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In this article, the connectivity-maintained platoon control problem for networked vehicles with external disturbance is considered. By modeling each vehicle as a nonlinear triple integral system, a specified-time sliding model disturbance observer and a specified-time controller are presented to ensure the convergence of the platoon system. The predetermined performance method is applied to maintain the connectivity of vehicles in the platoon, which enables flexibility in the communication connectivity between vehicles. The specified time sliding mode disturbance observer is proposed to estimate the external disturbance within the predefined time and it is proved to be uniformly ultimate boundedness stable by using the time transformation method. A dynamic gain controller with the time constraint is proposed to achieve the stable platoon with connectivity maintained for each vehicle. The proposed approach can simultaneously guarantee the specified-time convergence and connectivity maintenance without depending on any system parameters. The effective of the proposed controller is proved by the Lyapunov function. Finally, comparative simulation results are obtained to verify the effectiveness of the proposed protocols.
Jiange Wang; Wai‐Choong Wong; Xiaoyuan Luo; Xiaolei Li; Xinping Guan. Connectivity‐maintained and specified‐time vehicle platoon control systems with disturbance observer. International Journal of Robust and Nonlinear Control 2021, 1 .
AMA StyleJiange Wang, Wai‐Choong Wong, Xiaoyuan Luo, Xiaolei Li, Xinping Guan. Connectivity‐maintained and specified‐time vehicle platoon control systems with disturbance observer. International Journal of Robust and Nonlinear Control. 2021; ():1.
Chicago/Turabian StyleJiange Wang; Wai‐Choong Wong; Xiaoyuan Luo; Xiaolei Li; Xinping Guan. 2021. "Connectivity‐maintained and specified‐time vehicle platoon control systems with disturbance observer." International Journal of Robust and Nonlinear Control , no. : 1.
The event-triggered consensus control for second-order multi-agent systems subject to actuator saturation and input time delay, is investigated in this paper. Based on the designed triggering function, a distributed event-triggered control strategy is presented to drive the system to achieve consensus. Communication energy can be saved as the agents send their state information only at infrequent event instants, the continuous communication among agents is not necessary. Lyapunov-Krasovskii functional is used together with linear matrix inequality technique to analyze the stability of the closed-loop error system. The results show that agents achieve exponentially consensus under the proposed controller. Furthermore, the bounds of solution are obtained by establishing the differential equation associated with the first delay interval. The initial domain is estimated by optimizing the linear matrix inequalities. Finally, simulation examples are presented to illustrate the effectiveness of the proposed controller.
Jinran Wang; Xiaoyuan Luo; Jing Yan; Xinping Guan. Event-triggered consensus control for second-order multi-agent system subject to saturation and time delay. Journal of the Franklin Institute 2021, 358, 4895 -4916.
AMA StyleJinran Wang, Xiaoyuan Luo, Jing Yan, Xinping Guan. Event-triggered consensus control for second-order multi-agent system subject to saturation and time delay. Journal of the Franklin Institute. 2021; 358 (9):4895-4916.
Chicago/Turabian StyleJinran Wang; Xiaoyuan Luo; Jing Yan; Xinping Guan. 2021. "Event-triggered consensus control for second-order multi-agent system subject to saturation and time delay." Journal of the Franklin Institute 358, no. 9: 4895-4916.
We investigate the generalized projective synchronization (GPS) problem of fractional-order extended Hindmarsh-Rose (FOEHR) neuronal models with magneto-acoustical stimulation input. The improved neuronal model has advantages in depicting the biological characteristics of neurons and therefore exhibits complex firing behaviors. In addition, we consider the nonlinearity and uncertain parameters of the neuronal model as well as the unknown external disturbances, which make the synchronization control of the master-slave neuron system more difficult. For the synchronous firing rhythms of neurons, a neural network (NN) sliding mode algorithm for the FOEHR neuron system is derived by the Lyapunov approach. We use a radial basis NN to approximate the unknown nonlinear dynamics of the error system, and the adaptive parameters are robust to the approximation errors, model uncertainties and unknown external disturbances. Under the proposed control scheme, the master and slave neuron systems can achieve GPS in a finite amount of time and realize resilience for the uncertain parameters and the external disturbances. The simulation results demonstrate that the membrane potentials of the slave neuron synchronize with those of the master neuron in proportion and that the underlying synchronization errors converge towards an arbitrarily small neighborhood of zero.
Dan Liu; Song Zhao; Xiaoyuan Luo; Yi Yuan. Synchronization for fractional-order extended Hindmarsh-Rose neuronal models with magneto-acoustical stimulation input. Chaos, Solitons & Fractals 2021, 144, 110635 .
