Article
Automation & Control Systems
Guanyu Lai, Kai Huang, Liang Li, Zhi Liu, Yun Zhang, C. L. Philip Chen
Summary: When the Preisach operator is coupled with uncertain unparametrizable nonlinear dynamics, the tracking control problem becomes challenging. This study proposes a fixed-time adaptive fuzzy control scheme to solve this problem. The scheme achieves bounded convergence time and ensures all signals in the closed-loop system are bounded.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Erik Tamsen, Daniel Balzani
Summary: The paper presents a fully-coupled, two-scale homogenization method for dynamic loading, capable of capturing micro inertia and dynamic effects, suitable for large deformation problems. The method can be implemented in standard finite element program architectures and has been demonstrated for applications in composite materials and engineering problems.
COMPUTATIONAL MECHANICS
(2021)
Article
Automation & Control Systems
Xin Li, Wenlin Zhou, Dan Jia, Junzhang Qian, Jun Luo, Ping Jiang, Wenli Ma
Summary: This article proposes a feedforward friction compensated linear active disturbance rejection control (FFLADRC) algorithm to solve the problem of a two motors system in a large optical telescope. By decoupling and compensating the friction, the algorithm achieves control over the motors and demonstrates effective results.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Mathematics
Zemin Yang, Xiaopeng Li, Jinchi Xu, Renzhen Chen, Hexu Yang
Summary: The study proposes a dynamic model for a previously proposed nonlinear variable stiffness actuator based on a two-inertia-system theory. It analyzes the effects of friction and gravity on the system's dynamic performance and concludes that considering these factors in the dynamics modeling process is reasonable and necessary. The study also presents a sliding mode control strategy based on a nonlinear disturbance observer and dynamics model, which effectively addresses the impact of friction and gravity on the system and ensures the position-tracking performance meets the requirements. Experimental verification demonstrates the correctness and effectiveness of the control strategy.
Article
Automation & Control Systems
Dongyang Shang, Xiaopeng Li, Meng Yin, Fanjie Li
Summary: In this study, a neural network compensation sliding mode control strategy mixed with the angle-independent method is proposed to suppress the vibration of the two-inertia system with variable-length flexible load. By designing the fluctuating desired input of the motor and using speed fluctuation to offset the load vibration, the proposed strategy is able to effectively decrease the error and weaken the vibration.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Engineering, Mechanical
Zemin Yang, Xiaopeng Li, Renzhen Chen, Dongyang Shang, Jinchi Xu, Hexu Yang
Summary: The study proposed a variable stiffness actuator for the human knee joint and experimentally verified its feasibility. It analyzed the effects of friction and gap characteristics on the dynamic performance of this actuator, finding significant impacts. Both friction and gap characteristics can cause dead zones and vibrations in the actuator, requiring further research in control to meet vibration suppression and high accuracy requirements.
MECHANISM AND MACHINE THEORY
(2022)
Article
Engineering, Mechanical
Lewei Tang, Marc Gouttefarde, Haining Sun, Lairong Yin, Changjiang Zhou
Summary: This paper introduces the dynamic modeling of a single-link flexible manipulator with two cables and proposes a calculation method to determine its natural frequency. Simulation experiments are performed to validate the method and three non-dimensional parameters are introduced to investigate the effects on the natural frequency of the flexible system. An experimental verification is implemented to show the potential of utilizing cables in lightweight flexible systems for various applications.
MECHANISM AND MACHINE THEORY
(2021)
Article
Acoustics
Erik R. Gomez, Leif Kari, Ines Lopez Arteaga
Summary: A flywheel-mounted centrifugal pendulum absorber is designed to suppress the low-frequency shuffle-mode resonance of a heavy-duty truck powertrain. Linear analysis and nonlinear simulations are used to validate the design, and the CPVA is found to effectively reduce vibrations and environmental impact.
JOURNAL OF SOUND AND VIBRATION
(2022)
Article
Mathematics
Chuanzhi Sun, Ruirui Li, Ze Chen, Yingjie Mei, Xiaoming Wang, Chengtian Li, Yongmeng Liu
Summary: The optimal assembly phase of multi-stage rotors obtained by the dynamic analysis model of unbalanced vibration response in a single-rotor system effectively suppresses the vibration of the combined rotor.
