Article
Automation & Control Systems
Xiao Lu, Yuanyu Cai, Hongxia Wang, Haixia Wang, Guilin Zhang, Xiao Liang
Summary: This paper addresses the optimal control problem for networked control systems with Markovian packet dropouts. While previous studies only consider one-way Markovian packet dropouts, this study aims to provide a complete solution for the two-way Markovian packet dropouts. By utilizing the Pontryagin's maximum principle and mathematical induction method, a solution to the forward and backward stochastic difference equations is derived. Furthermore, the necessary and sufficient condition for the optimal control problem is obtained, and the optimal controller is determined based on the complete square method. Numerical examples are provided to illustrate the effectiveness of the proposed theory.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Automation & Control Systems
Wenli Chen, Xiaojian Li
Summary: This paper investigates data-driven control for networked control systems with multiple packet dropouts and unknown system parameters. The paper establishes a model-based mean-square asymptotic stability condition and a data-driven mean-square asymptotic stability condition for the closed-loop system. Additionally, it presents data-driven controller gain design approaches for combining unknown and known input gain matrices. Two simulation examples are provided to demonstrate the applicability of the developed approaches.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Weixiang Zhou, Yueying Wang, Yinzheng Liang
Summary: In recent years, there has been increasing attention on the control synthesis and analysis of networked control systems (NCSs), and many research contributions have been published. Sliding mode control (SMC) is an effective method to address uncertainties and nonlinear characteristics in NCSs due to its complete robustness on the sliding mode surface. This paper provides a review of the recent advances and challenges of SMC in NCSs, along with potential future research topics.
Article
Computer Science, Information Systems
Bin Li, Xinglian Zhou, Zhaoke Ning, Xiaoyi Guan, Ka-Fai Cedric Yiu
Summary: This article investigates a dynamic event-triggered security control problem in networked control systems under deception attacks and packet dropouts. A combined cyberattack model is proposed to reflect randomly occurring cyber-attacks. A dynamic event-triggered protocol is constructed to reduce data transmission and relieve bandwidth pressure. An online model predictive control algorithm is established to ensure stochastic stability and expected performance. Two examples are simulated to verify the effectiveness of the proposed design strategy.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Hao-Yuan Sun, Hong-Gui Han, Jian Sun, Jun-Fei Qiao
Summary: This paper investigates the observer-based sampled-data control problem for networked systems with consecutive packet dropouts. A new consecutive packet dropout model is established to describe the phenomenon of packet dropout in both the sensor-controller (S-C) and controller-actuator (C-A) channels. The effectiveness of the proposed observer-based sampled-data control approach is verified using a numerical example, and the advantages of the proposed approach are validated through comparisons.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
El Hacene Djallel Lakehal Ayat, Noura Mansouri, Abdelkrim Boukabou
Summary: This paper addresses the problem of robust stabilization for networked control systems (NCS) using Takagi-Sugeno (T-S) fuzzy systems. A time-varying delay fuzzy controller is designed to achieve robust exponential stability with a prescribed decay rate. The proposed method is validated through numerical examples.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Xiongbo Wan, Tizhuang Han, Jianqi An, Min Wu
Summary: This paper investigates fault detection in singularly perturbed systems with data transmitted over a limited bandwidth communication network using a hidden Markov model. A homogeneous Markov chain is used to model packet dropouts and time delays, with a focus on designing an HMM-based FD filter for system stability and performance. By introducing a new Lyapunov-Krasovskii function and linear matrix inequalities, a design scheme for the FDF and evaluation criteria for system performance are provided.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Xiao Lu, Ruidong Liu, Chuanzhi Lv, Na Wang, Qiyan Zhang, Haixia Wang, Guilin Zhang, Xiao Liang
Summary: This paper focuses on the optimal output feedback control problem for networked control systems with various interferences, overcoming barriers of packet dropouts and measurement delays. It provides optimal estimator and controller, and demonstrates the effectiveness of the algorithm through numerical examples.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Automation & Control Systems
Xiaowei Jiang, Jianhao Li, Bo Li, Xiangyong Chen, Huaicheng Yan
Summary: This study focuses on the optimal tracking performance (OTP) of networked control systems (NCSs) considering packet dropouts and noise constraints in communication networks, and provides explicit expressions for the OTP limitation under these constraints. The results indicate that the intrinsic features of the plant and the communication parameters of the network channel will affect the OTP of the NCSs.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Mathematics
Fan Zhang, Mingang Hua, Mengyu Gao
Summary: This paper studies the design of a dynamic output feedback controller for a networked control system with dual-channel data packet loss and special discrete-time delay, where the data packet loss is described by the Markov process. To effectively alleviate network congestion, a quantizer was added to the sensor-to-controller channel. The transition probabilities of the Markov process are uncertain but exist in the convex sets of known convex polyhedron types. Mode-dependent Lyapunov function was constructed, and a sufficient condition was given to ensure the stochastic stability and performance index satisfaction of the closed-loop system. The controller parameters were solved using the linear matrix inequality method. Finally, an aircraft example demonstrates the effectiveness of the proposed approach, and a numerical example shows its superiority compared to other literature.