AMA StyleDan Liu, Song Zhao, Xiaoyuan Luo, Yi Yuan. Synchronization for fractional-order extended Hindmarsh-Rose neuronal models with magneto-acoustical stimulation input. Chaos, Solitons & Fractals. 2021; 144 ():110635.
Chicago/Turabian StyleDan Liu; Song Zhao; Xiaoyuan Luo; Yi Yuan. 2021. "Synchronization for fractional-order extended Hindmarsh-Rose neuronal models with magneto-acoustical stimulation input." Chaos, Solitons & Fractals 144, no. : 110635.
In this paper, we propose a progressive model predictive control scheme (PMPCS) by considering the cooperative control of local planning and path tracking for intelligent vehicles. An improved particle swarm optimization (IPSO) based model predictive control (MPC) method is developed to solve the planning and tracking problem. With the PMPCS, the total computational burden can be reduced sharply because of the seamless connection and mutual promotion between the optimization of two layers. Besides we also propose a novel planning algorithm, which can take traffic lights and overtaking time constraint into account. To solve these problems, we first combine model predictive control with artificial potential field (APF) to get a collision-free path by treating the timevarying safety constraints as the scope of the repulsive force and an asymmetrical lane potential field function. Furthermore, the pseudo velocity planning method is adopted to take traffic lights into account in the planning module. Simulation results show the reliability of the proposed algorithm and the advantages of the scheme compared with general hierarchical algorithm.
Zhiqiang Zuo; Xu Yang; Zheng Li; Yijing Wang; Qiaoni Han; Li Wang; Xiaoyuan Luo. MPC-Based Cooperative Control Strategy of Path Planning and Trajectory Tracking for Intelligent Vehicles. IEEE Transactions on Intelligent Vehicles 2020, 6, 513 -522.
AMA StyleZhiqiang Zuo, Xu Yang, Zheng Li, Yijing Wang, Qiaoni Han, Li Wang, Xiaoyuan Luo. MPC-Based Cooperative Control Strategy of Path Planning and Trajectory Tracking for Intelligent Vehicles. IEEE Transactions on Intelligent Vehicles. 2020; 6 (3):513-522.
Chicago/Turabian StyleZhiqiang Zuo; Xu Yang; Zheng Li; Yijing Wang; Qiaoni Han; Li Wang; Xiaoyuan Luo. 2020. "MPC-Based Cooperative Control Strategy of Path Planning and Trajectory Tracking for Intelligent Vehicles." IEEE Transactions on Intelligent Vehicles 6, no. 3: 513-522.
The biased load attacks pose enormous security risks to smart grids, due to the characteristics of spoofing attack. To handle the risks, a novel scheme for detecting and localizing biased load attacks is developed. Firstly, an unknown input interval observer is designed to mitigate the influences of disturbances and regional interconnection information, contributing to an accurate estimation of the interval state. Secondly, considering the feature of interval residuals, a novel detection criterion is developed to eliminate the limitation resulted by the prior threshold in the existing detection techniques. In addition, a logic judgment matrix is established based on the combination of sensor set, addressing the problem of attack detection and localization under structural vulnerability. Finally, the simulation results indicate that the developed scheme can detect and localize the biased load attacks effectively. Also, the developed scheme shows superior performance than state-of-the-art techniques.
Xinyu Wang; Xiaoyuan Luo; Mingyue Zhang; Zhongping Jiang; Xinping Guan. Detection and localization of biased load attacks in smart grids via interval observer. Information Sciences 2020, 552, 291 -309.
AMA StyleXinyu Wang, Xiaoyuan Luo, Mingyue Zhang, Zhongping Jiang, Xinping Guan. Detection and localization of biased load attacks in smart grids via interval observer. Information Sciences. 2020; 552 ():291-309.
Chicago/Turabian StyleXinyu Wang; Xiaoyuan Luo; Mingyue Zhang; Zhongping Jiang; Xinping Guan. 2020. "Detection and localization of biased load attacks in smart grids via interval observer." Information Sciences 552, no. : 291-309.
Underwater sensor networks (USNs) are envisioned to enable a large variety of marine applications. Such applications require accurate position information of sensor nodes. However, the openness and inhomogeneity characteristics of underwater medium make it much more challenging to solve the localization issue. This paper is concerned with a privacy-preserving localization issue for USNs in inhomogeneous underwater medium. An honest-but-curious model is considered to develop a privacy-preserving localization protocol. Based on this, a localization problem is constructed for sensor nodes to minimize the sum of all measurement errors, where a ray compensation strategy is incorporated to remove the localization bias from assuming the straight-line transmission. To make the above problem tractable, we consider the unsupervised, supervised and semisupervised scenarios, through which deep reinforcement learning (DRL) based localization estimators are utilized to estimate the positions of sensor nodes. It is noted that, the proposed localization solution in this paper can hide the private position information of USNs, and more importantly, it is robust to local optimum for nonconvex and nonsmooth localization problem in inhomogeneous underwater medium. Finally, simulation studies are given to show the position privacy can be preserved, while the localization accuracy can be enhanced as compared with the other existing works.
