We study the leader-follower consensus problem in multiagent systems with heterogeneous agent dynamics and multiple internal players per agent, each with distinct and interaffected objectives. Formulated as a multiplayer differential game per agent, the goal is to achieve output consensus among all agents while ensuring Nash equilibrium controls across each agent's internal players. To address this challenge, we introduce a distributed control framework that integrates both feedforward (regulator-based) and feedback (game-theoretic Riccati-based) components. We further design a FilterNet reinforcement learning (RL) architecture that solves the control solutions while eliminating the need for large-scale distributed data storage. Organized into four layers, FilterNet handles admissible policy identification, online initialization, asynchronous updates for Nash policy convergence, and real-time regulator solutions. This design reduces data requirements, ensures initial excitation, and accelerates convergence. Theoretical guarantees establish conditions for solvability and convergence. Numerical simulations and comparisons with existing methods confirm the effectiveness and superiority of the proposed approach.

This article investigates the prescribed-time leader-following consensus problem for heterogeneous multiagent systems (MASs) with unknown sensor sensitivity. Considering a connected undirected topology, we introduce a time-varying dual observer/controller design framework that leverages both regular local and inaccurate feedback to achieve consensus tracking within a prescribed time. The proposed analytical framework applies to MASs equipped with sensors exhibiting uncertain sensitivities. A key innovation of our design is the framework of a distributed matrix pencil formulation based on the worst case sensor, leading to control parameters that exhibit sufficient robustness and relatively low conservativeness. Additionally, we establish a bounded time-varying feedback (TVF) scheme that extends the prescribed-time distributed protocol to an infinite time domain without compromising final control accuracy. This includes a detailed discussion of the analytical relationship between switching time and the upper bound of the time-varying gain. In particular, we employ the proportional coefficient obtained from several matrix pencil formulations along with a monotonically increasing time-varying (blow-up) function to derive the feedback gain, simplifying the complexity of control design. Simulations validate the effectiveness of the methodology through a series of electromechanical systems and single-link robot manipulators.

This article investigates event-triggered cooperative adaptive optimal output regulation for unknown discrete-time multiagent systems (MASs) with input saturation. To address the issue that some followers may have no direct access to the leader, distributed observers are proposed to estimate the reference signals. A dynamic event-triggering mechanism is introduced to reduce communication and computational costs. By combining the internal model principle with low-gain and policy iteration (PI) techniques, an inner-outer loop-based dynamic event-triggered adaptive optimal control approach is developed. The convergence of the proposed algorithm is rigorously analyzed, and the control inputs are explicitly constrained within the input limits. A comprehensive stability analysis is provided, along with conditions for the MASs to achieve leader-to-formation stability (LFS). The sensitivity of the suboptimality index to system parameters is also taken into consideration. Finally, the effectiveness of the proposed approach is validated through a simulation example applied to grid-connected ac microgrid control.

This article investigates the problem of removing the peaking phenomenon in the stabilization of a class of nonlinear cascaded systems using linear partial state feedback within a quadratic program (QP) framework. By appropriately designing the QP and selecting its parameters, the inter-subsystem cascaded input terms are effectively constrained within a desirable control-invariant set, thereby eliminating undesirable transient peaks. Semi-global stabilization of the overall system is achieved through only minimal modifications to the nominal linear feedback controllers. Owing to the simplicity of the resulting controller structure and the real-time efficiency of QP solvers, the proposed method is readily applicable to practical systems. Numerical examples from previous studies are revisited to demonstrate the effectiveness and robustness of the proposed control strategy.

This article explores the problem of prescribed-time stabilization for a class of high-order polynomial nonlinear systems with unknown time-varying nonlinearities. The key technique behind the proposed strategy involves fixing the time-varying components to their bounded values before the prescribed time and establishing a new lemma to suppress the time-varying continuous functions in the investigated system. We design a continuous bounded feedback controller to address the singularities induced by infinite control gains at the prescribed time and to suppress the implicit effects of time variations. Superior to the existing prescribed-time stabilization results, our strategy achieves the states' convergence within the prescribed-time and the nontruncated run of controller simultaneously. We employ the wing rock motion to demonstrate the practicality and superiority of the developed strategies.

This study introduces a new asynchronous sampled-data distributed consensus control protocol for nonlinear fractional-order multiagent systems (MASs) containing system uncertainties along with time-varying disturbances. With strict consideration of the hereditary and infinite-memory characteristics of fractional-order systems, a novel adaptive backstepping-based distributed sampled-data control scheme is developed for individual agents with asynchronous sampling mechanisms. Through Lyapunov stability analysis, it is demonstrated that the proposed strategy guarantees the stability of the entire closed-loop system, meaning that all signals will remain within bounds and each agent can achieve output consensus with the specified time-varying reference trajectory. The efficacy of the proposed approach is illustrated through simulation studies, which also serve to validate the results obtained.

The advent of cyber-physical production systems (CPPSs) has greatly improved production responsiveness. However, effective control and decision-making in CPPSs remain challenging due to the dynamic nature of both internal operations and external environments. We present a multiobjective optimization approach for managing operation, maintenance, and support decisions in CPPSs under time-varying demands. Specifically, a decision-making framework is developed to enable collaborative control, incorporating reliability-based risk assessment and multiobjective optimization techniques. To facilitate continuous decision-making in response to uncertainties, a biobjective optimization model is formulated using a receding horizon control architecture, addressing conflicting objectives simultaneously. An enhanced multiobjective pigeon-inspired optimization algorithm is proposed to generate Pareto-optimal solutions by co-minimizing the production risks and costs. Experimental validations are carried out through both numerical simulations and real-world experiments on a subsea production system in the South China Sea, involving two support sites, six production sites, thirty-six machines, and 288 components.

This article presents a double-channel (sensor-to-controller channel and controller-to-actuator channel) event triggered control method for nonlinear interconnected systems subject to sensor and actuator faults via the backstepping technique. It should be emphasized that the utilization of triggering mechanism at the sensor side poses a challenge to the design of backstepping control, as it leads to nondifferentiable virtual control signals due to the discontinuous nature of the state/output signals received at the controller side. In contrast to existing methods, the proposed event triggering mechanism eliminates the need for computing virtual control signals at the sensor side before transmitting them to the controller side. By establishing the relationships of the corresponding variables in two communication scenarios (namely, without and with event triggering) and introducing dynamic filtering technique, the problem of nondifferentiable virtual control signals in backstepping design is solved. We present a numerical case study to validate the effectiveness and advantages of the proposed decentralized event triggered control approach.

Efficient real-time decision-making for long-term multiple unmanned aerial vehicles (multi-UAV) missions in geo-distributed environments requires an integrated approach to manage dynamic task demands. We propose a hierarchical dual-layer decision-making framework for multi-UAV mission replanning. The upper layer optimizes multi-UAV deployment using the density-constrained K-medoids clustering and simulated annealing algorithm, achieving globally optimal solutions. The lower layer addresses task assignment via the goal-oriented belief space multiagent reinforcement learning algorithm, which leverages updated belief distributions to mitigate sparse reward and enhance training efficiency. Coordination between the two layers ensures comprehensive coverage of predefined demands while adapting to dynamic events. The effectiveness of the proposed methods is validated through a real-world case study using the 911 call dataset from city emergency services.

High-level decision-making for multiple uncrewed aerial vehicles (multi-UAV) mission planning is crucial, especially with the rising demand for long-term services in geo-distributed environments. However, the interrelated issues of multi-UAV deployment and task allocation are often addressed separately. This article integrates these two problems and introduces a hierarchical framework for effective decision-making. This is achieved by proposing balanced capability (BC), a customized metric tailored for long-term multi-UAV missions with geographically dispersed targets. By considering the global objective and self-organized coordination, a joint optimization model is established from a game-theoretical perspective. Additionally, a novel tangent and cotangent search algorithm (TCSA) is proposed to steer cooperative players toward the global objective in the upper layer, while in the lower layer, a modified distributed task allocation algorithm (MDT2A) incentivizes each autonomous player to efficiently maximize their individual benefits. Simulations validate the effectiveness of the proposed method, with comparative results highlighting the superiority of the algorithms.

This study focuses on addressing the challenge of global output feedback control problem for a class of time-varying stochastic nonlinear systems subject to multiple uncertainties. The primary challenge concerns how to construct time-varying functions to counteract the effects of unmeasurable error coming from system output as well as the persistently increasing nonlinearities. By employing a full-order state observer and the dual gain approach, we design an output feedback regulator over the entire time domain to guarantee the existence and uniqueness of the closed-loop system’s solution and the almost sure asymptotic convergence of the state. This methodology achieves both the domination of the unknown growth rate and the unified system design, irrespective of sensor sensitivity. Finally, practical and numerical simulation examples demonstrate the feasibility of the presented approach.

This article investigates the event-triggered cooperative adaptive optimal output regulation problem for unknown multiagent systems (MASs) under switching network. To address communication disruptions between subsystems and the leader, a distributed observer is provided to estimate the reference signals. Without using system dynamics, an event-triggered mechanism is established to reduce computation and communication costs. Then, event-triggered adaptive optimal controllers are developed by using the available input/state data. By exploiting the Lyapunov stability theory and the method of input-to-state stability (ISS), rigorous stability analysis is conducted, and conditions for MASs to achieve the leader-to-formation stability (LFS) are provided. Additionally, the sensitivity of the suboptimality index to system parameters is analyzed. Finally, an application to cooperative adaptive cruise control (CACC) is presented to validate the proposed approach.

It is technically challenging to maintain stable tracking for multiple-input-multiple-output (MIMO) nonlinear systems with modeling uncertainties and actuation faults. The underlying problem becomes even more difficult if zero tracking error with guaranteed performance is pursued. In this work, by integrating filtered variables into the design process, we develop a neuroadaptive proportional-integral (PI) control with the following salient features: 1) the resultant control scheme is of the simple PI structure with analytical algorithms for auto-tuning its PI gains; 2) under a less conservative controllability condition, the proposed control is able to achieve asymptotic tracking with adjustable rate of convergence and bounded performance index collectively; 3) with simple modification, the strategy is applicable to square or nonsquare affine and nonaffine MIMO systems in the presence of unknown and time-varying control gain matrix; and 4) the proposed control is robust against nonvanishing uncertainties/disturbances, adaptive to unknown parameters and tolerant to actuation faults, with only one online updating parameter. The benefits and feasibility of the proposed control method are also confirmed by simulations.

This article investigates the leaderless output consensus control problem for a class of nonlinear multiagent systems with heterogenous system orders and unmatched unknown parameters via output-feedback control. The interaction topology among the agents is undirected and jointly connected. Due to the heterogenous system orders and switching topology among the agents, the classical distributed adaptive backstepping-based control technique cannot be applied to solve the problem considered in this article. To solve this issue, a novel distributed reference system is first proposed for each agent, by using only relative outputs of the neighboring agents. Subsequently, a fully distributed reference system-based adaptive leaderless output consensus control scheme is designed via output-feedback control. A remarkable merit of the proposed control scheme lies in that precisely known nonlinear dynamics, system states, distributed parameter estimates, and the states of virtual reference system are no longer needed to be shared with neighbors. This implies that the communication burden can be effectively alleviated, and even the communication network can be replaced by some perception sensors. Finally, two illustrative examples are provided to verify the effectiveness of the proposed control scheme.

