results by numerical simulations on various connection topologies including scale-free and random structures. We find that increasing the number of communication links does not generally improve the stability of the whole power network.

Power networks can be modeled as networked structures with nodes representing the bus bars (connected to generator, loads and transformers) and links representing the transmission lines. In this manuscript we study cascaded failures in power networks. As network structures we consider IEEE 118 bus network and a random spatial model network with similar properties to IEEE 118 bus network. A maximum flow based model is used to find the central edges. We study cascaded failures triggered by both random and targeted attacks to the edges. In the targeted attack the edge with the maximum centrality value is disconnected from the network. A number of metrics including the size of the largest connected component, the number of failed edges, the average maximum flow and the global efficiency are studied as a function of capacity parameter (edge critical load is proportional to its capacity parameter and nominal centrality value). For each case we identify the critical capacity parameter by which the network shows resilient behavior against failures. The experiments show that one should further protect the network for a targeted attack as compared to a random failure.

This manuscript discusses a new algorithm for finite time convergence of the states for a higher order system. To this end, a set of fast terminal sliding mode surfaces are constructed. The fractional powers of the switching surfaces are selected such that singularity is avoided during sliding. A control signal is developed with necessary and sufficient conditions for the finite time convergence is obtained, under which the system states reach the fast terminal sliding surfaces in a finite time and stay in the sliding modes thereafter. A condition on external disturbance is obtained to guarantee the finite time convergence. Numerical simulations are provided to validate the theoretical results and examine the robustness of control against external disturbances.

As the world advances into the information age, the proliferating demand for energy entails an increasing need for effective smart energy management systems. The resulting smart grids and smart energy management solutions are beginning to generate Big Data; high-variety data at cumulative volumes and velocity. Simultaneously, data analytics techniques and methodologies are being introduced to comprehend this changing nature of data. A number of conventional data mining techniques have successfully transitioned into the Big Data landscape. However, integration of multi-source information in this landscape remains hitherto unaddressed. In this paper, we present a novel data fusion technique that incrementally integrates information from multiple sources. Based on an incremental, unsupervised learning algorithm and possibilistic fusion, the technique overcomes limitations to continuous learning and integrating information of mixed granularity. The technique is also extensible into the Big Data landscape. The collective effect of these features postulate smart energy management as a fitting application domain for the proposed technique. We demonstrate practical applicability of the technique using a household energy and utility consumption dataset. Results and analytics outcomes from these experiments confirm its effectiveness as a data fusion technique and its extensibility into further high-volume applications in smart energy management.

In this paper we introduce a novel population-based binary optimization technique, which works based on consensus of interacting multi-agent systems. The agents, each associated with an opinion vector, are connected through a network. They can influence each other, and thus their opinions can be updated. The agents work collectively with their neighbors to solve an optimization task. Here we consider a specific opinion update rule and various topologies for the connection network. Our experiments on a number of benchmark non-convex cost functions show that ring topology results in the best performance as compared to others. We also compare the performance of the proposed method with a number of well-known optimizers (genetic algorithms, binary particle swarm optimizer, and binary differential evolution) and show its outperformance over them. The proposed optimizer also shows rather fast convergence to the optimal solution.

Smart grids are electric networks that employ advanced monitoring, control, and communication technologies to deliver reliable and secure energy supply, enhance operation efficiency for generators and distributors, and provide flexible choices for prosumers. Smart grids are a combination of complex physical network systems and cyber systems that face many technological challenges. In this paper, we will first present an overview of these challenges in the context of cyber-physical systems. We will then outline potential contributions that cyber-physical systems can make to smart grids, as well as the challenges that smart grids present to cyber-physical systems. Finally, implications of current technological advances to smart grids are outlined.

This paper studies the aperiodic sampled-data control for the sliding-mode control (SMC) scheme of fuzzy systems with communication-induced delays via the event-triggered method. In practice, it is impossible to update control in continuous manner; thus, an event-based control technique has become popular with the advantage that the control task is executed only if it is triggered by an event. In this paper, the event-based sliding-mode control (ESMC) is designed for each linear subsystem of the global fuzzy model first. Then, the conditions for “fuzzily” amalgamated ESMC are discussed to stabilize the global fuzzy model. This ensures that the SMC is executed only when necessary. Furthermore, the results are extended to the fuzzy systems with communication-induced delays. Finally, case studies are carried out to demonstrate the effectiveness of the derived results.

Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social, and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics, which deals with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interests. Furthermore, the paper highlights challenges as well as opportunities arising due to the advent of big data and the increasing popularity of cloud environments.