Jing Yan; Yuan Meng; Xian Yang; Xiaoyuan Luo; Xinping Guan. Privacy-Preserving Localization for Underwater Sensor Networks via Deep Reinforcement Learning. IEEE Transactions on Information Forensics and Security 2020, 16, 1880 -1895.
AMA StyleJing Yan, Yuan Meng, Xian Yang, Xiaoyuan Luo, Xinping Guan. Privacy-Preserving Localization for Underwater Sensor Networks via Deep Reinforcement Learning. IEEE Transactions on Information Forensics and Security. 2020; 16 ():1880-1895.
Chicago/Turabian StyleJing Yan; Yuan Meng; Xian Yang; Xiaoyuan Luo; Xinping Guan. 2020. "Privacy-Preserving Localization for Underwater Sensor Networks via Deep Reinforcement Learning." IEEE Transactions on Information Forensics and Security 16, no. : 1880-1895.
In this article, a coupled sliding mode control (CSMC) is developed for vehicular systems with nonlinear uncertainties by using the disturbance observer (DO) and multi power reaching law. The DO is designed to estimate the nonlinear uncertainties. It is worth mentioning for the DO that the uncertainties include not only parameter uncertainty but also external disturbance, and the bounds of the uncertainties are not required to be known. In addition, the multi power reaching law is constructed to avoid the chattering problem of the traditional sliding mode control (SMC) and to improve the convergence speed effectively. Firstly, the constant time headway policy (CTHP) based on multi power reaching law and SMC is proposed to achieve the string stability for vehicle platoons. Compared with constant spacing policy (CSP), CTHP is more feasible in practice, because the desired spacing between adjacent vehicles is dependent on vehicle speed. Then, a modified constant time headway policy (MCTHP) is proposed for the vehicular systems to decrease the intervehicle spacing and increase the traffic density effectively. Finally, the numerical simulation and experiment are performed to demonstrate the effectiveness and advantage of the developed strategy.
Jianmei Wang; Xiaoyuan Luo; Jing Yan; Xinping Guan. Distributed Integrated Sliding Mode Control for Vehicle Platoons Based on Disturbance Observer and Multi Power Reaching Law. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -11.
AMA StyleJianmei Wang, Xiaoyuan Luo, Jing Yan, Xinping Guan. Distributed Integrated Sliding Mode Control for Vehicle Platoons Based on Disturbance Observer and Multi Power Reaching Law. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-11.
Chicago/Turabian StyleJianmei Wang; Xiaoyuan Luo; Jing Yan; Xinping Guan. 2020. "Distributed Integrated Sliding Mode Control for Vehicle Platoons Based on Disturbance Observer and Multi Power Reaching Law." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-11.
We investigate the generalized projective synchronization (GPS) control of fractional-order extended Hindmarsh-Rose (FOEHR) neuronal models with transcranial magneto-acoustical stimulation (TMAS) input. This improved neuronal model has advantages in describing the complex firing characteristics of neurons stimulated by alternating current. In this study, a master-slave neuron system consisting of two FOEHR neuronal models is assumed to be subject to uncertain model parameters and unknown external disturbances. To quantify the GPS error, we design a new error variable based on the properties of the fractional-order derivative and construct a related GPS error system. Fuzzy logic systems are introduced to approximate the unknown nonlinear dynamics of the error system. To ensure the synchronous firing rhythms of the master-slave neuron system, an adaptive fuzzy control algorithm is proposed under the Lyapunov approach, in which the adaptive parameters are robust to the estimation errors. By choosing the appropriate design parameters, the proposed control scheme enables the master-slave neuron system to achieve GPS in a finite amount of time and to be resilient to uncertain parameters and unknown disturbances. The simulation results demonstrate that after the designed control inputs are implemented, the states of the slave neuron synchronize with those of the master neuron in specified proportions, and the corresponding synchronization error converges towards an arbitrarily small neighborhood of zero.
Dan Liu; Song Zhao; Xiaoyuan Luo. Adaptive Fuzzy Control for the Generalized Projective Synchronization of Fractional-Order Extended Hindmarsh-Rose Neurons. IEEE Access 2020, 8, 190689 -190699.