Many networked systems built upon real-life physical or social interactions have time-varying connections among individual units, where the temporal changes in connectivity and/or interaction strength lead to complicated dynamics. The temporal network model was proposed in the form of controlled linear dynamical systems acting in an ordered sequence of time intervals. One of the core challenges in network science is the control of networks and the optimization of the control strategy. However, most canonical frameworks for solving optimal control problems were established for static networks featuring constant topology. New theories and techniques are yet to be developed for the temporal networks, with an important case being that the input and the source-node connection are both variables. In this work, by formulating a quadratic energy cost without solving the Riccati differential equation, we show that the control effort can be reduced substantially by improving either the system trajectories or the input matrices. The two approaches are further combined in a coordinate descent framework, integrating linearly constrained quadratic programming, and a projected gradient descent method. Taken together, the results underline the potential of temporal networks as energy-efficient control systems and present strategies to improve the control input. Moreover, the proposed algorithms can serve as a starting point for future engineering of real-world temporal networks.

This article is concerned with the global fast finite-time adaptive stabilization for a class of high-order uncertain nonlinear systems in the presence of serious nonlinearities and constraint communications. By renovating the technique of continuous feedback domination to the construction of a serial of integral functions with nested sign functions, this article first proposes a new event-triggered strategy consisting of a sharp triggered rule and a time-varying threshold. The strategy guarantees the existence of the solutions of the closed-loop systems and the fast finite-time convergence of original system states while reaching a compromise between the magnitude of the control and the trigger interval. Quite different from traditional methods, a simple logic is presented to avoid searching all the possible lower bounds of trigger intervals. An example of the maglev system and a numerical example are provided to demonstrate the effectiveness and superiority of the proposed strategy.

High-precision and safety control in face of disturbances and uncertainties is a challenging issue of both theoretical and practical importance. In this article, new adaptive anti-disturbance control schemes are proposed for a class of uncertain nonlinear systems with composite disturbances, including additive disturbances, multiplicative actuator faults, and implicit disturbances deeply coupled with system states. Both the cases with known and unknown control/fault directions are investigated. By properly fusing the techniques of disturbance observers and adaptive compensation, it is shown that all closed-loop signals are globally uniformly bounded and the tracking error converges to zero asymptotically, no matter the control/fault directions are known or not. In the case of known directions, the proposed control scheme, for the first time, guarantees asymptotic tracking andL$_{\infty}$tracking performance simultaneously in face of disturbances and actuator faults. Moreover, novel Nussbaum functions and a contradiction argument are introduced, which allow the system to have multiple unknown nonidentical control directions and unknown time-varying fault direction. Simulation results illustrate the effectiveness of the proposed control schemes.

In this article, the decentralized adaptive secure control problem for cyber-physical systems (CPSs) against deception attacks is investigated. The CPSs are formed as a type of nonlinear interconnected strict-feedback systems with uncertain time-varying parameters. The attack affects the information transmission between sensor and actuator in a multiplicative manner. A novel decentralized adaptive backstepping secure control strategy is established by exploiting a particular kind of Nussbaum functions and a flat-zone Lyapunov function analysis approach. It is shown that all of closed-loop signals remain globally bounded, and each output signal eventually converges into a small neighborhood of the origin. Simulation results on an illustrative example are provided to display the effectiveness of the proposed control scheme.

This article focuses on the distributed adaptive consensus control problem for nonlinear systems with unknown parameters and denial-of-service (DoS) attacks. The communication channels among subsystems are directed and DoS attacks are executed on subsystems to jam the communication transmission. Besides, only part of these subsystems can access states of the desired reference system. To actively alleviate attack effects on the consensus performance, a distributed adaptive consensus control scheme with an active-defense mechanism is proposed. The active-defense mechanism consists of an attack-detection algorithm and a switching strategy. The distributed adaptive consensus controller is designed with normalized damping terms in control inputs and parameter update laws. In the presence of DoS attacks, a stability condition is derived based on the designed control scheme, which guarantees that the consensus errors are globally uniformly bounded under arbitrary switching dwell-time. Experimental results are provided to validate the effectiveness of the proposed control scheme with the active-defense mechanism.

This article is concerned with developing a privacy-preserving distributed-learning-based fault diagnosis approach for power transformers. Due to the constraints of data privacy, it is not possible to have enough labeled samples for training. Recently, the emergence of federated learning (FL) has provided a secure and distributed learning framework. However, the unbalanced data from multiple power stations may reduce the overall performance of FL while an untrusted central server can threaten the data privacy and security of clients. To address such challenges, a privacy-preserving FL scheme is developed for transformer fault diagnosis, where a multistep data-sharing strategy and an adaptive differential privacy technology are proposed. Specifically, amounts of shared data and noise perturbation will be designed according to the quantity of local data by the central server. The experimental results on the dataset generated according to IEC publication 60599 show that the proposed method has high diagnostic accuracy across various categories of transformer faults and even on training datasets with extremely unbalanced data quantity where the average accuracy is as high as 95.28%.

It is highly desirable yet challenging to maintain stable system operation in the presence of unexpected malfunctioning sensors and actuators arising from internal component faults and/or external malicious attacks. In this note, we investigate the reliable control problem for a class of nonlinear systems with mismatched modeling uncertainties and unknown control gain matrix as well as abnormal actuating and sensoring units. Such malfunctioning sensors and actuators not only bring about additional modeling uncertainties but also literally pollute the original control influence gain matrix, making the underlying problem further complicated. By using the backstepping-like design procedure, we present an adaptive control solution with relaxed controllability conditions, capable of achieving asymptotic stabilization under severely twisted feedback information due to malicious attacks/sensor failures. Besides a primary actuator, we also propose a strategy to use additional actuators as the backup ones to enhance system survivability, where the actuator replacement automatically and seamlessly takes place from the primary actuator to a backup one, once a severe failure at the primary actuator is detected. Numerical simulation also confirms the effectiveness and benefits of the proposed method.

In this article, the distributed adaptive secure formation control problem for mobile robots is considered. The communication channels among robots are undirected and suffer from denial-of-service (DoS) attacks. Only part of the robots can access the reference trajectories. To mitigate attack effects on the formation performance, a switching strategy for communication channels is designed. Besides, an adaptive secure control scheme is proposed with a distributed adaptive trajectory estimator and an adaptive tracking controller for each robot. In the estimator, damping and normalizing terms are introduced to alleviate attack effects on the system performance. Moreover, these terms can also remove the constraints on the lower bounded dwell time of switching topologies. By applying the backstepping technique, an adaptive tracking control scheme is proposed for robots with uncertainties to track estimator states. According to the proposed secure control scheme, a stability condition is provided, such that the boundedness of formation errors can be guaranteed under DoS attacks and arbitrary switching dwell time. Experimental results are given to illustrate the effectiveness of the proposed secure control scheme.

In this article, an adaptive fractional backstepping controller is designed for integer-order nonlinear uncertain systems of arbitrary order with prescribed performance subject to unknown time-varying disturbances. The integration of fractional adaptive backstepping control procedure and prescribed performance bound technique, which describes the convergence rate and largest overshoot of the output error, into a unitary framework can result in an enhanced control scheme with high precision. Based on Lyapunov analysis, it is theoretically demonstrated that under the proposed control scheme, all the closed-loop signals remain bounded and the output tracking error with respect to the reference signal, which is strictly confined within the specified performance bound for all time, will asymptotically converge to zero. Simulation studies are presented to show the effectiveness of the proposed fractional adaptive prescribed performance-based control strategy.

An approach with multi-rate samplings has been proposed to deal with the uncontrollable challenge of nonholonomic system's linearization around equilibrium in its stabilization problem. Concerning unknown nonlinearities, one scaling factor has been introduced to guarantee global asymptotic stability using techniques of linear feedback domination. Due to the interplay between this scaling factor and system's sampling period under multi-rate samplings, not only controllability of the whole nonholonomic systems has been guaranteed but also flexibility of choosing different sampling periods has been increased. Stabilization of uncertain unicycle under different sampling periods is also presented to demonstrate our proposed scheme and verify the established theoretical results.

In this paper, a noncooperative game with private and coupled constraints under aperiodically intermittent communication is studied, and a distributed generalized Nash equilibrium (GNE) seeking algorithm with two-time-scale structure is proposed to address the issue. Under a practical changing environment that causes aperiodically intermittent communications, players cannot directly and continuously obtain the action information of other players. That is, each player only estimates the actions of all the other players in the communication period, which greatly saves the communication cost on the basis of ensuring actual estimation. At the same time, an adaptive technique is introduced to deal with private constraints, where the penalty parameters can be dynamically adjusted according to the degree of constraint violation. On the basis of players' actions entering the action set, the GNE seeking algorithm is fully distributed. Finally, the effectiveness of the algorithm is verified by economic dispatch game.

State synchronization is crucial in matching digital twins to physical objects dynamically in order to have trustworthy modeling and reliable prediction. This work proposes two approaches to ensure state synchronization on the introduced Dual Digital Twin (DDT). DDT is deployed on both the cloud and the edge ends for better realtime monitoring and control of EVs' batteries, especially when Internet communication is limited. In this paper, the state synchronization problem is cast as a stability problem and also a contraction problem to understand systems' evolution over time. The proposed approaches leverage the Lyapunov stability condition and contraction theory to enforce the synchronization between the twin models and real battery states with streaming data. The contraction analysis framework does not require the knowledge of initial points. The derived stability and contraction conditions that reflect the physical properties of dynamical systems can be embedded into the training process of the reduced order model (ROM) neural network. Simulation results demonstrate the effectiveness of the proposed approaches. The average ROM prediction error is 1.16% and has a 30.39% accuracy improvement with the proposed algorithm.

This article addresses the resilient practical cooperative output regulation problem (RPCORP) for multiagent systems subjected to both denial-of-service (DoS) attacks and actuator faults. Fundamentally different from the existing solutions to RPCORPs, the system parameters considered in this article are unknown to each agent, and a novel data-driven control approach is introduced to handle such an issue. The solution starts with developing resilient distributed observers for each follower in the presence of DoS attacks. Then, a resilient communication mechanism and a time-varying sampling period are introduced to, respectively, ensure the neighbor state is available as soon as attacks disappear and to avoid targeted attacks launched by intelligent attackers. Furthermore, a model-based fault-tolerant and resilient controller is designed based on the Lyapunov approach and the output regulation theory. In order to remove the reliance on system parameters, we leverage a new data-driven algorithm to learn controller parameters via the collected data. Rigorous analysis shows that the closed-loop system can resiliently achieve practical cooperative output regulation. Finally, a simulation example is given to illustrate the effectiveness of the achieved results.

This paper develops a novel approach to addressing the distributed situation awareness (SA) consensus problem for unmanned aerial vehicle (UAV) swarm systems. SA consensus is an important condition for gaining decision-making superiority. However, because of the complexity and antagonism of the mission environment, the widely employed centralized architectures are unable to reach a distributed consensus. We address this urgent issue by proposing a systematic distributed SA consensus scheme, including a distributed optimization-based consensus reaching model, dual-loop decision-making framework, and novel coordination algorithm. Necessarily, we conduct convergence analysis on the proposed algorithm. The effectiveness and superiority of the proposed method are verified by comparative simulations.