To reduce information exchange requirements in smart grids, an event-triggered communication-based distributed optimization is proposed for economic dispatch. In this work, the θ-logarithmic barrier-based method is employed to reformulate the economic dispatch problem, and the consensus-based approach is considered for developing fully distributed technology-enabled algorithms. Specifically, a novel distributed algorithm utilizes the minimum connected dominating set (CDS), which efficiently allocates the task of balancing supply and demand for the entire power network at the beginning of economic dispatch. Further, an event-triggered communication-based method for the incremental cost of each generator is able to reach a consensus, coinciding with the global optimality of the objective function. In addition, a fast gradient-based distributed optimization method is also designed to accelerate the convergence rate of the event-triggered distributed optimization. Simulations based on the IEEE 57-bus test system demonstrate the effectiveness and good performance of proposed algorithms.

Networked control systems (NCSs) are systems whose control loops are closed through communication networks such that both control signals and feedback signals can be exchanged among system components (sensors, controllers, actuators, and so on). NCSs have a broad range of applications in areas such as industrial control and signal processing. This survey provides an overview on the theoretical development of NCSs. In-depth analysis and discussion is made on sampled-data control, networked control, and event-triggered control. More specifically, existing research methods on NCSs are summarized. Furthermore, as an active research topic, network-based filtering is reviewed briefly. Finally, some challenging problems are presented to direct the future research.

This paper proposes a generalized Hopfield network for solving general constrained convex optimization problems. First, the existence and the uniqueness of solutions to the generalized Hopfield network in the Filippov sense are proved. Then, the Lie derivative is introduced to analyze the stability of the network using a differential inclusion. The optimality of the solution to the nonsmooth constrained optimization problems is shown to be guaranteed by the enhanced Fritz John conditions. The convergence rate of the generalized Hopfield network can be estimated by the second-order derivative of the energy function. The effectiveness of the proposed network is evaluated on several typical nonsmooth optimization problems and used to solve the hierarchical and distributed model predictive control four-tank benchmark.

This brief investigates the interval iterative learning problem for dynamic systems with hierarchical neural network (HNN)-structural output. The first objective is to design the output of a dynamic system with HNN structure. A sufficient condition is obtained to achieve the interval tracking in a finite interval by applying iterative learning control (ILC). Then, the saturated ILC is considered into the discussed system, and a less conservative criterion is obtained to achieve the tracking in a finite interval using a network structure decomposition technique. Finally, simulation results are given to illustrate the usefulness of the developed criteria.

The generation of representative computer system behavior profile from system calls in LINUX environments to establish reliable Host Based Anomaly Detection Systems (HADS) against Next Generation of Attacks (NGA) is a challenge due to two major reasons. Firstly, NGA causes a low footprint upon host activities and consequently, attack activities are difficult to detect from normal computer processes in terms of accuracy and processing time. Secondly, there is no effective method to extract the natural difference from the two different types of traces (e.g. normal or abnormal) of system calls. Following these reasons, a semi-supervised model is proposed, which is comprised of two parts. Firstly, to establish an unsupervised computer behavior classification, an integer data zero-watermarking algorithm is developed to extract abstract hidden representation of system calls. This hidden representation constitutes the natural difference between attack and normal computer system behavior in real-time. Secondly, various supervised Machine Learning (ML) algorithms and normalizations are realized with proposed hidden representation of the system calls to evaluate the semi-supervised model in HADS. To evaluate the performance in terms of accuracy and processing time, the publicly available bench mark host based data sets: ADFA-LD and KDD 98 have been utilized. Each data set is the collection of traces of processes and each trace comprises of process's system calls. Experimental results shows that the suggested semi-supervised model outperforms existing methodologies in terms of accuracy and processing time for the detection of low and high foot print attacks.

In this paper a control strategy is proposed for Automatic Generation Control (AGC), which focuses on the interconnected system instead of individual areas and minimizes the cost of control while maintaining network constraints. A methodology is developed to maintain the network constraints by limiting the tie line flows within safe thermal limits when the generation and disturbances in the interconnected areas are utilized for AGC. Our contribution comes from extending the Economic AGC approach in [1] so that it is feasible for practical implementation. This is achieved by imposing constraints on a set of non physical auxiliary variables. The optimization function is extended to include the constraints on the auxiliary variables by using a logarithmic barrier function method. It is proved for a two area power system that the tie line flows attain the same values as the auxiliary variables under steady state conditions. A simulation study is presented to show the effectiveness of our approach.