Article
Automation & Control Systems
Shiqi Gao, Yuanyuan Zhang, Jinkun Liu
Summary: This study proposes an innovative control scheme for addressing the vibration control and boundary output constraint problems of a flexible wing system. By designing a boundary controller and disturbance observer, it achieves vibration suppression and eliminates the adverse effect of external disturbances. The effectiveness of the proposed control scheme is demonstrated through numerical simulations.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Engineering, Mechanical
Xiling Xie, Peitao He, Di Wu, Zhiyi Zhang
Summary: This study proposes an active control scenario to suppress the longitudinal vibration of the thrust bearing and its pedestal using electromagnetic constraint and inertial electromagnetic actuators. A dynamic model with electromagnetic control forces is established, and a longitudinal position control method is designed. Simulations and experiments demonstrate the effectiveness of the proposed scenario in longitudinal position adjustment and vibration attenuation.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Acoustics
Chaowu Jin, Yue Dong, Xudong Guan, Jin Zhou, Fan Wang
Summary: This article introduces the design and experimental research of a magnetic suspension dynamic vibration absorber. By establishing a theoretical model, designing a specific structure, conducting simulation studies, and experimental verification, it is found that the magnetic suspension dynamic vibration absorber can effectively reduce vibration within a certain frequency range.
JOURNAL OF VIBRATION AND CONTROL
(2021)
Article
Computer Science, Information Systems
Hui Huang, Guoyuan Tang, Hongxuan Chen, Lijun Han, De Xie
Summary: This paper proposes a composite controller to improve the accuracy of trajectory tracking and suppress the vibration of underwater flexible manipulators. The dynamic model of the manipulators considering hydrodynamic force is established, and a decomposed dynamic control strategy is presented. An adaptive fuzzy sliding mode control scheme is designed to track the trajectory and suppress vibration. The proposed composite controller is more effective in restraining vibration and resisting hydrodynamic force disturbance.
Article
Optics
Ronggang Zhu, Jianjie Zhou, Bo Li, Ya Huang
Summary: An iterative algorithm is proposed in this paper to effectively eliminate the periodic ripple error in measurement results of dynamic interferometers, improving measurement accuracy without the need for additional manual operation.
Article
Engineering, Mechanical
Heitor Nigro Lopes, Daniel Candeloro Cunha, Renato Pavanello, Jarir Mahfoud
Summary: The study utilized the BESO algorithm for topology optimization to maximize the natural frequency separation interval of a structure, solving issues with disconnected and trivial solutions through connectivity constraints. Feasibility of the structure was assessed by verifying compliance with manufacturing and design constraints.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Automation & Control Systems
Jing Na, Yongfeng Lv, Kaiqiang Zhang, Jun Zhao
Summary: This article proposes an ADP method for optimal tracking control of nonlinear systems using a neural network identifier and critic. The combination of static control and online training of the critic NN improves control response effectively.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Jing Na, Haoran He, Yingbo Huang, Ruili Dong
Summary: This paper presents a novel one-step adaptive parameter estimation framework for identifying unknown asymmetric dead-zone characteristic parameters in sandwich systems. It utilizes a continuous piecewise linear neural network to represent the dead-zone nonlinearities and designs an adaptive observer to reconstruct internal variables, achieving efficient parameter estimation.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Yiming Jiang, Yaonan Wang, Zhiqiang Miao, Jing Na, Zhijia Zhao, Chenguang Yang
Summary: This article presents an adaptive control method for dual-arm robot systems to perform bimanual tasks under modeling uncertainties. The control method incorporates trajectory tracking and contact force control by considering the relative motions between robotic arms and a grasped object. The proposed control also utilizes a radial basis function neural network (RBFNN) and a composite learning law to update the network weights and improve convergence. The stability analysis confirms the validity of the control and learning algorithm.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Guanbin Gao, Yangtao Gao, Fei Liu, Jing Na
Summary: A novel modeling and calibration method for collaborative robots based on position information and modified local product of exponentials (LPoE) is proposed, which improves the speed and accuracy of the identification calculation by simplifying the error model and separating the position error.