This work focuses on the issue of event-triggered practical prescribed time tracking control for a type of uncertain nonlinear systems subject to actuator saturation and unmeasurable states as well as time-varying unknown control coefficients. First, a state observer with simple structure is constructed by means of neural network technology to estimate the unmeasurable system states under time-varying control coefficients. Then, with the help of one-to-one nonlinear mapping of the tracking error, an event-triggered output feedback control scheme is developed to steer the tracking error into a residual set of predefined accuracy within a preassigned settling time. Unlike existing related control methods, there is no need to involve finite-time state observer or fractional power feedback of system states, and thus, the control solution presented here is less complex and more acceptable. The key technique in control design lies in the establishment of an alternative first-order auxiliary system for dealing with the impact arisen from the input saturation. In our proposed approach, a new bounded function related to auxiliary variable and new dynamics of the auxiliary system are skillfully utilized such that the upper bound of the difference between actual input and designed input signal is not involved in implementation of the controller.

To reduce the manpower consumption on box-level annotations, many weakly supervised object detection methods which only require image-level annotations, have been proposed recently. The training process in these methods is formulated into two steps. They firstly train a neural network under weak supervision to generate pseudo ground truths (PGTs). Then, these PGTs are used to train another network under full supervision. Compared with fully supervised methods, the training process in weakly supervised methods becomes more complex and time-consuming. Furthermore, overwhelming negative proposals are involved at the first step. This is neglected by most methods, which makes the training network biased towards to negative proposals and thus degrades the quality of the PGTs, limiting the training network performance at the second step. Online proposal sampling is an intuitive solution to these issues. However, lacking of adequate labeling, a simple online proposal sampling may make the training network stuck into local minima . To solve this problem, we propose an O nline Active P roposal Set G eneration (OPG) algorithm. Our OPG algorithm consists of two parts: Dynamic Proposal Constraint (DPC) and Proposal Partition (PP). DPC is proposed to dynamically determine different proposal sampling strategies according to the current training state. PP is used to score each proposal, part proposals into different sets and generate an active proposal set for the network optimization. Through experiments, our proposed OPG shows consistent and significant improvement on both datasets PASCAL VOC 2007 and 2012, yielding comparable performance to the state-of-the-art results.

In this paper, based on sampled-data control method, we address the stabilization problem for a general class of linear continuous-time fractional systems whose solution contains the Mittag-Leffler function that does not obey the basic exponentiation identity. By considering the infinite memory and hereditary characteristics of fractional-order calculus, we propose a sampled-data controller that guarantees the resulting closed-loop system to be asymptotically stable. Simulation examples are presented to demonstrate the effectiveness of the proposed controller and verify the established results.

Spatially varying exposure (SVE) is a promising choice for high-dynamic-range (HDR) imaging (HDRI). The SVE-based HDRI, which is called single-shot HDRI, is an efficient solution to avoid ghosting artifacts. However, it is very challenging to restore a full-resolution HDR image from a real-world image with SVE because: a) only one-third of pixels with varying exposures are captured by camera in a Bayer pattern, b) some of the captured pixels are over- and under-exposed. For the former challenge, a spatially varying convolution (SVC) is designed to process the Bayer images carried with varying exposures. For the latter one, an exposure-guidance method is proposed against the interference from over- and under-exposed pixels. Finally, a joint demosaicing and HDRI deep learning framework is formalized to include the two novel components and to realize an end-to-end single-shot HDRI. Experiments indicate that the proposed end-to-end framework avoids the problem of cumulative errors and surpasses the related state-of-the-art methods. Related codes and datasets will be provided athttps://github.com/yilun-xu/SVEHDRI/.

In this article, we investigate the distributed resilient observers-based decentralized adaptive control problem for cyber-physical systems (CPSs) with time-varying reference trajectory under denial-of-service (DoS) attacks. The considered CPSs are modeled as a class of nonlinear multi-input uncertain multiagent systems, which can be used to model an AC microgrid system consisting of distributed generators. When the communication to a subsystem from one of its neighbors is attacked by a DoS attack, the transmitted information is unavailable and the existing distributed adaptive methods used to estimate the bound of the n th-order derivative of the reference trajectory become nonapplicable. To overcome this difficulty, we first design a new distributed estimator for each subsystem to ensure that the magnitude of the state of the estimator is larger than the bound of the n th-order derivative of the reference trajectory after a finite time. By employing the estimator state, a distributed observer with a switching mechanism is proposed. Then, a new block backstepping-based decentralized adaptive controller is developed. Based on the DoS communication duration property, convex design conditions of observer parameters are derived with the Lebesgue integral theory and the average dwell time method. It is proved that the output tracking errors will approach a compact set with the developed method. Finally, the design method is successfully applied to show the effectiveness of the proposed method to solve the power sharing problem for AC microgrids.

In this article, we study the distributed resilient cooperative control problem for directed networked Lagrangian systems under denial-of-service (DoS) attacks. The DoS attacks will block the communication channels between the agents. Compared with the existing methods for the linear networked systems, the considered nonlinear networked Lagrangian systems with asymmetric channels under DoS attacks are more challenging and still not well explored. In order to solve this problem, a novel resilient cooperative control scheme is proposed by using the sampling control approach. Sufficient conditions are first derived in the absence of DoS attacks according to a multidimensional small-gain scheme. Then, in the presence of DoS attacks, the proposed resilient scheme works in a switching manner. Inspired by multidimensional small-gain techniques, the Lyapunov approach is used to analyze the closed-loop system, which enables us to establish sufficient stability conditions for the control gains in terms of the duration and frequency of the DoS attacks.

In this article, we consider the resilience problem in the presence of communication faults encountered in distributed secondary voltage and frequency control of an islanded alternating current microgrid. Such faults include the partial failure of communication links and some classes of data manipulation attacks. This practical and important yet challenging issue has been taken into limited consideration by existing approaches, which commonly assume that the measurement or communication between the distributed generations (DGs) is ideal or satisfies some restrictive assumptions. To achieve communication resilience, a novel adaptive observer is first proposed for each individual DG to estimate the desired reference voltage and frequency under unknown communication faults. Then, to guarantee the stability of the closed-loop system, voltage and frequency restoration, and accurate power sharing regardless of unknown communication faults, sufficient conditions are derived. Some simulation results are presented to verify the effectiveness of the proposed secondary control approach.

In this article, we consider the formation tracking problem of nonholonomic multiagent systems only using relative bearing measurements between the agents. Such a practical and important yet challenging issue has been taken into limited consideration by existing approaches, which usually requires additional measurements such as relative positions. The contributions of this article are two-fold. First, a fully distributed reference velocity estimator is proposed. Under the proposed adaptive estimator, each agent can estimate the time-varying reference velocity asymptotically. Second, an input-to-state stable controller is designed according to the bearing rigid theory. Under the proposed controller, the formation with bearing-only constraints can be achieved. Finally, the proposed scheme is demonstrated and its effectiveness is verified by presenting some simulation and experimental tests.

In this article, under directed graphs, an adaptive consensus tracking control scheme is proposed for a class of nonlinear multiagent systems with completely unknown control coefficients. Unlike the existing results, here, each agent is allowed to have multiple unknown nonidentical control directions, and continuous communication between neighboring agents is not needed. For each agent, we design a group of novel Nussbaum functions and construct a monotonously increasing sequence in which the effects of our Nussbaum functions reinforce rather than counteract each other. With these efforts, the obstacle caused by the unknown control directions is successfully circumvented. Moreover, an event-triggering mechanism is introduced to determine the time instants for communication, which considerably reduces the communication burden. It is shown that all closed-loop signals are globally uniformly bounded and the tracking errors can converge to an arbitrarily small residual set. Simulation results illustrate the effectiveness of the proposed scheme.

A wide range of cyber–physical systems can be modeled as Euler–Lagrange dynamics, whose inherent nonlinearities bring additional difficulties in the design and analysis of controllers for such systems. The objective of this article is to solve the jamming-resilient synchronization control problem of networked Lagrangian systems subject to unknown external disturbances, with the quantized sampling data. Under a jamming attack, a normal communication channel will be blocked with unknown frequencies and intervals. To achieve jamming attack resilience, a robust adaptive controller is proposed. Then a novel auxiliary system is designed for each subsystem over a directed network. By using the quantized sampling data from each subsystem and its neighbors, the jamming attack can be handled by an embedded single-integral subsystem. Sufficient conditions that are independent of the attack parameters are derived to guarantee the global stability of the closed-loop system. Compared with existing jamming-resilient methods, the proposed approach is concise and resilient to any jamming attacks with bounded frequencies and durations.

Currently, a nonconvex economic dispatch problem is one of the research focuses in the field of smart grid (SG). A variety of algorithms are developed to solve it. However, these algorithms are prone to suffering from high computation cost and slow convergence rate, which creates an inevitable gap between theoretical analysis and practical real-time operations. In this article, we aim at providing an ensemble deep-learning-based approach to tackle such a challenging issue. First, a novel ensemble method is presented to explore the ground truth of nonconvex economic dispatch problems. Second, considering the time-varying total load demand, cost coefficients, and dispatchability of all generation units in a practical SG system as the features, a new deep neural network structure is proposed to learn the complex mapping from instant features to an optimal nonconvex economic dispatch solution. If such a mapping is well approximated by the designed deep neural network, no significant effort is required to solve a new economic dispatch problem, and the solution is obtained on the scale of milliseconds. Third, analyzing that a single deep neural network may be weak to a small part of the mapping space of the nonconvex economic dispatch problem, we further present an ensemble of multiple parallel deep neural networks trained sequentially with a simplified Adaboost.R2 algorithm. Finally, case studies reveal that the proposed approach achieves orders of magnitude speedup in computational time while guaranteeing similar or better performance on minimizing the overall generation cost compared to the state-of-the-art nonconvex economic dispatch algorithms.

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

A swarming behavior problem is investigated in this article for heterogeneous uncertain agents with cooperation-competition interactions. In such a problem, the agents are described by second-order continuous systems with different intrinsic nonlinear terms, which satisfies the "linearity-in-parameters" condition, and the agents' models are coupled together through a distributed protocol containing the information of competitive neighbors. Then, for four different types of cooperation-competition networks, a distributed Lyapunov-based redesign approach is proposed for the heterogeneous uncertain agents, where the distributed controller and the estimation laws of unknown parameters are obtained. Under their joint actions, the heterogeneous uncertain multiagent system can achieve distributed stabilization for structurally unbalanced networks and output bipartite consensus for structurally balanced networks. In particular, the concept of coherent networks is proposed for structurally unbalanced directed networks, which is beneficial to the design of distributed controllers. Finally, four illustrative examples are given to show the effectiveness of the designed distributed controller.