In this paper, demand side management problem is reformulated by the jointly constrained noncooperative game. The corresponding networked optimization method that concentrates on seeking generalized Nash Equilibrium for noncooperative game is developed for the problem. Due to the large scale of users in demand side management, the noncooperative game based demand side management is divided into groups of sub games, which can be efficiently solved by Nikaido-Isoda function based Newton method. Simulation results verify that the effectiveness of the designed algorithm.

Blind localization of primary user (PU) is a geo-location spectrum awareness feature that can be very useful in enhancing the functionality of cognitive radios (CRs) in terms of minimizing the interference to the PU. However, the estimation of the PU position within the region is made difficult because cooperation between the PU and the secondary user (SU) does not exist and therefore the PU signal parameters remain unknown to the SU. The centroid-based localization techniques have significantly been adopted as suitable candidates that do not require knowledge of such parameters. In this paper we investigate the localization performance of such techniques by imposing constraints to the selection of the SU nodes, termed as SU cluster, to estimate the PU location. In particular, we impose a minimum distance constraint between any two SU nodes and group the qualifying nodes into a cluster. Only the SU nodes from the constrained cluster can take part in localizing the PU. We simulate the proposed method for a shadow fading wireless environment and compare the results with the centroid and the weighted centroid based blind localization methods. Our results show that the mean squared error in the estimation of the position of the PU is significantly improved for the proposed method compared to the two standard centroid localization techniques especially when the true PU location is away from the center of the region.

Noise can induce various dynamical behaviors in nonlinear systems. White noise perturbed systems have been extensively investigated during the last decades. In gene networks, experimentally observed extrinsic noise is colored. As an attempt, we investigate the genetic toggle switch systems perturbed by colored extrinsic noise and with kinetic parameters. Compared with white noise perturbed systems, we show there also exists optimal colored noise strength to induce the best stochastic switch behaviors in the single toggle switch, and the best synchronized switching in the networked systems, which demonstrate that noise-induced optimal switch behaviors are widely in existence. Moreover, under a wide range of system parameter regions, we find there exist wider ranges of white and colored noises strengths to induce good switch and synchronization behaviors, respectively; therefore, white noise is beneficial for switch and colored noise is beneficial for population synchronization. Our observations are very robust to extrinsic stimulus strength, cell density, and diffusion rate. Finally, based on the Waddington's epigenetic landscape and the Wiener-Khintchine theorem, physical mechanisms underlying the observations are interpreted. Our investigations can provide guidelines for experimental design, and have potential clinical implications in gene therapy and synthetic biology.

This paper investigates the passivity problem for a class of uncertain stochastic fuzzy nonlinear systems with mixed delays and nonlinear noise disturbances by employing an improved free-weighting matrix approach. The fuzzy system is based on the Takagi-Sugeno model that is often used to represent the complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning. To reflect more realistic dynamical behaviors of the system, the parameter uncertainties, the stochastic disturbances, and nonlinearities are considered, where the parameter uncertainties enter into all the system matrices, the stochastic disturbances are given in the form of a Brownian motion. The mixed delays comprise both discrete and distributed time-varying delays. By taking the relationship among the time delays, their lower and upper bounds into account, some less conservative linear-matrix-inequality-based delay-dependent passivity criteria are obtained without ignoring any useful terms in the derivative of Lyapunov functional. Finally, numerical examples are given to demonstrate the effectiveness and merits of the proposed methods.

This paper studies the global pinning synchronization problem for a class of complex networks with switching directed topologies. The common assumption in the existing related literature that each possible network topology contains a directed spanning tree is removed in this paper. Using tools from M -matrix theory and stability analysis of the switched nonlinear systems, a new kind of network topology-dependent multiple Lyapunov functions is proposed for analyzing the synchronization behavior of the whole network. It is theoretically shown that the global pinning synchronization in switched complex networks can be ensured if some nodes are appropriately pinned and the coupling is carefully selected. Interesting issues of how many and which nodes should be pinned for possibly realizing global synchronization are further addressed. Finally, some numerical simulations on coupled neural networks are provided to verify the theoretical results.

Concerning on optimal economic dispatch, interior point method via 6-logarithmic barrier is employed to reformulate the cost function of power generation. Fully distributed technology-enabled algorithm is developed to solve the economic dispatch. More specifically, the minimum connected dominating set based distributed algorithm aims at efficiently allocating the task of supply-demand balance for the whole power grid. A fast gradient based distributed optimization method is designed to fast converge to optimal solution. The simulations illustrate the effectiveness and good performance of our algorithms.

