Article
Engineering, Mechanical
Shengzeng Zhang, Xiongxiong He, Haiyue Zhu
Summary: This paper investigates the nonlinear control of overhead cranes, focusing on the constraints of the state variable. A novel coupling control design is proposed, which employs barrier functions to preserve all states within asymmetric limits and enhance antisway effectiveness. The proposed controller demonstrates robustness to parametric uncertainties and achieves asymptotic stability, as verified by experiments.
NONLINEAR DYNAMICS
(2023)
Article
Computer Science, Information Systems
Fangfang Dong, Dong Jin, Xiaomin Zhao, Jiang Han
Summary: An adaptive robust control method based on Udwadia-Kalaba theory is proposed for trajectory tracking of an omnidirectional mobile robot, compensating for uncertainties in the system and outperforming traditional PID control in terms of precision and performance.
Article
Computer Science, Information Systems
Xiaoli Liu, Qilin Wu, Shengchao Zhen, Han Zhao, Chuanyang Li, Ye-Hwa Chen
Summary: In this paper, a robust constraint-following control (RCFC) method with optimal design is proposed to address trajectory tracking control issues for permanent magnet linear motors. By utilizing fuzzy description and Lyapunov analysis, the controller achieves a balance between control effort and system performance while ensuring strong robustness to uncertainties.
INFORMATION SCIENCES
(2022)
Article
Multidisciplinary Sciences
Osama A. Choudhry, Muhammad Wasim, Ahsan Ali, Mohammad Ahmad Choudhry, Jamshed Iqbal
Summary: A literature review is conducted on self-balancing robots, and a method based on extended Kalman filter and sliding mode controller is proposed to improve the control performance and stability of two-wheeled self-balancing robots. Through comparing simulation results and other techniques, the significance and advantages of the proposed method are demonstrated.
Article
Engineering, Civil
Zeyu Yang, Jin Huang, Hui Yin, Diange Yang, Zhihua Zhong
Summary: This paper proposes a robust path tracking control method using a constraint-following approach to guide underactuated autonomous vehicles towards desired paths. By decomposing uncertainties and estimating matched uncertainty, a robust control method is designed to achieve excellent tracking performance even in the presence of time-varying uncertainties.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Acoustics
Amir Alihosseini, Nima Mahdian Dehkordi, Mohammadreza Sajjadi
Summary: In this paper, a novel free chattering robust nonlinear sliding mode control based on the Lyapunov theory for underactuated two-wheels mobile robots is proposed. The salient features of the proposed control method are: (1) considering the full dynamics of the system, disturbances, and uncertainties; (2) not considering the small signal model of the system that leads to the large signal instability; and (3) resulting in less costs, least chattering, easy implementation, and no stability issues. The proposed nonlinear controller is designed based on Lyapunov and Lassalle theory. Moreover, the proposed controller is shown to be robust against disturbances and uncertainties.
JOURNAL OF VIBRATION AND CONTROL
(2023)
Article
Automation & Control Systems
Esteban Restrepo, Antonio Loria, Ioannis Sarras, Julien Marzat
Summary: We address the problem of output- and state-consensus for multiagent high-order systems in feedback form. We consider systems interconnected over arbitrary undirected topology networks, directed spanning-trees, and directed cycles. The systems may have output or state constraints and may be subject to external disturbances. We propose a control framework and a formal analysis that guarantee robust stability in the input-to-state sense. The control framework is based on a modified backstepping method, while the analysis relies on multistability theory. We apply our approach to a case-study on safety-aware rendezvous control of underactuated UAVs in the aerospace industry.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Engineering, Mechanical
ShengChao Zhen, Xin Peng, XiaoLi Liu, HongMei Li, Ye-Hwa Chen
Summary: A PD-based robust control method is proposed in this study to improve the dynamic performance of the robot joint module system. The controller is shown to be effective through theoretical analysis and experimental verification with different friction models.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Automation & Control Systems
Kamal Rsetam, Zhenwei Cao, Zhihong Man
Summary: The article introduces a terminal sliding mode control for flexible joint robot systems to ensure finite-time convergence of the system output and achieve total robustness against disturbances and errors. The study uses two coordinate transformations and a finite-time sliding mode observer to estimate states and disturbances, with closed-loop stability and finite-time convergence rigorously proven using Lyapunov theorem. The proposed control method's effectiveness is verified through real-time comparative study on FJR manipulators.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Mohammad Pourmahmood Aghababa
Summary: This research focuses on the control problem of partially unknown nonlinear switched systems, with control algorithm based on variable structure control theory to handle mismatching uncertainties and gain deviations that weaken control effectiveness. The robust performance of the control strategy is confirmed through two illustrative examples.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Duo Fu, Jin Huang, Wen-Bin Shangguan, Hui Yin
Summary: This article presents a novel constraint-following servo control approach for underactuated mobile robots, which incorporates hard constraints into soft constraints to drive the system to strictly follow the original soft and hard constraints. The effectiveness of the proposed approach is verified through rigorous proof and simulations.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING
(2022)
Article
Automation & Control Systems
Sasa Rakovic
Summary: This article introduces and studies the robust control Minkowski-Lyapunov inequality, a natural generalization of the control Minkowski-Lyapunov inequality. The necessary and sufficient conditions for verifying the Minkowski functions in the robust control Minkowski-Lyapunov inequality are derived, and the topological properties of the corresponding robustly stabilizing set-valued control map are characterized and established.