AMA StyleDan Liu, Song Zhao, Xiaoyuan Luo. Adaptive Fuzzy Control for the Generalized Projective Synchronization of Fractional-Order Extended Hindmarsh-Rose Neurons. IEEE Access. 2020; 8 (99):190689-190699.
Chicago/Turabian StyleDan Liu; Song Zhao; Xiaoyuan Luo. 2020. "Adaptive Fuzzy Control for the Generalized Projective Synchronization of Fractional-Order Extended Hindmarsh-Rose Neurons." IEEE Access 8, no. 99: 190689-190699.
Localization accuracy is an important indicator to design and deploy the wireless sensor network. This paper quantitatively investigates the bearing-based localization accuracy (BBLA) from the perspective of network geometric structure. The average geometric dilution of precision (AGDOP) is used to model the geometric structure-based BBLA, and is expressed by the geometry matrix which is based on the bearing measurement. Since geometry matrix is influenced by two factors (the bearing and link length), these factors will convert to network characteristics such as the network scale, network size and network symmetry, to perform the interaction on the BBLA. So we consider two cases to deduce the closed-form expression for the BBLA lower bound based on AGDOP by using the method of control variables, and to independently analyze the influence of the network characteristics on the BBLA. Concretely, the impacts of the network scale and network symmetry on the BBLA are analyzed under the coordinate symmetry assumption in the fifirst case, while the impact of the network size on the BBLA is analyzed in the second case. From these analyses, we find that the network scale and network symmetry are approximate to the positive association with the BBLA, while the network size is negatively associated with the BBLA. Finally, simulations are provided to demonstrate the effectiveness of the calculated lower bounds and to vividly illustrate the correctness of the accuracy analyses.
Wenjing Zhong; Xiaoyuan Luo; Xiaolei Li; Jing Yan; Xinping Guan. Lower Bound Accuracy of Bearing-Based Localization for Wireless Sensor Networks. IEEE Transactions on Signal and Information Processing over Networks 2020, 6, 556 -569.
AMA StyleWenjing Zhong, Xiaoyuan Luo, Xiaolei Li, Jing Yan, Xinping Guan. Lower Bound Accuracy of Bearing-Based Localization for Wireless Sensor Networks. IEEE Transactions on Signal and Information Processing over Networks. 2020; 6 (99):556-569.
Chicago/Turabian StyleWenjing Zhong; Xiaoyuan Luo; Xiaolei Li; Jing Yan; Xinping Guan. 2020. "Lower Bound Accuracy of Bearing-Based Localization for Wireless Sensor Networks." IEEE Transactions on Signal and Information Processing over Networks 6, no. 99: 556-569.
The localizability analysis for wireless sensor network is of great signifificance to network localization and topology control. In this paper, the problem of localizability for the bearing-based localization, is investigated. An identification method for bearing rigid component is presented and the localizability is studied for the determined bearing rigid component. In the identifification process for bearing rigid component, the center node is introduced and an approach for identifying the bearing rigid component is proposed based on the characteristic of the bearing rigid graph by using the center nodes. Then, taking the redundancy and complexity of calculation into account, the node management strategy and the selection principle for the center nodes are put forward subsequently to provide the guidance for determining all bearing rigid components. The localizability analysis for an determined bearing rigid component reveals its complete localizability with two arbitrary anchor nodes. Finally, some simulation results are provided to demonstrate the effectiveness and simplicity of the proposed identification approach for bearing rigid components and to illustrate the superiority of the bearing rigidity-based localization compared with the distance rigidity-based localization from the perspective of localizability.
Xiaoyuan Luo; Wenjing Zhong; Xiaolei Li; Xinping Guan. Bearing Rigidity-Based Localizability Analysis for Wireless Sensor Networks. IEEE Transactions on Signal and Information Processing over Networks 2020, 6, 526 -539.
AMA StyleXiaoyuan Luo, Wenjing Zhong, Xiaolei Li, Xinping Guan. Bearing Rigidity-Based Localizability Analysis for Wireless Sensor Networks. IEEE Transactions on Signal and Information Processing over Networks. 2020; 6 (99):526-539.
Chicago/Turabian StyleXiaoyuan Luo; Wenjing Zhong; Xiaolei Li; Xinping Guan. 2020. "Bearing Rigidity-Based Localizability Analysis for Wireless Sensor Networks." IEEE Transactions on Signal and Information Processing over Networks 6, no. 99: 526-539.
