Article
Automation & Control Systems
Xinyu Zhao, Hao Chen, Zhenzhen Zhang, Shiyu Dong, Shouming Zhong, Zhiyu You
Summary: This paper addresses the H-infinity control problem of the nonlinear asynchronous switched system with mixed time-varying delays and exogenous disturbance. It proposes a novel Zeno-free controller mode-dependent dynamic event-triggered strategy to reduce unnecessary system operation costs. By applying the merging switching signal approach and multiple Lyapunov functional method, sufficient conditions are derived to ensure the H(infinity) performance of the nonlinear asynchronous delayed switched system. The feasibility and application of the proposed results are illustrated through numerical and practical examples.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Mathematics
Mourad Kchaou, Houssem Jerbi, Dan Stefanoiu, Dumitru Popescu
Summary: This paper examines the fault-tolerant control problem for discrete-time descriptor systems that are susceptible to intermittent actuator failures, nonlinear sensor data, and probability-based missing data. The theoretical developments are illustrated through numerical simulations of an infinite machine bus.
Article
Automation & Control Systems
Yali Dong, Meng Liu
Summary: This paper addresses the issues of finite-time boundedness and H∞ control for a class of uncertain systems with time-varying delays and exogenous disturbance. New sufficient conditions for finite-time boundedness are derived in terms of linear matrix inequalities (LMIs), by using Lyapunov-Krasovskii functional (LKF) and a new integral inequality. The output feedback controller is designed to ensure both finite-time boundedness and H∞ performance index for the closed-loop system. Numerical examples are provided to illustrate the effectiveness of the proposed methods.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Automation & Control Systems
Zhengbao Cao
Summary: This article studies the QSR-dissipativity of feedback interconnection of switched nonlinear systems via event-triggered control, and proposes a control scheme to ensure the QSR-dissipativity of the systems. Zeno behavior is excluded and the maximum number of triggers is estimated.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Mathematics, Applied
Hadi Gholami, Mohammad Hossein Shafiei
Summary: This paper designs static and dynamic output feedback controllers for a class of switched nonlinear time-delay systems to achieve finite-time boundedness under the presence of disturbances and uncertainties. An H-infinity index with respect to disturbances is guaranteed using auxiliary matrices and the average dwell time method. The theorems presented in this paper are less conservative and provide more degrees of freedom for feasible solutions compared to existing literature.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Automation & Control Systems
Feng Li, Zhenghao Ni, Lei Su, Jianwei Xia, Hao Shen
Summary: This paper addresses the problem of finite-region passive control for 2-D Markov jump Roesser systems, considering the partial statistical information issues on Markov parameters and transition probabilities. A 2-D hidden Markov model with partial statistical information is established to model this situation. The goal is to design a controller based on the 2-D hidden Markov model that ensures finite-time boundedness of both horizontal and vertical states of the 2-D Markov jump Roesser systems, while meeting a passive performance criterion. By employing the Lyapunov function method, criteria for the finite-region boundedness of 2-D Markov jump Roesser systems are developed, and a design method for the asynchronous controller based on the 2-D hidden Markov model is presented. The effectiveness of the proposed design method is validated through an illustrative example.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2024)
Article
Automation & Control Systems
Jing Wei, Bao-Zhu Guo
Summary: This paper considers the boundary output regulation problem for a one-dimensional reaction-diffusion equation with disturbances from both the domain and the boundaries. The control strategy involves designing a feedforward controller based on an infinite-dimensional regulator equation and a backstepping transformation, as well as an observer to estimate the states of the plant and the external system. The output feedback boundary control is then designed using the estimated states, ensuring exponential convergence to the reference signal over time.
Article
Computer Science, Information Systems
Mengran Li, Yang Yu, Huabo Liu
Summary: In this paper, the finite-time boundedness problems for large-scale continuous-time networked dynamical systems with arbitrary interactions and different dynamics of subsystems are investigated. Sufficient conditions for the design of finite-time boundedness state feedback controller are derived, utilizing the block-diagonal structure of system parameter matrices and the sparseness of system topology. Conditions depending only on parameter matrices of individual subsystems are also provided. The distributed output feedback controller with finite-time boundedness is also designed and its conditions are derived. Numerical simulations are conducted to validate the effectiveness of the derived conditions for a large-scale networked system.