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

Existing schemes for systems with state constraints require the bounds of the constraints for controller design and may result in conservativeness or even become invalid when they are applied to systems without such constraints. In this paper, we study the problem of event-triggered control for a class of uncertain nonlinear systems by considering the cases with or without state constraints in a unified manner. By introducing a new universal-constrained function and using certain transformation techniques, the original-constrained system is converted into an equivalent totally unconstrained one. Then, an event-triggered adaptive neural-network (NN) controller is designed to stabilize the unconstrained system and compensate for the control sampling errors caused by event-triggered transmission of control signals. Unlike some existing control schemes developed for systems with state constraints, which need to check whether each virtual control meets certain feasibility conditions at every design step, our proposed unified method enables such feasibility conditions to be relaxed. In addition, a suitable event-triggering rule is designed to determine when to transmit control signals. It is theoretically shown that the designed controller can achieve the desired tracking ability and reduce the communication burden from the controller to the actuator at the same time. Simulation verification also confirms the effectiveness of the proposed approach.

In this article, we investigate the distributed resilient control problem for a class of cyber-physical systems with communication delays under denial-of-service (DoS) attacks. In contrast to the previous DoS attacks results based on multiagent systems (MASs), a new distributed resilient control approach is proposed for more general heterogeneous linear MASs with nonuniform communication delays. Two types of sampled-based observers are, respectively, proposed. Namely, adaptive distributed observers are designed by introducing a buffer mechanism to eliminate the heterogeneous behavior caused by communication delays while adaptive distributed resilient observers are designed by introducing resilient mechanisms to resist the DoS attacks. Furthermore, a time-varying sampling period sequence is provided to prevent the attacker from identifying the sampling period of the system. Based on the developed resilient observers, a controller is developed. It is proved that the considered problem can be solved by the developed method. Finally, a numerical example is given to illustrate the effectiveness of the obtained result.

Key nodes are the nodes connected with a given number of external source controllers that result in minimal control cost. Finding such a subset of nodes is a challenging task since it impossible to list and evaluate all possible solutions unless the network is small. In this paper, we approximately solve this problem by proposing three algorithms step by step. By relaxing the Boolean constraints in the original optimization model, a convex problem is obtained. Then inexact alternating direction method of multipliers (IADMMs) is proposed and convergence property is theoretically established. Based on the degree distribution, an extension method named degree-based IADMM (D-IADMM) is proposed such that key nodes are pinpointed. In addition, with the technique of local optimization employed on the results of D-IADMM, we also develop LD-IADMM and the performance is greatly improved. The effectiveness of the proposed algorithms is validated on different networks ranging from Erdős-Rényi networks and scale-free networks to some real-life networks.

Optimal control of networks is to minimize the cost function of a network in a dynamical process with an optimal control strategy. For the time-invariant linear systems, · x(t)=A x(t)+B u(t) , and the traditional linear quadratic regulator (LQR), which minimizes a quadratic cost function, has been well established given both the adjacency matrix A and the control input matrix B . However, this conventional approach is not applicable when we have the freedom to design B . In this article, we investigate the situation when the input matrix B is a variable to be designed to reduce the control cost. First, the problem is formulated and we establish an equivalent expression of the quadratic cost function with respect to B , which is difficult to obtain within the traditional theoretical framework as it requires obtaining an explicit solution of a Riccati differential equation (RDE). Next, we derive the gradient of the quadratic cost function with respect to the matrix variable B analytically. Further, we obtain three inequalities of the cost functions, after which several possible design (optimization) problems are discussed, and algorithms based on gradient information are proposed. It is shown that the cost of controlling the LTI systems can be significantly reduced when the input matrix becomes "designable." We find that the nodes connected to input sources can be sparsely identified and they are distributed as evenly as possible in the LTI networks if one wants to control the networks with the lowest cost. Our findings help us better understand how the LTI systems should be controlled through designing the input matrix.

In this article, we address the bearing-only formation control problem of 3-D networked robotic systems with parametric uncertainties. The contributions of this article are two-fold: 1) the bearing-rigid theory is extended to solve the nonlinear robotic systems with the Euler-Lagrange-like model and 2) a novel almost global stable distributed bearing-only formation control law is proposed for the nonlinear robotic systems. Specifically, the robotic systems subject to nonholonomic constraints and dynamics are first transformed into a Euler-Lagrange-like model. By exploring the bearing-rigid graph theory, a backstepping approach is used to design the distributed formation controller. Simulations for 3-D robotics are given to demonstrate the effectiveness of the proposed control law. Compared to the distance-rigid formation control approach, the bearing-rigid approach guarantees almost global stability while naturally excluding flip ambiguities.

As a cyber-physical system (CPS), the security of microgrids (MGs) is threatened by unknown faults and cyberattacks. Most existing distributed control methods for MGs are proposed based on the assumption that secondary controllers of distributed generation units operate in normal conditions. However, the faults and attacks of the distributed control system could lead to a significant impact and consequently influence the security and stability of MGs. In this article, a distributed resilient control strategy for multiple energy storage systems (ESSs) in islanded MGs is proposed to deal with these hidden but lethal issues. By introducing an adaptive technique, a distributed resilient control method is proposed for frequency/voltage restoration, fair real power sharing, and state-of-charge balancing in MGs with multiple ESSs in abnormal condition. The stability of the proposed method is rigorously proved by Lyapunov methods. The proposed method is validated on test systems developed in OPAL-RT simulator under various cases.

This article presents a distributed consensus-based successive convex approximation (DSCA) algorithm to solve nonconvex nondifferentiable economic dispatch (ED) problems. The ED model formulated incorporates generation constraints, valve-point effects, and multiple fuel types. A perturbation technique enables the proposed DSCA to tackle such a nondifferentiable and nonconvex optimization, which paves the way to solving more complicated optimization problems that occur in practical applications. The local generation constraint is taken care by a local surrogate convex optimization directly. The global equality constraint is handled based on a consensus protocol, where the local generation-demand mismatch among all dispatchable generators (DGs) is shared in a distributed manner. As a result, the power distribution of DGs is updated, and the generation cost is minimized. Several case studies show that the proposed DSCA algorithm can achieve superior ED solutions and computational efficiency over existing nonconvex optimization algorithms.

Model drift is a challenging issue for visual object tracking. Most available approaches aim to address this issue by constructing of a stronger discriminative prediction model with the integration of multiple features. In this article, we also address the issue of model drift using multiple features, but we consider the fusion problem from a different point of view, namely, strengthening the features that are suitable for the current scenario while weakening the remaining ones. Therefore, an advanced tracker that adaptively redetermines the reliability for each feature during the tracking process is proposed. Furthermore, to correctly evaluate and redetermine these reliabilities, two different solutions, called model evaluation and numerical optimization, are proposed, and two independent trackers corresponding to these two solutions are implemented. Extensive experiments have been designed on five large datasets to validate the following: 1) The proposed tracking framework is superior for making the tracking model more robust, and 2) the two solutions proposed for redetermining the reliability for each feature are effective. As expected, the two implemented trackers do indeed improve the accuracy and robustness compared to state-of-the-art trackers. Especially on VOT2016, the proposed trackers based on model evaluation and numerical optimization achieve outstanding EAO scores (i.e.,0.453and0.428, respectively), outperforming the recently developed top trackers by a large margin.

In this article, to maximize the dimension of controllable subspace, we consider target controllability problem with maximum covered nodes set in multiplex networks. We call such an issue as maximum-cost target controllability problem. Likewise, minimum-cost target controllability problem is also introduced which is to find minimum covered node set and driver node set. To address these two issues, we first transform them into a minimum-cost maximum-flow problem based on graph theory. Then an algorithm named target minimum-cost maximum-flow (TMM) is proposed. It is shown that the proposed TMM ensures the target nodes in multiplex networks to be controlled with the minimum number of inputs as well as the maximum (minimum) number of covered nodes. Simulation results on Erdős-Rényi (ER-ER) networks, scale-free (SF-SF) networks, and real-life networks illustrate satisfactory performance of the TMM.

When a group of autonomous surface vehicles (ASVs) sail from a wide waterway to a narrow waterway, one difficulty is to keep relative formation with collision avoidance. Scaling the formation sizes with formation shapes invariant is a promising way. This article investigates such a formation scaling control problem of ASVs with uncertainties and input saturation. A novel bearing-based adaptive neural formation scaling control scheme for ASVs is developed. The main idea of this formation scheme is as follows. Choose a small number of leader ASVs based on bearing rigidity theory and program their trajectories according to the kinematics of formation scaling maneuver. Steer remaining ASVs to follow leader ASVs via adaptive neural techniques and the formation sizes can be scaled only by leaders without redesigning control inputs of followers. To deal with the uncertainties of ASVs, weights updating of neural networks is simplified into one-parameter estimation in each control channel. Auxiliary systems are introduced for each ASV to reduce the effect of limited actuator capability. It is shown that desired formation scaling maneuver of ASVs can be achieved with the proposed formation scheme if the augmented formation is infinitesimally bearing rigid. Formation errors are guaranteed to be uniformly ultimately bounded. The main advantage of our scheme over existing results is that directional, computational, and actuator constraints are satisfied simultaneously in the formation scaling control of ASVs. Simulations and comparisons are provided to illustrate the effectiveness of theoretical results.

In this article, we consider a convex optimization problem which minimizes the sum of local agents' cost functions subject to certain local constraints. Besides, both the local cost function and local constraints are only known by the local agent itself. To solve this problem, a new accelerated distributed gradient-based algorithm is proposed, which is inspired by the “momentum” phenomena in nature and aims to accelerate the convergence speed of conventional distributed gradient algorithms. Sufficient conditions for the stepsizes and the acceleration gains are derived to ensure the convergence of the proposed algorithm. Furthermore, based on this proposed fast distributed algorithm, a new decentralized approach is proposed to solve economic dispatch problem, especially for a large-scale power system. Based on the idea of virtual agent, it is proved that this decentralized algorithm is equivalent to the original fast distributed gradient method. Several case studies implemented on IEEE 30-bus, IEEE 118-bus power systems, and a large-scale power system consisting of 1000 generators are conducted to validate the proposed method.

In this article, we consider controlling a class of single-input–single-output (SISO) commensurate fractional-order nonlinear systems with parametric uncertainty and external disturbance. Based on the backstepping approach, an adaptive controller is proposed with adaptive laws that are used to estimate the unknown system parameters and the bound of unknown disturbance. Instead of using discontinuous functions, such as the sign function, an auxiliary function is employed to obtain a smooth control input that is still able to achieve perfect tracking in the presence of bounded disturbances. Indeed, the global boundedness of all closed-loop signals and asymptotic perfect tracking of fractional-order system output to a given reference trajectory are proved by using fractional directed Lyapunov method. To verify the effectiveness of the proposed control method, simulation examples are presented.

In this article, we consider the distributed optimal consensus problem under nominal and nonfragile cases for a class of minimum-phase uncertain nonlinear systems with unity-relative degree and disturbances generated by an external autonomous system. The involved cost function is the sum of all local cost functions associated with each individual agent. Two different edge-based distributed adaptive algorithms utilizing the internal model principle are designed to solve the problem in a fully distributed manner. Graph theory, nonsmooth analysis, convex analysis, and the Lyapunov theory are employed to show that the proposed algorithms converge accurately to the optimal solution of the considered problem. Finally, an example involving the dynamics of a Lorenz-type system is provided to demonstrate the effectiveness of the obtained results.