This paper mainly studies the speed control for a permanent magnet synchronous motor system. The relationship between the reference quadrature axis current and the speed output is approximately considered as a second-order model. Based on this second-order model, a composite control strategy is adopted, where a continuous sliding mode controller is designed for the speed regulation without chattering and a disturbance observer is introduced as a compensator to resist disturbances and to reduce control gains. Simulation results have been presented to illustrate that the proposed method has good responses to reference speed signals with torque load disturbances.

Vulnerability assessment of power system networks is becoming an essential requirement for minimizing the risk of disastrous power outage events. This paper proposes a novel centrality index which treats the power system as two complex networks: real power flow network and reactive power flow network. Two vulnerability indices (real power flow centrality index and reactive power flow centrality index) are proposed which represent the vulnerability level in two different networks. They are combined using fuzzy logic to generate the system composite centrality index. The analysis is carried out on the IEEE 14 bus system.

The forecast of electricity consumption is a key element to develop successful policies for electricity demand management. A significant variable affecting the demand of electric energy in commercial and industrial building is the outdoor temperature. In this paper, an intelligent relational pattern matching system is proposed to forecast electricity demand using smart meter data and outdoor temperature profiles. In order to identify the relationship map between the patterns of power consumption and temperature, a learning rule based approach is developed to incrementally learn the correlation between both pattern bases. One-week-ahead forecast is executed by a similarity search method using the found relationship map. The effectiveness of the proposed system and prediction method is verified using real smart meter data from a commercial building, and weather forecasts from the Australian Bureau of Meteorology.

Zero-order holder discretization effects in sliding mode control systems with an input delay are studied. Conditions for the existence of periodic solutions are derived and the existence of periodic steady states is investigated. The influence of the discretization step and the delay on the period and the amplitude of steady state oscillations is studied. Simulation results are presented to show the structure of basins of attraction of periodic orbits with different switching patterns.

This paper investigates the distributed consensus problem of fractional-order multi-agent systems under a time-invariant communication topology, where the dynamics of each agent is described by a general fractional-order differential equation. To achieve consensus, a fractional-order observer-type consensus protocol based on relative output measurements is introduced. By using tools from Lyapunov stability theory for fractional-order systems, two theorems about the consensus of fractional-order multi-agent system with a fixed communication topology having a spanning tree are then proposed. Finally, the effectiveness of the theoretical results is demonstrated through numerical simulations.

Identification of important nodes is an emerging hot topic in complex networks over the last few decades. The so-called important nodes are hub, influential nodes, leaders, and so on. To characterize the importance of nodes, various indexes are introduced in complex networks, such as degree, closeness, betweenness, k-shell, and principal component analysis based on the adjacency matrix. By using the above indexes and multivariate statistical analysis technique, this paper aims at developing a new approach to identify the important nodes in artificial bio-molecular networks generated from the duplication-divergence (DD) model. In particular, the statistical characteristics of important nodes are also investigated. The above results shed light on the potential real-world applications in bio-molecular networks, such as deducing the genes related to the specific disease.

A new continuous dynamic sliding-mode control (CDSMC) method is proposed for high-order mismatched disturbance attenuation in motion control systems using a high-order sliding-mode differentiator. First, a new dynamic sliding surface is developed by incorporating the information of the estimates of disturbances and their high-order derivatives. A CDSMC law is then designed for a general motion control system with both high-order matched and mismatched disturbances, which can attenuate the effects of disturbances from the system output. The proposed control method is finally applied for the airgap control of a MAGnetic LEViation (MAGLEV) suspension vehicle. Simulation results show that the proposed method exhibits promising control performance in the presence of high-order matched and mismatched disturbances.

This article discusses recent trends in Smart Grid technology. Three selected aspects are (1) distributed information technology (IT), used for smart metering and multi-agent based controls, (2) big-data, resulting out of metering, sensors and other IT-enabled sources of information, and (3) an intelligent demand side, where demand response serves as contribution to grid services. New elements in the grid, most notably large numbers of fluctuating renewable energy sources, and new functionality like markets make it necessary to introduce methods and technologies from other domains that already faced such changes.

Synchronization in complex networks has been widely investigated recently. Almost all the existing conditions for reaching certain dynamics in complex networks require the global spectrum information of the network. A challenging problem for how the network structure affects the network dynamics in a distributed way especially with directed topologies is still unreleased in the recent decade. In particular, what kind of network structure or coupling weights in the general directed complex networks are very critical and how to change these weights in a local setting to achieve the desired behavior? This paper aims to solve this challenging problem.

This paper proposes a maximum-flow-based complex network approach for the analysis of the vulnerability of power systems. A new centrality index is proposed, taking into consideration the maximum flow from the source (generator) nodes to the sink (load) nodes, for assessing the network. The Max-Flow Min-Cut Theorem, also known as Ford-Fulkerson Theorem, is used for evaluating the capacity of links. The proposed methodology is then used to identify vulnerable lines of the IEEE 118 bus system and its effectiveness is demonstrated through simulation studies.