The cyber security of large-scale smart grid against False Data Injection Attack (FDIA) is concerned in this paper. FDIA can modify the sensor data and make internal states cause bias without being detected by the bad data detection system. We propose a method for FDIA detection and localization in smart grid in the paper. Firstly, a series of interval observers are designed with considering the bounds of internal states, modeling errors and disturbances to estimate the interval states of the grid physical system. By using the interval residuals of interval observers, a detection scheme again FDIA is proposed. For FDIA localization, the measurement data of corresponding sensor is used as the input of the interval observer. Therefore, each interval observer is responsible for FDIA detection and localization of the corresponding sensor. Furthermore, the logic localization judgment matrix is constructed for localizing the sensor in which is injected FDIA. Then, the detection and localization scheme against FDIA is proposed based on the interval observer and the logic localization judgment matrix. Finally, simulations on the IEEE 36-bus grid are performed to illustrate the effectiveness of the proposed interval observer-based FDIA detection and localization algorithm.
Xiaoyuan Luo; Yating Li; Xinyu Wang; Xinping Guan. Interval Observer-Based Detection and Localization Against False Data Injection Attack in Smart Grids. IEEE Internet of Things Journal 2020, 8, 657 -671.
AMA StyleXiaoyuan Luo, Yating Li, Xinyu Wang, Xinping Guan. Interval Observer-Based Detection and Localization Against False Data Injection Attack in Smart Grids. IEEE Internet of Things Journal. 2020; 8 (2):657-671.
Chicago/Turabian StyleXiaoyuan Luo; Yating Li; Xinyu Wang; Xinping Guan. 2020. "Interval Observer-Based Detection and Localization Against False Data Injection Attack in Smart Grids." IEEE Internet of Things Journal 8, no. 2: 657-671.
Accurate sensor localization is a crucial requirement for the deployment of underwater acoustic sensor networks (UASNs) in a large variety of applications. However, the asynchronous clock, stratification effect and mobility characteristics of underwater environment make it challenging to realize accurate node localization for UASNs. This paper develops an autonomous underwater vehicle (AUV) aided localization solution for UASNs, subjected to asynchronous clock, stratification effect and mobility constraints in cyber channels. A hybrid architecture including surface buoys, AUVs, active and passive sensor nodes, is first presented to construct a cooperative location-aware network. Then, an iterative least squares estimator is developed for AUVs to capture the unknown water current parameters, through which the relationship between propagation delay and location estimation can be established. With the assistance of AUVs, two asynchronous localization algorithms are designed to estimate the locations of active and passive sensor nodes. Particularly, motion and ray compensation strategies are jointly employed to improve the localization accuracy. It is worth noticing that, the proposed localization algorithms incorporate the current field estimation into the localization process of UASNs, and more importantly, they can eliminate the influences of asynchronous clock, stratification effect and node mobility together. Moreover, performance analyses for the proposed localization solution are also presented. Finally, simulation and experimental results reveal that the node localization accuracy in this paper can be significantly improved as compared with the other works.
Jing Yan; Dongbo Guo; Xiaoyuan Luo; Xinping Guan. AUV-Aided Localization for Underwater Acoustic Sensor Networks With Current Field Estimation. IEEE Transactions on Vehicular Technology 2020, 69, 8855 -8870.
AMA StyleJing Yan, Dongbo Guo, Xiaoyuan Luo, Xinping Guan. AUV-Aided Localization for Underwater Acoustic Sensor Networks With Current Field Estimation. IEEE Transactions on Vehicular Technology. 2020; 69 (8):8855-8870.
Chicago/Turabian StyleJing Yan; Dongbo Guo; Xiaoyuan Luo; Xinping Guan. 2020. "AUV-Aided Localization for Underwater Acoustic Sensor Networks With Current Field Estimation." IEEE Transactions on Vehicular Technology 69, no. 8: 8855-8870.
Localization is a critical issue for many location-based applications in the Internet of Underwater Things (IoUT). Nevertheless, the asynchronous time clock, stratification effect and mobility properties of underwater environment make it much more challenging to solve the localization issue. This paper is concerned with an autonomous underwater vehicle (AUV) aided localization issue for IoUT. We first provide a hybrid network architecture that includes surface buoys, AUVs, active and passive sensor nodes. On the basis of this architecture, an asynchronous localization protocol is designed, through which the localization problem is provided to minimize the sum of all measurement errors. In order to make this problem tractable, a reinforcement learning (RL) based localization algorithm is developed to estimate the locations of AUVs, active and passive sensor nodes, where an online value iteration procedure is performed to seek the optimization locations. It is worth mentioned that, the proposed localization algorithm adopts two neural networks to approximate the increment policy and value function, and more importantly, it is much preferable for nonsmooth and nonconvex underwater localization problem due to its insensitivity to the local optimal. Performance analyses for the RL-based localization algorithm are also provided. Finally, simulation and experimental results reveal that the localization performance in this paper can be significantly improved as compared with the other works.