In this paper, the leader-following consensus control problem is studied for fractional-order nonlinear multiagent systems in the presence of system uncertainties and unknown external disturbances. An adaptive distributed control scheme is proposed for each agent with a local controller that contains a filter and estimators for estimating the bounds of the fractional-order derivatives of desired trajectory. All the control signals resulted from the control scheme are smooth and they are still able to achieve asymptotic output consensus tracking while ensuring global boundedness of all the closed-loop signals. Simulation results are provided to illustrate the effectiveness of the proposed control protocol and verify the obtained results.

In this paper, we study the adaptive tracking control problem for a class of uncertain nonlinear systems with input quantization. Different from the existing results, we propose a new quantizer with saturated quantization levels motivated by the saturation property of practical actuators and sensors. With this new quantizer, we know the exact number and values of the quantization levels in advance, regardless of the magnitude of the designed control signal. Thus, we only need to code these quantization levels accordingly such that less network resources are consumed. It is shown that the proposed control scheme guarantees that all the closed-loop signals are globally bounded and the tracking error converges towards a known compact set.

Cyber-physical systems (CPSs) are often complexly nonlinear and uncertain. This paper investigates the adaptive output-feedback control problem for CPSs subject to intermittent denial-of-service (DoS) attacks. The considered CPSs are modeled as a class of uncertain second-order strict-feedback nonlinear systems. When a DoS attack is active, the output signal becomes unavailable. To overcome this diffculty, an adaptive observer is constructed. Based on an average-dwell-time (ADT) method incorporated by frequency and duration properties of DoS attacks, convex design conditions of controller parameters are derived by solving a set of linear matrix inequalities (LMI). The proposed controller guarantees that all closed-loop signals remain globally bounded. An illustrative example is included to validate the theoretical results.

This paper considers the distributed economic dispatch problem in power systems. A novel constrained consensus-based iterative algorithm is proposed to cooperatively search the optimal incremental cost. The algorithm is carried out by alternately executing a continuous-time finite-time consensus algorithm and employing local projection operations only when consensus variables reach agreement. At each iteration, the initial values of consensus variables are restored locally based on the deviation between the consensus value in the last iteration and its projection on local constraints. Compared to existing methods, our method only needs a single consensus algorithm and does not require continuous projection operations. Thus each node in our algorithm only transmits a single variable to neighbours and the search time of the optimal solution is significantly reduced. Convergence analysis is given, and numerical examples are presented to show the effectiveness of our method with comparison to some existing results.

In weakly supervised object detection, most existing approaches are proposed for images. Without box-level annotations, these methods cannot accurately locate objects. Considering an object may show different motion from its surrounding objects or background, we leverage motion information to improve the detection accuracy. However, the motion pattern of an object is complex. Different parts of an object may have different motion patterns, which poses challenges in exploring motion information for object localization. Directly using motion information may degrade the localization performance. To overcome these issues, we propose a Motion Context Network (MC-Net) in this letter. Our method generates motion context features by exploiting neighborhood motion correlation information on moving regions. These motion context features are then incorporated with image information to improve the detection accuracy. Furthermore, we propose a temporal aggregation module, which aggregates features across frames to enhance the feature representation at the current frame. Experiments are carried out on ImageNet VID, which shows that our MC-Net significantly improves the performance of the image based baseline method (37.4% mAP v.s. 29.8% mAP).

In this paper, we investigate the distributed secure state estimation and control problems for interconnected cyber-physical systems (CPSs) with some sensors being attacked. First, by exploring the distinct properties of the unidentifiable attacks to a CPS, an explicit sufficient condition that the secure state estimation problem can be solvable is established. Then distributed preselectors and observers are presented to solve the secure state estimation problems. Furthermore, with the obtained state estimation, fractional dynamic surface-based distributed secure controllers are also proposed for the secure control problem. Theoretical analysis shows that, with the proposed distributed secure observers and controllers, not only the state of the CPS under attacks can be obtained in a given finite time but also the dynamic surface can be achieved and maintained in a finite time. Finally, the results are applied to an islanded micro-grid system as an illustration, which verifies the effectiveness of the proposed schemes.

In this paper, a novel adaptive neural network (NN) control scheme is proposed for a class of nonlinear systems with unknown control direction. By introducing some differentiable functions and high-order Lyapunov functions, the obstacle caused by unknown control direction in NN control is successfully circumvented and all closed-loop signals are shown to be uniformly bounded up to infinite time. Meanwhile, by introducing an error transformation technique, it is rigorously proved that the argument of the unknown nonlinearities remains within a compact set which can be explicitly calculated a priori, making the NN approximation always valid. Moreover, with the aid of a bound estimation approach, we effectively compress the impact of approximation errors and external disturbances and steer the tracking error into a predefined small residual set. Simulation results illustrate the effectiveness of the proposed scheme.

This paper revisits the decentralized stabilization problem for a class of interconnected nonlinear systems subject to strongly coupled uncertain interactions. As a main contribution, we show that, without any priori required restrictive nonlinear growth conditions, a C1condition of the nonlinear interactions is sufficient to derive a finite-time convergent decentralized stabilizing control law under a semi-global control objective. Another new feature is the adoption of a novel one step nonsmooth synthesis method which leads to a simple form of stabilizing control law with easily implementable gain tuning mechanisms. A numerical example and its simulation verification are provided to illustrate the simplicity and effectiveness of the proposed method.

This paper investigates a novel nonrecursive tracking control law design for a class of nonlinear systems via dynamic output feedback. As the main contribution of this paper, a global nonrecursive tracking design procedure is first proposed to render a simple construction of a realizable output feedback control law, whose gain selections follow the conventional pole placement approach while the stability margin can be guaranteed via a sufficiently large scaling gain. By introducing a Lyapunov function which neglects the virtual controllers in essence, rigorous analysis is presented to ensure the global stability. In addition, finite-time and asymptotical tracking results can now be achieved within the same design framework whereas the tunable homogeneous degree plays as a key role. As another contribution, by proposing a saturated dynamic compensator, a less ambitious but practical control objective, namely semiglobal stability is achieved of the closed-loop system to relax the requirement of the restrictive growth conditions for global control design. Taking consideration of the case when system is subject to mismatched disturbances, a unified design and stability analysis framework shows that the practical tracking result can also be realized. A numerical example is provided to illustrate the effectiveness of the nonrecursive design and the simplicity of the proposed tracking control algorithm.

We equip an ultra-wideband (UWB) node and a 2D LiDAR sensor a.k.a. 2D laser rangefinder on a mobile robot, and place UWB beacon nodes at unknown locations in an unknown environment. All UWB nodes can do ranging with each other thus forming a cooperative sensor network. We propose to fuse the peer-to-peer ranges measured between UWB nodes and laser scanning information, i.e., range measured between robot and nearby objects/obstacles, for simultaneous localization of the robot, all UWB beacons and LiDAR mapping. The fusion is inspired by two facts: 1) LiDAR may improve UWB-only localization accuracy as it gives a more precise and comprehensive picture of the surrounding environment; 2) on the other hand, UWB ranging measurements may remove the error accumulated in the LiDAR-based SLAM algorithm. Our experiments demonstrate that UWB/LiDAR fusion enables drift-free SLAM in real-time based on ranging measurements only.

This paper presents a decentralized secondary control scheme to solve the voltage restoration and current sharing problem in islanded DC microgrid (MG) systems. The existing solutions to this problem are either centralized control or distributed control based approaches. For these methods, communication and information exchange is inevitable. In order to improve the system robustness and reduce the system implementation cost, a decentralized communication-free leaky integral control is proposed in this paper, which is able to realize the same control goals without any communication. The stability of overall system together with proposed decentralized controller is analysed. A test DC MG system is built in Matlab Simulink to validate the effectiveness of proposed control method.

Loop closure detection (LCD) is a crucial technique for robots, which can correct accumulated localization errors after long time explorations. In this paper, we propose a robust LCD algorithm based on Bag of SuperPoints and graph verification. The system first extracts interest points and feature descriptors using the SuperPoint neural network. Then a visual vocabulary is trained in an incremental and self-supervised manner considering the relations between consecutive training images. Finally, a topological graph is constructed using matched feature points to verify candidate loop closures obtained by a Bag-of-Words (BoW) framework. Comparative experiments with state-of-the-art LCD algorithms on several typical datasets have been carried out. The results demonstrate that our proposed graph verification method can significantly improve the accuracy of image matching and the overall LCD approach outperforms existing methods.

The minimum cost control problem is one of the most important issues in controlling complex networks. Different from the previous works, in this paper, we consider the minimum cost control problem with selectable inputs by adopting the cost function summed over both quadratic terms of system input and system state with a weighting factor. To address such an issue, the orthonormal-constraint-based projected gradient method is proposed to determine the input matrix iteratively. Convergence of the proposed algorithm is established. Extensive simulation results are carried out to show the effectiveness of the proposed algorithm. We also investigate what kinds of nodes are most important for minimizing average control cost in directed stems/circles and small networks through simulation studies. The presented results in this paper bring meaningful physical insights in controlling the directed networks from an energy point of view.

In this paper, based on differential inclusions and the saddle point dynamics, a novel second-order continuous-time algorithm is proposed to solve the optimal resource allocation problem in power systems. The considered cost function is the sum of all local cost functions with a set of affine equality demand constraints and an inequality constraint on generating capacity of the generator. In virtue of nonsmooth analysis, geometric graph theory, and Lyapunov stability theory, all generators achieve consensus on the Lagrange multipliers associated with a set of affine equality constraints while the proposed algorithm converges exponentially to the optimal solution of the resource allocation problem starting from any initial states over an undirected and connected graph. Moreover, the obtained results can be further extended to the optimal resource allocation problem in case of switching communication topologies. Finally, two numerical examples involving a smart grid system composed of five generators and the IEEE 30-bus system demonstrate the effectiveness and the performance of the theoretical results.

In this paper, reciprocal collision avoidance is studied for nonholonomic mobile robots to achieve an efficient navigation. Two strategies are proposed to respectively adjust the linear and angular velocities so that a collision-free navigation can be achieved. By characterizing the collision-free navigation as a set of changing ratios for linear velocities, it is shown that collision can be avoided if the changing ratio of linear velocities is inside this set. Moreover, the strategy of the adjusting angular velocities is established following TTC-based method. With the combination of these two strategies, a simulation is done for four robots crossing the intersection, which shows the effectiveness of the proposed method.

In this paper, we present a new approach, named as dynamic window approach with collision suppression cone (DWA-CSC), to handle the issue of a robot avoiding moving obstacles in a partially known dynamic environment. The concept of collision suppression cone is firstly introduced to define a probable collision area. When moving obstacles approach this area, the proposed DWA-CSC will be activated to allow the robot avoiding the obstacles smoothly and thus preventing collision with them by suppressing its motion direction and velocity, just like the way of obstacle avoidance done by human beings. Various simulation results show that the proposed DWA-CSC approach indeed achieve the avoidance of moving obstacles safely and smoothly.