Article
Automation & Control Systems
Chengchao Li, Xudong Zhao, Chunyu Wu, Le Liu, Ning Zhao
Summary: This paper proposes a co-design method for dynamic output feedback control and periodic event-triggered control in networked control systems, to tolerate a certain number of consecutive packet dropouts. By introducing a periodic event-triggering mechanism and modeling the closed-loop system as a hybrid system, the stability conditions and specific design parameters are used to ensure system stability.
Article
Mathematics, Applied
Chengchao Li, Chunyu Wu, E. Abozinadah, Madini O. Alassafi, Ning Xu
Summary: This paper investigates the output-based event-triggered control problem of discrete-time networked control systems subject to bilateral packet dropouts. The NCS is converted into a closed-loop stochastic parameter system considering the stochastic sequences of packet dropouts. Sufficient conditions are derived using a Lyapunov functional based on stochastic variables to guarantee the exponentially mean-square stability. An improved iterative algorithm is proposed to obtain the output-based control law and event-triggering parameters.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Huiying Chen, Renwei Liu, Ping He, Zuxin Li
Summary: This paper focuses on the asynchronous dissipative control of networked time-delay Markov jump systems using an event-triggered transmission scheme and the Bernoulli model to describe packet dropout during communication. By using a mode-dependent Lyapunov-Krasovskii function, a sufficient condition for the stochastic stability and strict (mu, theta, nu)-gamma-dissipativity of the closed-loop control system is obtained. Furthermore, the design of the dissipative controller is simplified using matrix scaling and slack matrix techniques. The effectiveness of the proposed design method is verified using a robotic arm system as an example.
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
(2022)
Article
Automation & Control Systems
Yuanyu Cai, Xiao Lu, Haixia Wang, Xiao Liang
Summary: This article studies the optimal control for networked control systems subject to Markovian packet dropouts and input delay. The solutions to the forward and backward stochastic difference equations are obtained using Pontryagin's maximum principle. Sufficient and necessary optimal control conditions are derived and the explicit expression of the optimal controller is presented.
ASIAN JOURNAL OF CONTROL
(2023)
Article
Automation & Control Systems
Hong-Tao Sun, Chen Peng, Maoli Wang, Min Zhao
Summary: This paper investigates the input to state stabilizing control of networked control systems (NCSs) with a specified packet dropout rate. The study considers two transmission intervals in NCSs based on the occurrence of packet dropouts: small delay intervals (packet-dropout-free case) and large delay intervals (packet-dropout case). The concept of average packet dropout rate (ADR) is introduced to characterize the quality of service (QoS) for networks. The paper presents a switched systems approach to derive the input to state stability (ISS) conditions for a specified ADR using Lyapunov theory and input delay approach. The controller design method for NCSs under a specified ADR is obtained by solving linear matrix inequalities (LMIs). A control and communication co-design method is proposed to design the controller gain based on QoS. Finally, simulations on self-steering control of autonomous vehicles are conducted to verify the effectiveness of the proposed co-design method.
Article
Automation & Control Systems
Chun Kiat Tan, Jian Liang Wang, Yew Chai Paw, Fang Liao
INTERNATIONAL JOURNAL OF CONTROL
(2019)
Article
Automation & Control Systems
Xiang-Gui Guo, Jun-Jie Zhao, Hong-Jian Li, Jian-Liang Wang, Fang Liao, Yang Chen
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2020)
Article
Automation & Control Systems
Xiang-Gui Guo, Wei-Dong Xu, Jian-Liang Wang, Ju H. Park
Summary: This paper investigates neuroadaptive fault-tolerant control of nonlinear vehicular platoon systems subject to unknown direction actuator faults, unmodeled dynamics, and external disturbances. A novel control scheme is designed to ensure the finite-time stability of the entire vehicular platoon and tolerate unknown direction actuator faults. Simulation results demonstrate the effectiveness and advantages of the proposed scheme.
Article
Computer Science, Artificial Intelligence
Xiang-Gui Guo, Xiao Fan, Jian-Liang Wang, Ju H. Park
Summary: This article investigates an adaptive event-triggered fault detection and isolation scheme for nonlinear networked control systems under DoS attacks, utilizing a novel triggering mechanism and a switching state-feedback controller. The proposed approach demonstrates effectiveness in simulation cases and offers advantages in saving communication resources.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Information Systems
Xiang-Gui Guo, Pei-Ming Liu, Hong-Jian Li, Jian-Liang Wang, Choon Ki Ahn
Summary: This paper investigates the cluster synchronization problem of a heterogeneous second-order leader-following multi-agent system with nonlinear dynamics, actuator faults, and integral quad-ratic constraints under a directed topology with a directed spanning tree. Two adaptive fault-tolerant pinning control strategies are proposed to guarantee cluster synchronization in finite time, along with an adaptive input compensation method to mitigate the adverse effects of actuator faults. These strategies effectively reduce computational cost while demonstrating effectiveness and advantages in large-scale multi-agent systems through numerical simulation examples.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Ruibing Jin, Guosheng Lin, Changyun Wen, Jianliang Wang, Fayao Liu
Summary: This paper introduces a novel network structure (IFF-Net) with an In-network Feature Flow estimation module (IFF module) for video object detection, which can directly produce feature-level motion information without pre-training on additional datasets. It efficiently and accurately detects objects, and further improves performance through a transformation residual loss (TRL).