Jing Yan; Yadi Gong; Cailian Chen; Xiaoyuan Luo; Xinping Guan. AUV-Aided Localization for Internet of Underwater Things: A Reinforcement-Learning-Based Method. IEEE Internet of Things Journal 2020, 7, 9728 -9746.
AMA StyleJing Yan, Yadi Gong, Cailian Chen, Xiaoyuan Luo, Xinping Guan. AUV-Aided Localization for Internet of Underwater Things: A Reinforcement-Learning-Based Method. IEEE Internet of Things Journal. 2020; 7 (10):9728-9746.
Chicago/Turabian StyleJing Yan; Yadi Gong; Cailian Chen; Xiaoyuan Luo; Xinping Guan. 2020. "AUV-Aided Localization for Internet of Underwater Things: A Reinforcement-Learning-Based Method." IEEE Internet of Things Journal 7, no. 10: 9728-9746.
The elimination of parallax and the processing of natural issue in complex scenes are challenging tasks for image stitching. In this paper, an image stitching method with positional relationship constraints of feature points and lines, which can accomplish accurate alignment and reduce projection distortion, is proposed. At first, to reduce the computational cost and the number of outliers on subsequent feature matching, we combine the template matching to propose a quick way for detecting overlapping regions. Then, the appropriate reference image is determined to mitigate the projection distortion that the image warping is in the cases of non-planar geometry of the scenes. Furthermore, a local mesh model based on dual features is established to guide the mesh deformation. And an energy function is designed to refine alignment. In addition to alignment error terms, a novel positional relationship constraint term is proposed to improve quality of naturalness of final stitching results. Finally, experimental results demonstrate that our approach is superior to the existing image stitching algorithms in improving quality of the stitched image naturalness.
Xiaoyuan Luo; Yang Li; Jing Yan; Xinping Guan. Image stitching with positional relationship constraints of feature points and lines. Pattern Recognition Letters 2020, 135, 431 -440.
AMA StyleXiaoyuan Luo, Yang Li, Jing Yan, Xinping Guan. Image stitching with positional relationship constraints of feature points and lines. Pattern Recognition Letters. 2020; 135 ():431-440.
Chicago/Turabian StyleXiaoyuan Luo; Yang Li; Jing Yan; Xinping Guan. 2020. "Image stitching with positional relationship constraints of feature points and lines." Pattern Recognition Letters 135, no. : 431-440.
This article studies the detection and location of the bias load injection attack (BLIA) in smart grid. As one of typical false data injection attacks (FDIAs), the BLIA aims at destroying the vulnerable generator load. In particularly, the BLIA can bypass the traditional bad data detection techniques, by compromising the measurable sensor-data estimation. Because of this reason, the emergency of BLIA brings enormous threat to the security of smart grid. To address this problem, a detection and location framework against BLIA consisting of three steps is proposed. In the first step, we propose a topology structure-based subregion division algorithm to reduce the complexity of attack detection in the large-scale grid system. In the second step, taking the stealthy characteristics of the BLIA into account, a robust adaptive observer-based detection algorithm is proposed. Through of capabilities of observers, we can estimate the physical dynamics accurately. To detect the BLIA quickly and avoid missed alarm, we compute the adaptive threshold as a substitute for the precomputed threshold, by taking the model linearization error and external disturbance into account. In the third step, we propose a logical judgment matrix to address the sensor attack undetectability problem under structure vulnerability, based on the combinations of all observable sensors. Finally, the effectiveness of the proposed detection and location framework is illustrated, by using detailed case studies on the IEEE 55-bus smart grid system.
Xinyu Wang; Xiaoyuan Luo; Xueyang Pan; Xinping Guan. Detection and Location of Bias Load Injection Attack in Smart Grid via Robust Adaptive Observer. IEEE Systems Journal 2020, 14, 4454 -4465.
AMA StyleXinyu Wang, Xiaoyuan Luo, Xueyang Pan, Xinping Guan. Detection and Location of Bias Load Injection Attack in Smart Grid via Robust Adaptive Observer. IEEE Systems Journal. 2020; 14 (3):4454-4465.
Chicago/Turabian StyleXinyu Wang; Xiaoyuan Luo; Xueyang Pan; Xinping Guan. 2020. "Detection and Location of Bias Load Injection Attack in Smart Grid via Robust Adaptive Observer." IEEE Systems Journal 14, no. 3: 4454-4465.