Tone mapping operators (TMOs) and inverse TMOs (iTMOs) are important for scalable coding of high dynamic range (HDR) images. Because of the high nonlinearity of local TMOs, it is very difficult to estimate the iTMO accurately for a local TMO. In this letter, we present a two-layer local iTMO estimation algorithm using an edge-preserving decomposition technique. The low dynamic range (LDR) image is first linearized and then decomposed into a base layer and a detail layer via a fast edge-preserving decomposition method. The base layer of the HDR image is generated by subtracting the LDR detail layer from the HDR image. An iTMO function is finally estimated by solving a novel quadratic optimization problem formulated on the pair of base layers rather than the pair of HDR and LDR images as in existing methods. Experimental results show that the proposed two-layer iTMO can recover the HDR accurately so that it is possible to use these local TMOs in scalable HDR image coding schemes.

In this paper, we investigate the adaptive consensus control for a class of high-order nonlinear systems with different unknown control directions where communications among the agents are represented by a directed graph. Based on backstepping technique, a fully distributed adaptive control approach is proposed without using global information of the topology. Meanwhile, a novel Nussbaum-type function is proposed to address the consensus control with unknown control directions. It is proved that boundedness of all closed-loop signals and asymptotically consensus tracking for all the agents' outputs are ensured. In simulation studies, a numerical example is illustrated to show the effectiveness of the control scheme.

In this paper, a new hierarchical decentralized optimization architecture is proposed to solve the economic dispatch problem for a large-scale power system. Conventionally, such a problem is solved in a centralized way, which is usually inflexible and costly in computation. In contrast to centralized algorithms, in this paper we decompose the centralized problem into local problems. Each local generator only solves its own problem iteratively, based on its own cost function and generation constraint. An extra coordinator agent is employed to coordinate all the local generator agents. Besides, it also takes responsibility to handle the global demand supply constraint based on a newly proposed concept named virtual agent. In this way, different from existing distributed algorithms, the global demand supply constraint and local generation constraints are handled separately, which would greatly reduce the computational complexity. In addition, as only local individual estimate is exchanged between the local agent and the coordinator agent, the communication burden is reduced and the information privacy is also protected. It is theoretically shown that under proposed hierarchical decentralized optimization architecture, each local generator agent can obtain the optimal solution in a decentralized fashion. Several case studies implemented on the IEEE 30-bus and the IEEE 118-bus are discussed and tested to validate the proposed method.

Multiscale exposure fusion is a fast approach to fuse several differently exposed images captured at the same high dynamic range (HDR) scene into a high-quality low-dynamic range (LDR) image. The fused image is expected to include all details of the input images. However the details in the brightest and darkest regions are usually not well preserved. Adding details that are extracted from the input images to the fused image is an efficient approach to overcome the problem. In this paper a new gradient domain weighted least square based image smoothing algorithm is proposed to extract the details in the brightest and darkest regions of the HDR scene. The extracted details are then added to an image that is produced using an edge-preserving smoothing pyramid based multiscale exposure fusion algorithm. Experimental results show that the proposed detail enhanced exposure fusion algorithm can preserve details in saturated regions especially the brightest regions better than the state-of-the-art multiscale exposure fusion algorithms.

This paper is devoted to distributed adaptive containment control for a class of nonlinear multiagent systems with input quantization. By employing a matrix factorization and a novel matrix normalization technique, some assumptions involving control gain matrices in existing results are relaxed. By fusing the techniques of sliding mode control and backstepping control, a two-step design method is proposed to construct controllers and, with the aid of neural networks, all system nonlinearities are allowed to be unknown. Moreover, a linear time-varying model and a similarity transformation are introduced to circumvent the obstacle brought by quantization, and the controllers need no information about the quantizer parameters. The proposed scheme is able to ensure the boundedness of all closed-loop signals and steer the containment errors into an arbitrarily small residual set. The simulation results illustrate the effectiveness of the scheme.

Multi-scale exposure fusion is an efficient way to fuse differently exposed low dynamic range (LDR) images of a high dynamic range (HDR) scene into a high quality LDR image directly. It can produce images with higher quality than single-scale exposure fusion, but has a risk of producing halo artifacts and cannot preserve details in brightest or darkest regions well in the fused image. In this paper, an edge-preserving smoothing pyramid is introduced for the multi-scale exposure fusion. Benefiting from the edge-preserving property of the filter used in the algorithm, the details in the brightest/darkest regions are preserved well and no halo artifacts are produced in the fused image. The experimental results prove that the proposed algorithm produces better fused images than the state-of-the-art algorithms both qualitatively and quantitatively.

Controlling directed networks with minimum cost has become an emerging branch in the areas of complex networks and control recently. In this paper, we focus on this minimum cost control problem subject to two types of boundary constraints, namely, trace boundary constraint and orthonormal boundary constraint on the input matrices. First, the minimum cost control problem is formulated as an optimization model for each type of boundary constraint. Next, two iterative algorithms, named as trace-constraint-based projected gradient method and orthonormal-constraint-based projected gradient method, are proposed to solve the optimal problem, respectively. Then, convergence properties of both algorithms are established. Finally, extensive simulation results show the effectiveness of our methods based on detailed comparisons between the two boundary conditions. We believe the results reveal some interesting physical insights for the optimal control of directed networks.

In this paper, we propose a new distributed event-trigger consensus protocol for linear multiagent systems with external disturbances. Two consensus problems are considered: one is a leader-follower case and the other is a nonleader case. Different from the existing results, our proposed scheme enables each agent to decide when to transmit its state signals to its neighbors such that continuous communication between neighboring agents is avoided. Clearly, this can largely decrease the communication burden of the whole communication network. Besides, since the control signal for each agent is discontinuous because of the event-triggering mechanism, the existence of a solution for the closed-loop system in the classical sense may not be guaranteed. To solve this problem, we employ a nonsmooth analysis technique including differential inclusion and Filippov solution. Through nonsmooth Lyapunov analysis, it is shown that uniformly bounded consensus results are derived and the bound of the consensus error is adjustable by choosing suitable design parameters.

Multi-scale exposure fusion is an effective image enhancement technique for a high dynamic range (HDR) scene. In this paper, a new multi-scale exposure fusion algorithm is proposed to merge differently exposed low dynamic range (LDR) images by using the weighted guided image filter to smooth the Gaussian pyramids of weight maps for all the LDR images. Details in the brightest and darkest regions of the HDR scene are preserved better by the proposed algorithm without relative brightness change in the fused image. In addition, a new weighted structure tensor is introduced to the differently exposed images and it is adopted to design a detail extraction component for the proposed fusion algorithm, such that users are allowed to manipulate fine details in the enhanced image according to their preference. The proposed multi-scale exposure fusion algorithm is also applied to design a simple single image brightening algorithm for both low-light imaging and back-light imaging.

In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.

This paper is concerned with the problem of adaptive tracking control for a class of uncertain nonlinear systems with nonsymmetric input saturation and immeasurable states. The radial basis function of neural network (NN) is employed to approximate unknown functions, and an NN state observer is designed to estimate the immeasurable states. To analyze the effect of input saturation, an auxiliary system is employed. By the aid of adaptive backstepping technique, an adaptive tracking control approach is developed. Under the proposed adaptive tracking controller, the boundedness of all the signals in the closed-loop system is achieved. Moreover, distinct from most of the existing references, the tracking error can be bounded by an explicit function of design parameters and saturation input error. Finally, an example is given to show the effectiveness of the proposed method.

In this paper, we propose a discriminative and robust appearance model based on features extracted from a random partition image hashing algorithm to account for severe occlusion and disappearance. We divide the original image into multiple sub-blocks with random positions and scales. Hash functions are used to map blocks into compact binary codes, with which more effective target matching can be achieved. The tracking task is then formulated by producing a confidence map for the target and background, and obtaining the best samples using maximum a posteriori estimate. Experimental results demonstrate that our tracker can achieve more accurate tracking results in situations of occlusion, out-of-view, and violent motion blur when compared with most of state-of-the-art competing algorithms. Besides, the proposed tracking algorithm is able to run in real time.

This paper presents a distributed voltage unbalance compensation method for an islanded microgrid (MG) system. By using hierarchical control concept, we design a distributed compensation scheme in the secondary control layer, while the traditional inverter control methods including voltage and current PR control, droop control, virtual impedance are implemented in the primary layer. Different from most existing centralized compensation methods, no specified individual centralized secondary controller exists. We decompose the traditional centralized controller into several local secondary controllers. A novel distributed PI controller with negative sequence current feedback is designed in each secondary controller. By allowing them to exchange information with their neighboring controllers respectively, the unbalanced voltage in the sensitive load bus (SLB) can be compensated by all the distributed generators (DGs) cooperatively. In addition, the compensation effort of each DG is designed to be proportional to their power ratings respectively. An islanded microgrid system consisting of 4 DGs is built in MATLAB to validate the proposed method.

In this paper, we present a distributed economic dispatch strategy for a large-scale power system. At first, we treat each generator and load in the grid as an "agent". By decomposing the centralized optimization into optimizations at local agents, a scheme is proposed for each agent to iteratively estimate a solution of the optimization problem in a distributed manner. Due to the large number of the agents, the agents are sorted into several clusters and each cluster has a leader to communicate with the leaders of its neighboring clusters. The agents in the same cluster can conduct local optimization and communicate with its neighboring agents in parallel. After that, the leader agents of each cluster exchange their information simultaneously. It is shown that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. Compared to our previous work in [14], where the leader agent in each cluster conducts the optimization in a sequential way, the proposed scheme in this paper allows them communicate and conduct optimization simultaneously, which greatly improves the algorithm efficiency. A case study implemented on IEEE 30-bus power system are discussed and tested to validate the proposed method.

The paper is concerned with the multi-robot leader-following problem in the presence of frequent dropouts in vision detection. In many scenarios, for instance a structured environment, it is inevitable to experience outage of vision detection due to reasons such as the target moving out of view, vision occlusion, motion blurring, etc. The paper proposes a Bayesian trajectory estimation based leader-following approach that can offer accurate path following given intermittent vision observations. The follower robot estimates the trajectory of the leader robot based on the noise-corrupted odometry information of both robots, and inter-robot relative observations based on detection of fiducial markers using an RGBD camera. A linear trajectory-following control method is employed to track a historical pose of the leader robot on the estimated trajectory. Results are obtained based on evaluating the proposed leader-following approach in tests with a zig-zag shaped trajectory and with a trajectory that contains sharp turns.

In this paper, a distributed adaptive control scheme is proposed for nth order multi-agent systems with pure integrator type of subsystem dynamics. It is assumed that the information transmission condition among different subsystems is represented by a fixed, balanced and weakly connected directed graph. The full knowledge of desired trajectory is allowed totally unknown by part of the subsystems, except that its first nth derivatives are bounded. It is shown that the globally uniform boundedness of all closed-loop signals and asymptotically consensus tracking for all the subsystem outputs can be guaranteed.

In this paper, we deal with the problem of image-based visual tracking for mobile robots. It is noted that the presence of actuator dynamics and the unknown target motion increases the complexity of the system model and makes the design of the controller more difficult. To solve this problem, we propose an adaptive control approach via utilizing backstepping technique and extended state observer (ESO). For the controller design, the adaptive technique is employed to estimate the bound of the target motion and the adaptive law is derived from the Lyapunov stability theory. We also adopt two ESOs to estimate and compensate for the disturbances affecting the mobile robot dynamics and the wheel actuator dynamics. It is shown that the proposed adaptive controller guarantees the boundness of all the signals in the closed-loop system and enables the tracking errors to exponentially converge to a compact set which is adjustable.