This paper investigates the speed regulation problem of permanent magnet synchronous motor servo system based on terminal sliding mode control method. By introducing a non-singular terminal sliding mode manifold, a novel terminal sliding mode controller is designed for the speed loop. This controller can make the states not only reach the manifold in finite time, but also converge to the equilibrium point in finite time. Thus, the controller could make the motor speed reach the reference value in finite time, obtaining a faster convergence and a better tracking precision. Meanwhile, considering the large chattering phenomenon caused by high switching gains, a composite terminal sliding mode control method based on disturbance observer is proposed to reduce chattering. Through disturbance estimation for feed-forward compensation, the composite terminal sliding mode controller may take a smaller value for the switching gain without sacrificing disturbance rejection performance. Matlab simulation and TMS320F2808 DSP experimental results are provided to show the superiority of the proposed methods.

This paper studies second-order consensus in multi-agent systems with sampled position and velocity data. A distributed linear consensus protocol with second-order dynamics is first designed, where both sampled position and velocity data are utilized. A necessary and sufficient condition based on the sampling period, the coupling gains, and the spectra of the Laplacian matrix, is established for reaching consensus of the system in this setting. It is found that second-order consensus in such a multi-agent system can be achieved by appropriately choosing the sampling period determined by a polynomial with order three. In particular, second-order consensus cannot be reached for a sufficiently large sampling period while it can be reached for a sufficiently small one under some conditions. Then, the coupling gains are carefully designed under the given network structure and the sampling period. Furthermore, the consensus regions are characterized for the spectra of the Laplacian matrix. On the other hand, second-order consensus in delayed undirected networks with sampled position and velocity data is then discussed. A necessary and sufficient condition is also given, by which appropriate sampling period can be chosen to achieve consensus in multi-agent systems. Finally, simulation examples are given to verify and illustrate the theoretical analysis.

This paper proposes high-order terminal sliding mode control strategies for wind energy conversion systems. Nonsingular terminal sliding mode manifolds and related second-order sliding mode control strategies are designed respectively for the speed controller and the current controllers of the induction generator in a wind energy conversion system. The second-order sliding mode technique is utilized to soften the non-smooth control signal, which can guarantee the actual control signal is continuous and smooth. The simulations are presented to validate the proposed method.

A generalized power flow sensitivity index is proposed based on the traditional power flow distribution sensitivity analysis method. A strategy for power injection and power flow allocation is proposed, concerning the background of integrating the renewable energy resources into the traditional power grids. A coordinated operation mechanism between the injected renewable energy resources and the existing power generators is proposed. The reliability of the power grids can be improved. The power flow on the distribution network can be reduced. Simulation study verifies the claims in this paper.

It is well known that the evolutionary dynamics characterizes the process of competition and evolution of phenotypes and behaviors in a population. Intuitively, the individual with a higher fitness will have a higher survival probability, which should be reflected in the evolutionary dynamic model. However, due to the computational complexity of fixation probability, it is very difficult to prove the existence of this property in evolutionary dynamics on complex networks. This paper aims at providing a rigorously theoretical proof for the global existence of such property in the local evolutionary dynamics by using the coupling and splicing techniques. In particular, we also prove that the fixation probability is monotone increasing for the initial nodes set of mutants. Numerical simulations are also given to validate the proposed approaches.

In this paper, a linear-prediction maximum power point tracking (MPPT) algorithm for photovoltaic (PV) power generation is presented. This allows rapid tracking without step-size reference. The new methodology has two parts: linear prediction and error correction. The first part estimates the maximum power point (MPP); this improves the MPPT response speed. The second part calibrates the error after the linear prediction; this enhances the calculation accuracy which leads to a faster MPP convergence. Theoretical analysis and simulations are put forward to validate the feasibility of the linear prediction method. Convergence, error correction, and steady and dynamic state evaluations are made. The results show that the algorithm can work effectively and have the advantages of fast response and high efficiency when compared to the Perturbation and Observe (P&O) algorithm.