This paper investigates the detection and isolation of false data injection (FDI) attacks in smart grid based on the unknown input (UI) interval observer. Recent studies have shown that the FDI attacks can bypass the traditional bad data detection methods by using the vulnerability of state estimation. For this reason, the emergency of FDI attacks brings enormous risk to the security of smart grid. To solve this crucial problem, an UI interval observer-based detection and isolation scheme against FDI attacks is proposed. We first design the UI interval observers to obtain interval state estimation accurately, based on the constructed physical dynamics grid model. Through the capabilities of the designed UI interval observers, the accurate interval estimation state can be decoupled from unknown disturbances. Based on the characteristics of interval residuals, an UI interval observer-based global detection algorithm is proposed. Particulary, the interval residual-based detection criteria can address the limitation of the precomputed threshold in traditional bad data detection methods. On this basis, we further consider the detection and isolation of FDI attacks under structure vulnerability. Namely, there exist undetectable FDI attacks in grid system. Taking the attack undetectability problem into account, a logic judgment matrix-based local detection and isolation algorithm against FDI attacks is developed. Based on the combinations of observable sensor cases, local control centers can further detect and isolate the attack set under structure vulnerability. Finally, the effectiveness of the developed detection and isolation algorithms against FDI attacks is demonstrated on the IEEE 8-bus and IEEE 118-bus smart grid system, respectively.
Xinyu Wang; Xiaoyuan Luo; Mingyue Zhang; Zhongping Jiang; Xinping Guan. Detection and Isolation of False Data Injection Attacks in Smart Grid via Unknown Input Interval Observer. IEEE Internet of Things Journal 2020, 7, 3214 -3229.
AMA StyleXinyu Wang, Xiaoyuan Luo, Mingyue Zhang, Zhongping Jiang, Xinping Guan. Detection and Isolation of False Data Injection Attacks in Smart Grid via Unknown Input Interval Observer. IEEE Internet of Things Journal. 2020; 7 (4):3214-3229.
Chicago/Turabian StyleXinyu Wang; Xiaoyuan Luo; Mingyue Zhang; Zhongping Jiang; Xinping Guan. 2020. "Detection and Isolation of False Data Injection Attacks in Smart Grid via Unknown Input Interval Observer." IEEE Internet of Things Journal 7, no. 4: 3214-3229.
Target tracking has been considered as one of the most important applications of underwater acoustic sensor networks. However, the long propagation delay, high-energy consumption, and strong noise properties of the underwater environment make target tracking more challenging as compared with terrestrial sensor networks. This article is concerned with an energy-efficient tracking issue for underwater targets, subject to an asynchronous clock, power restriction, and noise measurement constraints. The tracking process can be divided into two phases, i.e., position acquisition and persistent tracking. In the first phase, we establish the relationship between propagation delay and position, through which an asynchronous localization algorithm is developed for sensor nodes to estimate the position of target. Based on the estimated position, a consensus-based Bayesian filter is designed for sensor nodes in the second phase to enable persistent tracking. In particular, the consensus fusion strategy and duty-cycle mechanism are jointly adopted to improve the tracking accuracy and prolong the network lifetime. Moreover, the convergence analyses for the proposed approach are also presented. Finally, simulation and experimental results reveal that the proposed tracking approach can reduce the influence of malicious measurements, while the energy efficiency can be significantly improved as compared with the other works.
Jing Yan; Haiyan Zhao; Bin Pu; Xiaoyuan Luo; Cailian Chen; Xinping Guan. Energy-Efficient Target Tracking With UASNs: A Consensus-Based Bayesian Approach. IEEE Transactions on Automation Science and Engineering 2019, 1 -15.
AMA StyleJing Yan, Haiyan Zhao, Bin Pu, Xiaoyuan Luo, Cailian Chen, Xinping Guan. Energy-Efficient Target Tracking With UASNs: A Consensus-Based Bayesian Approach. IEEE Transactions on Automation Science and Engineering. 2019; (99):1-15.
Chicago/Turabian StyleJing Yan; Haiyan Zhao; Bin Pu; Xiaoyuan Luo; Cailian Chen; Xinping Guan. 2019. "Energy-Efficient Target Tracking With UASNs: A Consensus-Based Bayesian Approach." IEEE Transactions on Automation Science and Engineering , no. 99: 1-15.
With the integration in information and communication technologies, and advanced metering infrastructure, smart energy grid, as one of typical sustainable energy systems, addresses the energy and environment problems. However, the emergency of bias injection attack aiming at destroying the energy management center, brings great security threat to the security of smart energy grid. To address risks in energy-cyber-physical systems, this paper proposes a distributed detection and isolation scheme against the bias injection attack in smart energy grid. Considering the transmitted information of energy management centers in adjacent grid subareas, the proposed distributed detection and isolation scheme includes local and global steps. In the local-step, each local energy management center detects and isolates the possible sensor attack set, based on the constructed local attack signature judgment logic matrix. In the global-step, the subarea attack set is detected and isolated via the established global attack signature judgment logic matrix. Combining the above local and global detection and isolation framework, we can ensure the security of energy management center in smart energy system. This proposed distributed detection and isolation scheme examines some important practical aspects of deploying bias injection attack detection including: the limitation of the precomputed threshold; the detection delay; the accuracy in detecting bias injection attack. Finally, the effectiveness of the developed distributed detection and isolation scheme is demonstrated by using detailed studies on the IEEE 8-bus and IEEE 118-bus smart energy grid system.