Several major technical obstacles appear when moving toward more electric aircraft (MEA) architecture. First, there has been an increasing number of power electronic components used in aircraft power systems, leading to the modelling complexity. Second, the number of variables for system modelling increase significantly, leading to high computational complexity. To overcome these difficulties, this report proposes (1) a mathematical model of hybrid AC/DC electrical power systems for MEA architecture; and (2) a distributed power allocation and scheduling strategy based on the Lagrangian relaxation to reduce the computational complexity. Simulation results show that the distributed optimization approach is able to achieve good performance whilst reducing the computation complexity when the scale of an electrical power system increases.

Economic operation is a major concern for microgrids. Conventionally, economic dispatch of distributed generations (DGs) are solved by centralized control with optimization algorithms or distributed control with consensus algorithm. To improve the reliability, scalability and economy of microgrids, a fully decentralized economic power sharing strategy is proposed in this paper. The proposed method is based on frequency/incremental cost droop (f/IC) characteristics and incremental cost (IC) functions of DGs. ICs of DGs reach equality with the convergence of system frequency. Power dispatch of each DG is automatically achieved based on its relevant incremental cost function. Therefore, by using this method, the incremental cost of each DG will reach equality autonomously and the total operating cost can be optimized without any communication or central controllers. Simulation platform of an autonomous AC MG with three DGs is built in Matlab/Simulink to verify the effectiveness of the proposed method.

Event-triggered control (ETC) is a control strategy which can effectively reduce communication traffic in control networks. In the case where communication resources are scarce, ETC plays an important role in updating and communicating data. When network-induced delays are involved, two unsynchronized phenomena will appear if the existing ETC strategy, designed for networked control systems (NCSs) free of delays, is adopted. This paper deals with the ETC problem for NCS with delays existing in both sensor-to-controller and controller-to-actuator channels. A new predictive ETC strategy is proposed to solve both unsynchronized problems. It is shown that the stability of the resulting closed-loop system can be guaranteed under such an ETC strategy. Finally, both simulation studies and experimental tests are carried out to illustrate the proposed technique and verify its effectiveness.

This paper presents a distributed secondary control scheme for voltage unbalance compensation in the islanded microgrid (MG) systems. Conventionally, the voltage unbalance compensation is realized in a centralized way, which has certain intrinsic disadvantages such as poor fault tolerance ability and higher computational and communication cost. In order to overcome these drawbacks, a distributed secondary control scheme for voltage unbalance compensation is proposed. A finite-time average consensus algorithm is used for each local controller to obtain global information. The proposed scheme not only achieves similar voltage unbalance compensation performance as the centralized one but also shares the compensation efforts among local compensators dynamically in a distributed fashion. In addition, our proposed scheme has fault tolerance ability in the sense that when some compensators fail to work, the rest compensators can still ensure balanced voltage output in the sensitive load bus (SLB). Simulation results are presented to validate our proposed scheme.

Detail enhancement is required by many problems in the fields of image processing and computational photography. Existing detail enhancement algorithms first decompose a source image into a base layer and a detail layer via an edge-preserving smoothing algorithm, and then amplify the detail layer to produce a detail-enhanced image. In this letter, we propose a newL0norm based detail enhancement algorithm which generates the detail-enhanced image directly. The proposed algorithm preserves sharp edges better than an existingL0norm based algorithm. Experimental results show that the proposed algorithm reduces color distortion in the detail-enhanced image, especially around sharp edges.

This paper presents a distributed cooperative control scheme for voltage unbalance compensation (VUC) in an islanded microgrid (MG). By letting each distributed generator (DG) share the compensation effort cooperatively, unbalanced voltage in sensitive load bus (SLB) can be compensated. The concept of contribution level (CL) for compensation is first proposed for each local DG to indicate its compensation ability. A two-layer secondary compensation architecture consisting of a communication layer and a compensation layer is designed for each local DG. A totally distributed strategy involving information sharing and exchange is proposed, which is based on finite-time average consensus and newly developed graph discovery algorithm. This strategy does not require the whole system structure as a prior and can detect the structure automatically. The proposed scheme not only achieves similar VUC performance to the centralized one, but also brings some advantages, such as communication fault tolerance and plug-and-play property. Case studies including communication failure, CL variation, and DG plug-and-play are discussed and tested to validate the proposed method.

Guided image filter (GIF) is a well-known local filter for its edge-preserving property and low computational complexity. Unfortunately, the GIF may suffer from halo artifacts, because the local linear model used in the GIF cannot represent the image well near some edges. In this paper, a gradient domain GIF is proposed by incorporating an explicit first-order edge-aware constraint. The edge-aware constraint makes edges be preserved better. To illustrate the efficiency of the proposed filter, the proposed gradient domain GIF is applied for single-image detail enhancement, tone mapping of high dynamic range images and image saliency detection. Both theoretical analysis and experimental results prove that the proposed gradient domain GIF can produce better resultant images, especially near the edges, where halos appear in the original GIF.

This paper studies the global synchronization of complex dynamical network (CDN) under digital communication with limited bandwidth. To realize the digital communication, the so-called uniform-quantizer-sets are introduced to quantize the states of nodes, which are then encoded and decoded by newly designed encoders and decoders. To meet the requirement of the bandwidth constraint, a scaling function is utilized to guarantee the quantizers having bounded inputs and thus achieving bounded real-time quantization levels. Moreover, a new type of vector norm is introduced to simplify the expression of the bandwidth limit. Through mathematical induction, a sufficient condition is derived to ensure global synchronization of the CDNs. The lower bound on the sum of the real-time quantization levels is analyzed for different cases. Optimization method is employed to relax the requirements on the network topology and to determine the minimum of such lower bound for each case, respectively. Simulation examples are also presented to illustrate the established results.

Variable-speed fixed-pitch (VSFP) wind turbine is widely used in small-to-medium scale wind turbine markets due to its simple structure, low cost and high reliability features. However, power control, especially maximum power limitation control at high wind velocities, of such turbine concept is hard to realize and it has not been studied much in existing researches so far. To overcome this difficulty, we propose a simple power control strategy for VSFP wind turbines, covering the entire range of wind velocity. The proposed strategy contains a novel maximum power point tracking (MPPT) control method, a constant-speed (CS) soft-stalling control method and a constant-power (CP) soft-stalling control method. These three sub-control methods are appropriately combined in a way to realize seamless transitions among different operational modes (MPPT, CS and CP). The system structure and operational principle of the proposed strategy are thoroughly analyzed. Theoretical analysis is verified by simulation and experimental results performed by a 1.2kW fixed-pitch variable-speed wind turbine prototype.

Feature extraction is an essential step in pattern classification, which is normally divided into two tasks: transforming the input vector into a feature vector and/or reducing its dimensionality. A well-defined feature extraction algorithm makes the subsequent classification process more effective and efficient. One of the most important feature extraction algorithms is linear discriminant analysis (LDA). However, there is a critical drawback for LDA. For a classification task with c classes, since the rank of the between class matrix cannot be larger than c - 1, the dimension of the projected subspace is at most c - 1 for LDA. From this viewpoint, min-max discriminant analysis based on gradient method (MMDA-GM) is derived in this paper. With the proposed MMDA-GM, a set of features can be extracted simultaneously. It is shown that the proposed method achieves good performance for data sets from UCI Machine Learning Repository.

Congestion in urban areas constitutes an important problem that affects people in explicit but also implicit ways. Current research literature on Urban Traffic Estimation has shown that homogeneous distribution of vehicle density along the links of urban traffic networks plays an important role in the derivation or even the existence of the so-called Urban-Scale Macroscopic Fundamental Diagram or MFD in short. This Urban-Scale MFD can provide information that facilitates the application of perimeter traffic control strategies. In this paper, we implement a partitioning of an urban road network into homogeneous regions based on historical traffic information. Using prior information, we make informed decisions about the selection of the region on which the urban road network is based on, as well as the particular time periods for which the partitioning is to be implemented. We make use of weighted k-means, k-harmonic means and normalized spectral clustering techniques to successfully partition the region into clusters defined by low link density variability, while ensuring that the resulting partitions are spatially cohesive.

Magnetic Resonance Imaging (MRI) has become an important tool for doctors to diagnose liver cancer for decays. The survival rate of liver cancer patients can be significantly improved by an early diagnosis. In this paper, we present a computer aided kernel based support vector machine (SVM) algorithm for diagnosing liver cancer in early stage by applying our proposed method to the patients' magnetic resonance (MR) images. We apply the histogram-based feature extraction method to extract feature information from each raw MR image acquired. And 100 confirmed liver cancer and 100 confirmed benign type liver tumor (BLT) patients' feature information are used to form our training data set to train or SVM classification engine. The model is tested with a set of 30 confirmed early stage liver cancer and 30 BLT samples. Our trained SVM achieves an accuracy of 86.67% in classifying early stage liver cancer and 80.00% in classifying BLT.

Particle swarm optimization (PSO) has been proved to be an effective tool for function optimization. Its performance depends heavily on the characteristics of the employed exemplars. This necessitates considering both the fitness and the distribution of exemplars in designing PSO algorithms. Following this idea, we propose a novel PSO variant, called scatter learning PSO algorithm (SLPSOA) for multimodal problems. SLPSOA contains some new algorithmic features while following the basic framework of PSO. It constructs an exemplar pool (EP) that is composed of a certain number of relatively high-quality solutions scattered in the solution space, and requires particles to select their exemplars from EP using the roulette wheel rule. By this means, more promising solution regions can be found. In addition, SLPSOA employs Solis and Wets' algorithm as a local searcher to enhance its fine search ability in the newfound solution regions. To verify the efficiency of the proposed algorithm, we test it on a set of 16 benchmark functions and compare it with six existing typical PSO algorithms. Computational results demonstrate that SLPSOA can prevent premature convergence and produce competitive solutions.

New optimization models and algorithms for online learning with Kernels (OLK) in classification, regression, and novelty detection are proposed in a reproducing Kernel Hilbert space. Unlike the stochastic gradient descent algorithm, called the naive online Reg minimization algorithm (NORMA), OLK algorithms are obtained by solving a constrained optimization problem based on the proposed models. By exploiting the techniques of the Lagrange dual problem like Vapnik's support vector machine (SVM), the solution of the optimization problem can be obtained iteratively and the iteration process is similar to that of the NORMA. This further strengthens the foundation of OLK and enriches the research area of SVM. We also apply the obtained OLK algorithms to problems in classification, regression, and novelty detection, including real time background substraction, to show their effectiveness. It is illustrated that, based on the experimental results of both classification and regression, the accuracy of OLK algorithms is comparable with traditional SVM-based algorithms, such as SVM and least square SVM (LS-SVM), and with the state-of-the-art algorithms, such as Kernel recursive least square (KRLS) method and projectron method, while it is slightly higher than that of NORMA. On the other hand, the computational cost of the OLK algorithm is comparable with or slightly lower than existing online methods, such as above mentioned NORMA, KRLS, and projectron methods, but much lower than that of SVM-based algorithms. In addition, different from SVM and LS-SVM, it is possible for OLK algorithms to be applied to non-stationary problems. Also, the applicability of OLK in novelty detection is illustrated by simulation results.