The doubly salient permanent-magnet synchronous machine (DSPMSM) is a new type of brushless machine with permanent magnet locating in its stator pole. Compared with other traditional PMSMs, it can offer advantages of high power/torque density, simple mechanical structure and wide speed range for high speed cruising, which is attractive to the applications of wind energy, plug-in hybrid electrical vehicle, etc. However, due to the nature of salient poles in both the stator and rotor, the DSPMSM suffers from severe torque and flux ripples for its variable magnetic circuits and equivalent air gap length. The conventional switching-table-based direct torque control (DTC) receives increasing attention for its merits of quick dynamic response, strong robustness and simple control structure. However, during the conventional DTC algorithm, large ripple of both torque and air gap flux often occurs for its hysteresis control based on Bang-Bang modification principle. This paper presents one improved strategy to reduce the torque ripple of DSPMSM drive system by the help of model based predictive torque control (MPTC), which is an improved algorithm in the base of conventional DTC. Similar as the traditional MPTC strategy, the new algorithm still requires one completely decoupling control scheme. By selecting the best voltage vector to satisfy the demands of torque and flux, the new method can obviously reduce both torque and flux ripples. Comprehensive simulation results are finally presented to validate theoretical analysis, and further experiments will be available in the near future.

It is well known that the selection of fixation probability is the fundamental problem for the evolutionary dynamics in structured populations. This paper aims to introduce a general approach for investigating the evolutionary dynamics in a class of structured populations. It includes the evolutionary game dynamics and constant selection dynamics with different asynchronous updating rules, such as ‘birth-death’, ‘voter model’, ‘death-birth’, and ‘imitation’. It should be pointed out that the proposed method provides an effective way to resolve the evolutionary dynamics on general graphs. In particular, it introduces a useful calculating tool to analyze various evolutionary dynamics on small order graphs.

Variable Structure Systems (VSSs) have been studied extensively for over 60 years and widely used in practical applications. A particular interest in VSS is the so-called Sliding-Mode Control (SMC), which is simple in control design and robust in parameter variations and disturbances. Modern control systems nowadays are implemented through computers. This presents challenges for SMC based VSS because the digital nature of computer-control weakens the essential assumption of SMC, that is, the switching frequency should be unlimited in order to deliver effective disruptive control actions. Extensive research activities have been since undertaken in computer-controlled VSS over the last thirty years. This survey provides a comprehensive account of the key developments in this field and examines the key technical research challenges for the future developments.

Multi-agent systems are ubiquitous in the world. Recently, multi-agent systems have received increasing attention from mathematics, physics, engineering sciences, and social science communities. This paper firstly introduces several fundamental concepts and then reviews several representative models of multi-agent systems, including the Boids model, Vicsek model, Couzin-Levin model and its invariants, and various complex dynamical networks. Based on these models, it further investigates the dynamical behaviors of multi-agent systems, such as consensus, convergence, adaptation, and consensus decision-making. Moreover, it briefly reviews the main progress in the control of multi-agent systems. Finally, it looks ahead into some important research topics on multi-agent systems, with regard to modelling, analysis, and control.

Over the last two decades, multi-scroll chaos generation has seen promising advances and becomes an active research field. This paper initiates a novel approach to design various grid multi-wing butterfly chaotic attractors from piecewise Lü system based on switching control and heteroclinic orbit. It should be especially pointed out that these generating multi-wing chaotic attractors are chaotic in the sense of Smale horseshoe from Shilnikov theorem. Moreover, there are some potential engineering applications in the future because of the simplicity of the proposed design approach.

In this paper, the robust global asymptotic stability (RGAS) of generalized static neural networks (SNNs) with linear fractional uncertainties and a constant or time-varying delay is concerned within a novel input-output framework. The activation functions in the model are assumed to satisfy a more general condition than the usually used Lipschitz-type ones. First, by four steps of technical transformations, the original generalized SNN model is equivalently converted into the interconnection of two subsystems, where the forward one is a linear time-invariant system with a constant delay while the feedback one bears the norm-bounded property. Then, based on the scaled small gain theorem, delay-dependent sufficient conditions for the RGAS of generalized SNNs are derived via combining a complete Lyapunov functional and the celebrated discretization scheme. All the results are given in terms of linear matrix inequalities so that the RGAS problem of generalized SNNs is projected into the feasibility of convex optimization problems that can be readily solved by effective numerical algorithms. The effectiveness and superiority of our results over the existing ones are demonstrated by two numerical examples.

In this paper, the convergence accuracy of system states in discretized sliding mode control (SMC) systems is thoroughly analyzed using big O notation. It confirms that for a n-th order system, system states x1, x2, ..., xnhave different convergence rates.

This paper describes the critical features for information sharing and exchange between industrial pan stage process models within a knowledge based supervisory support system (KBSSS), for pan stage operations in a sugar mill. The innovation outlined underpins the integration of the industrial process models working cooperatively with a blackboard system to provide system forecasting of future pan stage operating conditions. The primary topic of this paper will be a description of the approach and how it supports information exchange during the forecast process undertaken by the pan stage industrial process models with a focus on: (1) design features, (2) implementation and (3) application to the prediction process.