Xiaoyuan Luo; Xinyu Wang; Mingyue Zhang; Xinping Guan. Distributed detection and isolation of bias injection attack in smart energy grid via interval observer. Applied Energy 2019, 256, 113703 .
AMA StyleXiaoyuan Luo, Xinyu Wang, Mingyue Zhang, Xinping Guan. Distributed detection and isolation of bias injection attack in smart energy grid via interval observer. Applied Energy. 2019; 256 ():113703.
Chicago/Turabian StyleXiaoyuan Luo; Xinyu Wang; Mingyue Zhang; Xinping Guan. 2019. "Distributed detection and isolation of bias injection attack in smart energy grid via interval observer." Applied Energy 256, no. : 113703.
The paper studies the consensus of event-triggered for second-order multi-agent systems (MASs) with or without input time delay. Based on the designed triggering function, a distributed event-triggered control strategy is presented to drive the system to achieve consensus. For each agent, controller update only needs the neighbor’s information communication and its own error value, thus continuous communication among agents is not necessary. Communication energy can be saved as the agents send their state information only at infrequent event instants. To demonstrate the asymptotical stability of the closed-loop error system, S-procedure approach is used by combing Lyapunov stability theories with linear matrix inequality (LMI) technique. Two sufficient conditions are derived by solving LMIs for achieving second-order consensus under the cases of with or without input time delay, respectively.Numerical examples are presented to verify the effectiveness of the proposed controllers.
Jinran Wang; Xiaoyuan Luo; Jing Yan; Xinping Guan. Event-Triggered Consensus Control for Second-Order Multi-Agent Systems With/Without Input Time Delay. IEEE Access 2019, 7, 156993 -157002.
AMA StyleJinran Wang, Xiaoyuan Luo, Jing Yan, Xinping Guan. Event-Triggered Consensus Control for Second-Order Multi-Agent Systems With/Without Input Time Delay. IEEE Access. 2019; 7 (99):156993-157002.
Chicago/Turabian StyleJinran Wang; Xiaoyuan Luo; Jing Yan; Xinping Guan. 2019. "Event-Triggered Consensus Control for Second-Order Multi-Agent Systems With/Without Input Time Delay." IEEE Access 7, no. 99: 156993-157002.
The platoon control for vehicles can efficiently reduce traffic pressure. Current control schemes are able to achieve stable platoon control, but cannot guarantee collision avoidance, connectivity preservation and convergence time at the same time. In this paper, we propose a novel control scheme that can simultaneously guarantee the safe distance and communication connectivity, which is called flexible safe distance constraint, and also can guarantee the specified convergence time. A dynamic gain based distributed control law is first designed for the vehicle with single integral model. Using a prescribed transient and steady state performance control approach, the flexible safe distance constraint can be satisfied. Using the time transformation method and Lyapunov stability theory, the closed-loop systems under the proposed protocols can achieve stable platoon within the specified time. Compared with some existing results, the novelty of this work is that the proposed approaches allow a vehicle to converge with the platoon within any preset time without dependence on the initial conditions or system parameters. Another contribution of this work is to propose specified-time platoon control protocols for the vehicle with double-integral model under flexible safe distance constraint. Furthermore, in order to get closer to the actual system, the controller is extended to the triple-integral system, and some special cases are also discussed. Finally, some simulations are presented to show the effectiveness of the proposed protocols.
Jiange Wang; Xiaoyuan Luo; Wai-Choong Wong; Xinping Guan. Specified-Time Vehicular Platoon Control With Flexible Safe Distance Constraint. IEEE Transactions on Vehicular Technology 2019, 68, 10489 -10503.
AMA StyleJiange Wang, Xiaoyuan Luo, Wai-Choong Wong, Xinping Guan. Specified-Time Vehicular Platoon Control With Flexible Safe Distance Constraint. IEEE Transactions on Vehicular Technology. 2019; 68 (11):10489-10503.
Chicago/Turabian StyleJiange Wang; Xiaoyuan Luo; Wai-Choong Wong; Xinping Guan. 2019. "Specified-Time Vehicular Platoon Control With Flexible Safe Distance Constraint." IEEE Transactions on Vehicular Technology 68, no. 11: 10489-10503.