In this paper, we consider the problem of designing distributed adaptive consensus tracking controllers for multiple nonlinear systems with unknown parameters and external disturbances. The desired trajectory is time varying given by the state of a reference system, which is only available to a portion of the group of the systems. Besides, the dynamics of the reference state is bounded but unknown to all of the systems. The communication graph characterizing the interactions among the systems is assumed to have undirected, fixed and connected topology. By introducing distributed estimators for the bound of the reference dynamics, two control schemes are proposed to address the problem. In the first scheme, a sign function is employed and perfect consensus tracking can be achieved. In the second scheme, an alternative control law is developed and the chattering phenomenon caused by the sign function can be reduced. However, new challenge will be triggered which is to compensate for possible destabilizing effects of the coupling elements relating to local parameter estimation errors and the synchronization errors of the neighbors. The overall communication graph is firstly reduced to an undirected spanning tree with single system notified of the reference state. Based on this, new synchronization error for each subsystem is then defined as the weighted distance relative to only one of its neighbors. It is shown that all the synchronization errors will converge to a prescribed bound which can be made as small as desired in this case.

In this paper, we consider a class of uncertain magnetic suspension system preceded by Bouc-Wen type of hysteresis nonlinearity. A new perfect inverse function of the hysteresis is constructed and used to cancel the hysteresis effects in controller design with backstepping technique. For the design and implementation of the controller, no knowledge is assumed on system parameters. It is shown that the proposed controller not only guarantees asymptotic stability, but also transient performance.

In this paper, synchronization of continuous dynamical networks with discrete-time communications is studied. Though the dynamical behavior of each node is continuous-time, the communications between every two different nodes are discrete-time, i.e., they are active only at some discrete time instants. Moreover, the communication intervals between every two communication instants can be uncertain and variable. By choosing a piecewise Lyapunov-Krasovskii functional to govern the characteristics of the discrete communication instants and by utilizing a convex combination technique, a synchronization criterion is derived in terms of linear matrix inequalities with an upper bound for the communication intervals obtained. The results extend and improve upon earlier work. Simulation results show the effectiveness of the proposed communication scheme. Some relationships between the allowable upper bound of communication intervals and the coupling strength of the network are illustrated through simulations on a fully connected network, a star-like network, and a nearest neighbor network.

Blood pressure (BP) is one of the most important physiological parameters reflecting cardiovascular status of people. Continuous and non-invasive BP measurement, which can provide beat-to-beat BP information and call attention to the doctor if the patent is in danger during surgery, plays a very important role in safety control in operation and intensive care. In this paper, a novel and complete methodology of continuous and non-invasive BP measurement by PWV without calibration by other methods such as cuff sphygmomanometer is proposed. Critical parts of this study include modeling BP-PWV relationship and identifying two PWVs relative to systolic blood pressure (SBP) and diastolic blood pressure (DBP) respectively. Some benchmark models for 20-year-old and 60-year-old age groups are developed and applied to BP measurement in clinical trials. The clinically obtained results meet the test standard in ANSI/AAMI SP10, which attests the feasibility of the measuring method. Lastly, the continuous SBP and DBP measurement results by PWV method are shown.

In this paper, we propose a new method for the identification of Wiener systems based on fixed point theory. The linear part of the system is an infinite impulse response (IIR) system and the nonlinear static function is allowed to be non-continuous or non-smooth. Our proposed technique transforms the estimation of parameters to finding a fixed point of a nonlinear equation. We show the existence of the fixed point and also develop an iterative algorithm to find the fixed point. It is proved that, the determined fixed point is actually a global minimum point of the cost function and it is unique, and thus global convergence of the estimates is ensured. The performance of the proposed approach is illustrated by simulation studies.

A human being can perform many different movements to adapt to variable environment, all these skills are extraordinary feats of coordination and control. It is believed that they result from the human hierarchical motor control system. This system is a complex nonlinear system including highly integrated neural centers in the brain and the spinal cord, many actuators constructed by muscles and bones and numerous sensory receptors. It is believed that the neuro-musculoskeletal model of human motor control system is very important to understand the characteristics of the system and many neuro-musculoskeletal models were presented over the past decades. In this paper, dynamic behavior and mechanism of the neuro-musculoskeletal model presented in are investigated. It is a single joint reflex model with agonist and antagonist muscles. Some intrinsic properties of the system model such as behavior of the moment arms, characteristics of force and stabilization are presented. The results reveal some inherit properties in the system. Firstly, to guarantee a unique equilibrium point of the system, the descending neuron control commands must be selected in special range; Secondly, the reflex regulation in spinal control circuit, i.e., the feedback from Renshaw cells and gamma motor neurons, are not absolutely necessary to ensure the system stable, the system can still converge to a equilibrium point with the constant muscle activations.

In this paper, an overview on pinning control of complex dynamical networks is reported. The research works on pinning control of complex dynamical networks are investigated thoroughly from seven aspects, including the dynamic behavior of the nodes, the structures of the networks, the control purpose, the control method, the methods of theoretical analysis, the implementation strategies and some other aspects of the research results. The unsolved problems existing in pinning control are also discussed, which will give some suggestions on future research works.

In this paper, the results of stabilizing a large scale nonlinear systems with uncertain dynamic interactions and unmodelled dynamics depending on both subsystem inputs and outputs in [9] and [10] are extended to highly nonlinear systems. Certain modifications on standard adaptive backstepping controllers are proposed to compensate for the effects of interactions from other subsystems in designing local controllers. .

The on-going scalable video coding (SVC) standard is an extension of H.264/AVC. It enables significantly improved compression performance at the expense of greatly increased computational complexity at both the encoder and the decoder sides. This paper presents an adaptive algorithm to reduce the complexity of decoder at the encoder side for coarse granular scalability. Hence, lightweight bitstreams are generated at the encoder which requires significantly less decoding complexity. The experimental results show that the proposed scheme provides significant reduction in the complexity of decoder with acceptable coding loss and minor impact on the encoder complexity.

This paper presents an improved fast mode decision algorithm for coarse grain scalability (CGS). The modified fast mode decision algorithm extends previous work to provide better encoder complexity reduction with insignificant degradation in picture quality. The candidate mode set is adaptive to the quantization parameter difference between a layer and its "base layer". Furthermore, the proposed scheme fully utilizes the statistics of mode transition between a layer and its "base layer" when the quantization parameter difference between these two layers is small. Simulation results demonstrate that the proposed scheme provides up to 64% of time saving compared with the original SVC encoder.

In this paper, the monotonicity of the likelihood ratio used in the optimal correlation detection is established for optical code division multiple-access (OCDMA). The monotonicity property not only guarantees the optimality of the detection, it also enables us to calculate the bit-error-rate bounds for OCDMA in the presence of uncertain a priori probabilities.

In this paper, a practical impulsive synchronization scheme is proposed for the synchronization of two Chen's chaotic systems with parametric uncertainty and mismatch. With this scheme, the error of the systems can converge to a given bound

Scalable Video Coding (SVC) is an on-going standard and the current working draft (WD) is an extension of H.264/AVC. In the WD, exhaustive search technique is employed to select the best coding mode for each macroblock (MB). This technique achieves highest possible coding efficiency, but it results in higher computational complexity. To overcome this, we propose a novel fast mode decision scheme for coarse grain SNR scalability (CGS) in SVC. In this scheme, the mode distribution relationships between the base layer and enhancement layers are employed to reduce the candidate mode set at enhancement layers. The experimental results show that the proposed scheme provides significant reduction in computational complexity with negligible coding loss.

Scalable video coding (SVC) is an on-going standard and the current working draft (WD) is an extension of H.264/AVC. It provides scalability at the bit stream level with good compression efficiency, and allowing free combinations of spatial, temporal and SNR scalability. In the WD, exhaustive search technique is employed to select the best coding mode for each macroblock. This technique achieves highest possible coding efficiency, but it results in higher computational complexity. To overcome this, we propose a novel fast mode decision scheme for spatial scalability in SVC. In this scheme, the mode distribution relationship between base layer and enhancement layers is employed to reduce the candidate mode set at enhancement layers. The experimental results show that the proposed scheme provides significant reduction in computational complexity without any noticeable coding loss.

Scalable video coding is an ongoing standard, and the current working draft (WD) is an extension of H.264/AVC. In the WD, an exhaustive search technique is employed to select the best coding mode for each macroblock. This technique achieves the highest possible coding efficiency, but it results in extremely large encoding time which obstructs it from practical use. This paper proposes a fast mode decision algorithm for inter-frame coding for spatial, coarse grain signal-to-noise ratio, and temporal scalability. It makes use of the mode-distribution correlation between the base layer and enhancement layers. Specifically, after the exhaustive search technique is performed at the base layer, the candidate modes for enhancement layers can be reduced to a small number based on the correlation. Experimental results show that the fast mode decision scheme reduces the computational complexity significantly with negligible coding loss and bit-rate increases

In this paper, we present an adaptive backstepping control algorithm for a class of uncertain nonlinear systems under state-feedback form including hysteretic nonlinearity. The result is applied to a system found in base isolation schemes for seismic active protection of building structures. This system exhibits a hysteretic nonlinear behavior which is described by the so-called Bouc-Wen model. Unlike other control schemes, the developed backstepping control does not require the model parameters within known intervals. It is shown that not only global stability is guaranteed by the proposed controller, but also both transient and asymptotic performances are quantified as explicit functions of the design parameters so that designers can tune the design parameters in an explicit way to obtain the required closed loop behavior.

The paper proposes a digital chaotic secure communication by introducing a magnifying glass concept, which is used to enlarge and observe minor parameter mismatch so as to increase the sensitivity of the system. The encryption method is based on a one-time pad encryption scheme, where the random key sequence is replaced by a chaotic sequence generated via a Chua's circuit. We make use of an impulsive control strategy to synchronize two identical chaotic systems embedded in the encryptor and the decryptor, respectively. The lengths of impulsive intervals are piecewise constant and, as a result, the security of the system is further improved. Moreover, with the given parameters of the chaotic system and the impulsive control law, an estimate of the synchronization time is derived. The proposed cryptosystem is shown to be very sensitive to parameter mismatch and hence the security of the chaotic secure communication system is greatly enhanced.

In the one-dimensional (1D) case, Bellman-Gronwall Lemma plays an important role in the field of system theory. For example, it is a key technique in analyzing the shift-variant systems and adaptive systems. But there is no such result available in the two-dimensional (2D) case. In this paper, we first establish a simplified version of 2D counterpart of the Bellman-Gronwall Lemma for its potential application in analyzing 2D shift-variant systems and adaptive systems. Then a general version of 2D Bellman-Gronwall Lemma is derived by a 2D inductive method. These results are analogical to the 1D Bellman-Gronwall Lemma.

In practical computer vision system, there are situations where displacement of an image is accompanied by a defocus blur. Since depth can be estimated from the degree of blur, in this paper we propose a method to get the blur parameter and simultaneously affine transformation parameters. Firstly we introduce a method to get the motion parameters from the original image and the affine transformed and simultaneously blurred image (second image). Then we use these parameters to construct affine transformed image. The last step is to get the blur parameters from the constructed image and the second image. The effectiveness of the proposed method is demonstrated by experimental results.