This paper describes a deployment proposal and functional characteristics of a knowledge based supervisory support system (KBSSS) to provide expert knowledge in the control and management of the pan stage within a sugar mill. This decision support system utilises fragmented and diverse sources of information and unifies these into a cohesive structure to solve an important industrial control problem. Given the functional system characteristics, a deployment proposal for the embedding of the KBSSS within existing sugar mill infrastructure is presented. The primary topic of this paper will be a description of: (1) the deployment proposal, (2) system process flows and (3) operational levels of the KBSSS.

This paper presents a modified PSO algorithm for solving constrained multi-objective optimization problems. Based on the constraint dominance concept, the proposed approach defines two sets of selection rules for determining the cognitive and social components of the PSO algorithm. The simulation results to the four constrained multi-objective optimization problems demonstrate the proposed approach is able to find Pareto-optimal solutions effectively.

This paper revisits a traditional Ant Foraging algorithm and proposes a Cluster-based Softbots algorithm to address the performance issues caused by constraints of random autonomous search featured in most swarm intelligence-based algorithms. A simple hierarchy is introduced to regulate the unfolding of dynamically changing swarm-like behaviors. Comparative experiments for Ant Foraging and the proposed Cluster-based Softbots are described. The results demonstrate that Softbots have significant comparative advantages over a traditional Ant Foraging algorithm on the benchmark criteria in the presented experimental settings. It is shown that Softbots are more suitable for resource-lean search circumstances whereas not many individual agents can be allocated.

SCADA (supervisory control and data acquisition) systems control and monitor industrial and critical infrastructure functions, such as the electricity, gas, water, waste, railway and traffic. Recent attacks on SCADA systems highlight the need of a SCADA security testbed, which can be used to model real SCADA systems and study the effects of attacks on them. We propose the architecture of a modular SCADA testbed and describe our tool which mimics a SCADA network, monitors and controls real sensors and actuators using Modbus/TCP protocol. Using distributed denial of service (DDoS) scenarios we show how attackers can disrupt the operation of a SCADA system.

A geometric features-based filtering technique, named as the adaptive geometric features based filtering technique (AGFF), is presented for removal of impulse noise in corrupted color images. In contrast with the traditional noise detection techniques where only 1-D statistical information is used for noise detection and estimation, a novel noise detection method is proposed based on geometric characteristics and features (i.e., the 2-D information) of the corrupted pixel or the pixel region, leading to effective and efficient noise detection and estimation outcomes. A progressive restoration mechanism is devised using multipass nonlinear operations which adapt to the intensity and the types of the noise. Extensive experiments conducted using a wide range of test color images have shown that the AGFF is superior to a number of existing well-known benchmark techniques, in terms of standard image restoration performance criteria, including objective measurements, the visual image quality, and the computational complexity.

This paper presents a multi-objective constraint handling method incorporating the Particle Swarm Optimization (PSO) algorithm. The proposed approach adopts a concept of Pareto domination from multi-objective optimization, and uses a few selection rules to determine particles’ behaviors to guide the search direction. A goal-oriented programming concept is adopted to improve efficiency. Diversity is maintained by perturbing particles with a small probability. The simulation results on the three engineering benchmark problems demonstrate the proposed approach is highly competitive.

Discretization effects in single input equivalent control based SMC systems of arbitrary dimension are studied. A bound for the number of iterates a trajectory may stay on one side of the sliding surface is found. Conditions for existence of periodic orbits are formulated. It is confirmed in simulations that even for very small values of discretization steps complex behaviors including periodic solutions of various length may be observed.

An Adaptive geometric features based filtering (AGFF) technique with a low computational complexity is proposed for removal of impulse noise in corrupted color images. The effective and efficient detection is based on geometric characteristics and features of the corrupted pixel and/or the pixel region. A progressive restoration mechanism is devised using multi-pass non-linear operations. Through extensive experiments conducted using a wide range of test color images, the proposed filtering technique has demonstrated superior performance to that of well-known benchmark techniques, in terms of objective measurements, the visual image quality and the computational complexity.

This paper introduces a novel image enhancement methodology driven by both global and local processes. This methodology, based on the parameters controlled hybrid approach which integrates the advantages of point operations with local enhancement techniques, can enhance, simultaneously, the overall contrast and the sharpness of an image and increase, especially, the visibility of specified portions or aspects of the image. The methodology was compared with other classical image enhancement techniques, such as linear contrast stretching and histogram equalization. Results obtained in terms of subjective evaluation have shown the superiority of the proposed approach.