Article
Acoustics
ShengChao Zhen, MuCun Ma, XiaoLi Liu, Feng Chen, Han Zhao, Ye-Hwa Chen
Summary: In this paper, a novel robust control method is designed to reduce trajectory tracking errors of the SCARA robot with uncertainties. The proposed control algorithm can handle time-varying and nonlinear uncertainties and is proved to be stable according to the Lyapunov method. Experimental results demonstrate its effectiveness on the SCARA robot.
JOURNAL OF VIBRATION AND CONTROL
(2023)
Article
Computer Science, Information Systems
P. Parsa, M-R Akbarzadeh-T, F. Baghbani
Summary: This study introduces a command-filtered backstepping H1 robust adaptive emotional controller for strict-feedback nonlinear systems with mismatched uncertainties. The controller utilizes command filters and compensating filters to handle matched/mismatched uncertainties and disturbances, requiring only the known and continuous reference signal and its first derivative.
INFORMATION SCIENCES
(2021)
Article
Engineering, Mechanical
Kang Huang, Yuanjie Xian, Shengchao Zhen, Hao Sun
Summary: This study proposes a nonlinear control algorithm based on engineering practice for uncertainties and variable loads of a robot arm, including PD control and robust control parts. Experimental results show that the proposed control method can effectively deal with load changes and parameter uncertainties, significantly improving the trajectory tracking accuracy of the robot.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Multidisciplinary
Bohao Cai, Wen-Bin Shangguan, Hui Lu
ENGINEERING OPTIMIZATION
(2020)
Article
Engineering, Multidisciplinary
Hui Yin, Ye-Hwa Chen, Dejie Yu, Hui Lu, Wenbin Shangguan
APPLIED MATHEMATICAL MODELLING
(2020)
Article
Engineering, Mechanical
Hui Lu, Kun Yang, Xiaoting Huang, Hui Yin
Summary: In this paper, a possibility-based robust design optimization framework is suggested for dealing with hybrid uncertain structures with fuzzy-boundary interval variables. The approach involves establishing a dual robust design and utilizing the fuzzy-boundary interval Taylor series-central difference method to efficiently manage FuBI uncertainties. The nested-loop PBRDO with FuBI variables can be simplified to a single-loop one based on FITS-CDM and TPA, demonstrating the effectiveness of the proposed optimization approach on dealing with FuBI uncertainties.
INTERNATIONAL JOURNAL OF MECHANICS AND MATERIALS IN DESIGN
(2021)
Article
Computer Science, Interdisciplinary Applications
Hui Lu, Haikuan Mao, Xiaoting Huang, Hui Yin, Wen-Bin Shangguan
Summary: This study proposes an effective method for addressing the uncertainties and imprecise information of uncertain parameters in automotive powertrain mounting systems, and introduces a reliability-based robust design optimization approach. By optimizing objectives and reliability constraints, utilizing evidence theory and uncertainty analysis methods, and considering both reliability and robustness of the system, a nested design model is established to seek the optimal design solution.