PATTERN RECOGNITION
(2022)
Article
Automation & Control Systems
Xianggui Guo, Dongyu Zhang, Jianliang Wang, Choon Ki Ahn
Summary: This study investigates the event-triggered security consensus problem for nonlinear multi-agent systems under denial-of-service attacks over an undirected graph. An adaptive memory observer-based anti-disturbance control scheme is proposed to improve observer accuracy and provide reasonable control signals during DoS attacks. An adaptive memory event-triggered mechanism is also introduced to save network resources and exclude Zeno behavior, with observer and controller gains obtained using LMI techniques. Simulation results demonstrate the effectiveness of the control scheme.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Automation & Control Systems
Xiang-Gui Guo, De-Chu Tan, Choon Ki Ahn, Jian-Liang Wang
Summary: This article proposes two novel fully distributed adaptive fault-tolerant control strategies to handle the leader-following consensus problem of nonlinear multiagent systems with integral quadratic constraints and actuator faults. The strategies combine the skills of the pseudo-PID sliding-mode control method with adaptive control techniques and are designed for systems with and without asymmetric nonlinear actuator saturations. Simulation results demonstrate the effectiveness of the proposed approaches.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Engineering, Multidisciplinary
Xiang-Gui Guo, Dong-Yu Zhang, Jian-Liang Wang, Ju H. Park, Lei Guo
Summary: This paper investigates the problem of event-triggered observer-based security consensus and fault detection for nonlinear multi-agent systems under external disturbances and stochastic false data injection attacks. A strategy using only local measurements and information from neighboring agents is developed to improve the accuracy of the observer and the performance of the fault detection mechanism. Simulation results demonstrate the effectiveness and advantages of the proposed strategy.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Civil
Xiang-Gui Guo, Wei-Dong Xu, Jian-Liang Wang, Ju H. Park, Huaicheng Yan
Summary: This paper investigates the neuroadaptive fault-tolerant control of a nonlinear vehicular platoon with unmodeled dynamics, external disturbances, time-varying actuator fault directions, and distance restrictions. Two neuroadaptive fault-tolerant controllers are designed based on adaptive terminal sliding mode control technique and barrier Lyapunov function to ensure reliability and safety. The proposed scheme effectively solves the influence of unknown time-varying fault directions and ensures spacing error convergence through nonsingular TSM control technique and RBFNN method.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Xiang-Gui Guo, Dong-Yu Zhang, Jian-Liang Wang, Ju H. Park, Lei Guo
Summary: This article investigates the security consensus and composite anti-disturbance problems for nonlinear multi-agent systems under stochastic false data injection attacks and multiple disturbances. Disturbance observer and H $_{infinity}$ control method are designed to attenuate the negative effects of disturbances. An observer-based control strategy and a novel adaptive compensation technique are proposed to ensure consensus performance. A novel event-triggered mechanism is developed to reduce controller update frequency and communication burden.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Xiang-Gui Guo, Pei-Ming Liu, Jian-Liang Wang, Choon Ki Ahn
Summary: This paper presents an event-triggered cluster consensus scheme for heterogeneous nonlinear second-order multiagent systems subject to cyber attacks and actuator faults. The proposed control scheme does not require the communication topology to satisfy the in-degree balance between different clusters, and an event-triggered mechanism is developed to save network resources and exclude Zeno behavior. Simulation results confirm the effectiveness and superiority of the proposed control scheme.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Proceedings Paper
Automation & Control Systems
Xiao Fan, De-Chu Tan, Xiang-Gui Guo, Jian-Liang Wang, Yan-Hua Yang
2020 IEEE 16TH INTERNATIONAL CONFERENCE ON CONTROL & AUTOMATION (ICCA)
(2020)
Article
Engineering, Electrical & Electronic
Ruibing Jin, Guosheng Lin, Changyun Wen, Jianliang Wang
IEEE SIGNAL PROCESSING LETTERS
(2020)
Proceedings Paper
Automation & Control Systems
Fang Liao, Pengfei Wang, Kemao Peng, Rodney Teo, Feng Lin, Jianliang Wang
2019 18TH EUROPEAN CONTROL CONFERENCE (ECC)
(2019)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