Article
Mathematics, Applied
Jiacheng Wu, Lei Su, Shaoming Li, Jing Wang, Xiangyong Chen
Summary: This article presents an extended dissipative filtering method for slow sampling singularly perturbed systems based on a double-layer switching mechanism. The method utilizes a Markov jump signal and a persistent dwell-time switching signal to capture system mode transitions and variations in transition probabilities. Uncertainties in actual measurement outputs are described by a set of Bernoulli distributed white sequences. Through Lyapunov stability theory and advanced linear matrix transformation techniques, sufficient conditions for mean-square exponentially stable filtering error systems with extended dissipative performance are derived. A simulation example is provided to demonstrate the effectiveness of the proposed method.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Automation & Control Systems
Diego de S. Madeira
Summary: This article presents necessary and sufficient conditions for exponential stabilizability of non-linear systems using the notion of exponential QSR-dissipativity. It shows that the exponential stabilization of the closed-loop system is equivalent to the exponential QSR-dissipativity of the plant. The article also introduces new conditions and a controller design strategy for linear systems using static output feedback.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Xiangze Lin, Chih-Chiang Chen
Summary: This paper presents an output feedback control strategy for planar switched systems with or without an output constraint to achieve finite-time stability. By revamping the adding a power integrator technique, state feedback controllers are systematically designed. Deliberately constructed reduced-order switched observers ensure finite-time output feedback stabilization with guaranteed output constraint. The method proposed in this paper works in a unified form for planar switched systems with or without an output constraint, without changing controllers and observers structures.
Article
Automation & Control Systems
Adam Jbara
Summary: This paper proposes a family of homogeneous controllers that utilize noisy state measurements to stabilize integrator chains subject to system faults and disturbances. Despite the presence of system faults, exact system stabilization is ensured in the absence of sampling noises. The same controllers also have practical stabilization capabilities for generally disturbed integrator chains, demonstrate strong noise reduction capabilities, and robustly detect system faults. The accuracy of stabilization is estimated in the presence of noises and faults, and simulation results confirm the theoretical findings.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Chenhong Zhu, Xiaodi Li, Jinde Cao
Summary: This paper addresses the issues of finite-time bounded (FTB) and finite-time H-infinity control for nonlinear impulsive switched systems by proposing sufficient conditions and designing a hybrid dynamic output feedback controller. The effectiveness of the results is demonstrated through numerical examples.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2021)
Article
Automation & Control Systems
Jie Wang, Baoli Ma, Kai Yan
Summary: This paper presents a control scheme for a robot to track an unknown target in both obstacle-free and obstacle-presence environments. A dynamic output feedback controller is derived for the obstacle-free case to ensure the convergence of the distance between the robot and the target. For the obstacle-presence case, a collision-allowed control strategy is developed to enable the robot to escape from obstacles and maintain a given distance from the target.
Article
Engineering, Electrical & Electronic
R. Sakthivel, V Nithya, V. T. Suveetha, F. Kong
Summary: This paper presents a solution to the problem of finite-time dissipative-based distributive non-fragile filter design for a class of discrete-time complex systems subject to randomly occurring multiple delays, dynamic quantization, and missing measurements. The proposed distributive non-fragile filter ensures stochastic finite-time boundedness and prescribed dissipative performance in the presence of multiple delays. Stochastic variables are introduced to characterize the random nature of delays, and missing measurements and dynamic quantization are implemented in the measurement signal. By employing S-procedure and constructing a proper Lyapunov-Krasovskii functional, a set of linear matrix inequality (LMI)-based sufficient conditions that guarantee the stochastic finite-time boundedness with dissipative performance is obtained for the augmented filtering error system. The efficiency of the proposed distributive non-fragile filter design is demonstrated through three numerical examples, including a continuous stirred tank reactor (CSTR) and a quarter-car suspension model.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2022)
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)