ENGINEERING WITH COMPUTERS
(2022)
Article
Computer Science, Interdisciplinary Applications
Hui Lu, Zebin Zheng, Xiaoting Huang, Hui Yin, Wen-Bin Shangguan
Summary: A comprehensive methodology for the design optimization of automotive powertrain mounting systems (PMSs) involving hybrid interval-random uncertainties is proposed in this study. The methodology includes the development of a hybrid interval-random perturbation central difference method (HIRP-CDM) for calculating uncertain responses, construction of reliability assessment models, and formulation of a design optimization model considering system inherent characteristics and reliability constraints. The proposed methodology is demonstrated to be effective through a numerical example.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Engineering, Mechanical
Duo Fu, Jin Huang, Hui Yin
Summary: This study investigates the tracking control problem of Uncertain Mobile Robots (UMR) under uncertain conditions and designs a control strategy using constraint-following and adaptive robust controls. The effectiveness of the proposed approach is demonstrated through rigorous proof and simulation results. The control is approximation-free and can tolerate large mismatched uncertainties.
NONLINEAR DYNAMICS
(2021)
Article
Automation & Control Systems
Duo Fu, Jin Huang, Wen-Bin Shangguan, Hui Yin
Summary: This article presents a novel constraint-following servo control approach for underactuated mobile robots, which incorporates hard constraints into soft constraints to drive the system to strictly follow the original soft and hard constraints. The effectiveness of the proposed approach is verified through rigorous proof and simulations.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Hui Yin, Jin Huang, Ye-Hwa Chen
Summary: This article introduces a new robust control design framework for uncertain mechanical systems, utilizing possibility theory in Lyapunov stability analysis and proposing possibility-based LSA. It presents a class of robust constraint-following controls and investigates optimal control parameter design considering both system performance and control cost.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Wu Qin, Feifei Liu, Hui Yin, Jin Huang
Summary: In this article, the control problem of active suspension systems with uncertainties is addressed. An adaptive robust control approach is proposed to ensure that the system meets both soft and hard constraints. The control design involves incorporating hard constraints into soft constraints and the model, decomposing uncertainties into matched and mismatched portions, and constructing a continuous adaptive law. The proposed control guarantees the boundedness of the system in the presence of uncertainties and hard constraints. Experimental and simulation results validate the effectiveness of the control approach.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Civil
Zeyu Yang, Jin Huang, Hui Yin, Diange Yang, Zhihua Zhong
Summary: This paper proposes a robust path tracking control method using a constraint-following approach to guide underactuated autonomous vehicles towards desired paths. By decomposing uncertainties and estimating matched uncertainty, a robust control method is designed to achieve excellent tracking performance even in the presence of time-varying uncertainties.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Mechanical
Zheshuo Zhang, Bangji Zhang, Hui Yin
Summary: This paper proposes an adaptive diffeomorphism-constraint-based control (ADCBC) method for trajectory tracking of a nonlinear AC. The method transforms inequality servo constraints into equality servo constraints using a diffeomorphism approach and compensates for uncertainty by estimating online uncertainty bounds. The efficacy and robustness of the method are confirmed through rigorous proofs and simulation results.
NONLINEAR DYNAMICS
(2023)
Article
Automation & Control Systems
Zheshuo Zhang, Bangji Zhang, Wen Hu, Rui Zhou, Dongpu Cao, Hui Yin
Summary: Manually planning a crane's lifting path is complicated and laborious. This study proposes a novel approach that includes problem formulation, a new method for determining the lifting path, and an improved A* algorithm for lift planning. The method considers energy consumption and visibility in C-space, and the experimental results demonstrate its effectiveness and efficiency.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Automation & Control Systems
Yifan Liu, Ye-Hwa Chen, Dejie Yu, Hui Yin
Summary: This study investigates the prescribed performance control (PPC) for uncertain underactuated systems. An approximation-free adaptive robust PPC based on a state transformation technique is designed, achieving approximate constraint-following and guaranteeing the prescribed transient and steady state performance. The effectiveness of the approach is illustrated through the PPC of a planar vertical take-off and landing aircraft.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2023)
Article
Automation & Control Systems
Zheshuo Zhang, Bangji Zhang, Dongpu Cao, Hui Yin
Summary: This study proposes an adaptive prescribed performance tracking control method for fuzzy AC, which aims to resist time-variant uncertainties and fulfill the task of precise tracking control robustly. The proposed method uses fuzzy set theory to describe uncertainties and applies state transformation to simultaneously track the desired trajectory and satisfy the prescribed performance. It does not involve IF-THEN fuzzy rules and avoids linearizations and nonlinear cancelation, making it approximation free. The controlled AC has both deterministic performance ensured by Lyapunov analysis and improved fuzzy-based performance achieved through an optimal design based on a two-player Nash game.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
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)