Two discretization methods for a simple sliding mode control system are studied in detail. It is shown that for arbitrary discretization step the zero-order holder discretization is equivalent to the Euler discretization with a smaller value of the discretization step. A complete diagram of admissible short periodic orbits is found and structure of periodic orbits is analyzed.

Communications to, or between, low-end microprocessors within a product always comes at a cost. This paper develops a new, economic solution that will be useful in a variety of cost-sensitive applications. This paper starts by identifying the properties of an inter-microprocessor communications system that adds minimal cost to a product and enables the use of lower price microprocessors. This leads us to introduce a new category of communications called time independent asynchronous (TIA) communications. An economic 2-wire TIA communications protocol is developed and described using timing diagrams. The protocol is modeled using signal transition graphs (STGs), which are found to have some limitations, and so a modification is developed called STG for threads (STG-FT). Two-wire TIA is simulated to confirm livelock and deadlock properties. An implementation is created that verifies the simulation results, and the performance is reported. Finally, a novel application of 2-wire TIA is discussed.

A fuzzy neural network enhanced with evolutionary algorithms, based on the GRNNFA, is proposed that is able to accurately predict the effects of a single compartment fire, based on experimental data. This system is shown to make predictions with within 5% accuracy, thus demonstrating that it can learn the non-linear nature of fluid dynamics. Because of its speed it is able to quickly generate views of the nature of the fire, enabling users to interrogate it and gain intelligence as to what compartment geometries lead to greater fire hazards.

A new adaptive backpropagation (BP) algorithm based on Lyapunov stability theory for neural networks is developed in this paper. It is shown that the candidate of a Lyapunov function V(k) of the tracking error between the output of a neural network and the desired reference signal is chosen first, and the weights of the neural network are then updated, from the output layer to the input layer, in the sense that deltaV(k) = V(k) - V(k - 1) < 0. The output tracking error can then asymptotically converge to zero according to Lyapunov stability theory. Unlike gradient-based BP training algorithms, the new Lyapunov adaptive BP algorithm in this paper is not used for searching the global minimum point along the cost-function surface in the weight space, but it is aimed at constructing an energy surface with a single global minimum point through the adaptive adjustment of the weights as the time goes to infinity. Although a neural network may have bounded input disturbances, the effects of the disturbances can be eliminated, and asymptotic error convergence can be obtained. The new Lyapunov adaptive BP algorithm is then applied to the design of an adaptive filter in the simulation example to show the fast error convergence and strong robustness with respect to large bounded input disturbances.

It has been established that a class of bandpass sigma delta modulators (SDMs) may exhibit state space dynamics which are represented by elliptical or fractal patterns confined within trapezoidal regions when the system matrices are marginally stable. It is found that fractal patterns may also be exhibited in the phase plane when the system matrices are strictly stable. This occurs when the sets of initial conditions corresponding to convergent or limit cycle behavior do not cover the whole phase plane. Based on the derived analytical results, some interesting results are found. If the bandpass SDM exhibits periodic output, then the period of the symbolic sequence must equal the limiting period of the state space variables. Second, if the state vector converges to some fixed points on the phase portrait, these fixed points do not depend directly on the initial conditions.

A general backpropagation algorithm is proposed for feedforward neural network learning with time varying inputs. The Lyapunov function approach is used to rigorously analyze the convergence of weights, with the use of the algorithm, toward minima of the error function. Sufficient conditions to guarantee the convergence of weights for time varying inputs are derived. It is shown that most commonly used backpropagation learning algorithms are special cases of the developed general algorithm.

This paper demonstrates the framework and methodology how Parthenium weed population can be predicted using rule-base fuzzy logic as applied to GIS spatial image.

An evolutionary procedure is proposed for the design of the fuzzy gain scheduling controller (FGSC) for nonlinear control systems. The FGSC makes use of the structure of conventional gain scheduling controllers and incorporates the Tanaka-Sugeno (TS) fuzzy system structure. A learning algorithm based on evolutionary programming is used for learning the structure and optimising the parameters of the FGSC. Simulation results are shown to demonstrate the effectiveness of the scheme.

In this paper the design of adaptive sliding mode control for fuzzy systems is discussed. For a complex physical system represented by an amalgamated fuzzy global model that compromises a set of linear models, conditions for the adaptive sliding mode control to stabilize the global fuzzy model are given. The advantage of the control structure is that a priori knowledge of the upper bounds of bounded uncertainties and internal parameters is not required. Numerical simulations are presented to show the effectiveness of the controller.