JOURNAL OF SENSORS
(2022)
Article
Engineering, Electrical & Electronic
Bin Wang, Ramon Costa-Castello, Jing Na, Oscar de la Torre, Xavier Escaler
Summary: This paper proposes a new adaptive estimation approach for online parameter estimation of a piezoelectric cantilever beam. By introducing the Galerkin method and separating the time and space variables of the PDE, the unknown parameters of the derived ODE model can be estimated in real time.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Article
Engineering, Electrical & Electronic
Yingbo Huang, Huidong Hou, Jing Na, Haoran He, Jing Zhao, Zhenghao Shi
Summary: This paper presents a novel control method for half-vehicle active suspension systems driven by hydraulic actuators. It introduces a coordinate transform approach to reformulate the strict-feedback system into a canonical form without using the backstepping method. A modified high-gain observer (HGO) is studied to rebuild the unknown system states of the nonlinear active suspension system. To eliminate the effect of unknown nonlinearities, a simple robust unknown system dynamics estimator (USDE) is developed. Finally, the observer and estimator are integrated to design an output feedback controller to regulate the vehicle motion. Comparative experiments demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Automation & Control Systems
Xin Chen, Yu Guo, Jing Na
Summary: Synchronous averaging (SA) is a powerful signal processing tool that enhances the features of periodic events by suppressing nonsynchronous components. However, under random slip conditions, SA may not effectively enhance the features related to rolling element bearing (REB) faults. This article proposes two frameworks based on instantaneous angular speed (IAS) for synchronous averaging and introduces an improved negentropy indicator to characterize the richness of REB fault information. The effects of encoder resolution and structure damping factor on the waveform related to faulty REB are also studied. Simulation and experiment results demonstrate the effectiveness of the proposed schemes in enhancing the features of REB faults under random slip conditions.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Haoran He, Jing Na, Yingbo Huang, Tao Liu
Summary: In this article, a novel adaptive parameter estimation scheme is proposed for the continuous-time Hammerstein model. A continuous piecewise linear neural network is adopted to reformulate the dead-zone dynamics, and the K-filter operation is applied to obtain an integrated parametric model. Two adaptive laws based on estimation error are given to estimate the unknown parameters, and an observer is designed to reconstruct the unknown system states. Theoretical analysis and experiments verify the effectiveness of the proposed methods.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Jing Zhao, Jincan Liu, Pak Kin Wong, Zhongchao Liang, Zhengchao Xie, Jing Na
Summary: This article proposes a generalized fuzzy subset (GFS) method to assess the time-varying multistate reliability. The method integrates all possible perturbations as inputs and constructs a GFS reliability model based on the composite limit state. The concept of uncertain subset boundary is introduced to conduct the reliability assessment using embedded interval type-2 fuzzy sets. A data-driven strategy is designed to address the deficiency of the GFS reliability model.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Dong-Dong Zheng, Xianyan Li, Xuemei Ren, Jing Na
Summary: The purpose of this study is to improve the transient performance and address the potential boundary-crossing issue in the design of a neural network-based intelligent prescribed performance control for robotic manipulators with input saturation. An auxiliary system is created to modify the performance boundaries when saturation occurs, ensuring that the tracking errors meet the performance constraints even when control effort is limited. A composite learning-based online identification scheme is employed to enhance the transient performance, and a Gaussian function is used to adaptively adjust the learning rate during weight updating. The stability of the closed-loop system is demonstrated through the Lyapunov approach, and simulation results support the effectiveness of the proposed identification and control schemes.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Xingling Shao, Fei Zhang, Wendong Zhang, Jing Na
Summary: This article investigates a finite-time composite learning-based elliptical enclosing control for nonholonomic robots under a GPS-denied environment. A novel bearing measurement-based relative position observer is proposed to assure estimation errors decay without GPS. An elliptical guidance law is established to yield the reference velocity and angular rate using observation outcomes. A finite-time composite neural learning driven by weight and tracking errors is devised to achieve precise disturbance compensation and error convergence.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Xiaomei Wang, Jing Na, Ben Niu, Xudong Zhao, Tingting Cheng, Wenqi Zhou
Summary: This paper proposes an adaptive bipartite secure consensus asymptotic tracking control scheme based on event-triggered strategy for the nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks. The paper successfully addresses the bipartite consensus control problem with unbalanced communication topology by incorporating the concept of shortest path into the hierarchical algorithm. An anti-attack bipartite control strategy is proposed using improved forms of tracking errors and virtual controllers, and a modified event-triggered mechanism based on relative threshold strategy ensures asymptotic convergence of bipartite consensus tracking errors.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Jintao Zhang, Xingling Shao, Wendong Zhang, Jing Na
Summary: This article proposes a path-following control method that enhances transient performances for networked mobile robots traveling over a single curve. By using a coordinated error based on projective arc length, a path-following controller is designed for multiple robots, achieving a queue formation pattern with equal arc spacing at a uniform velocity. Additionally, a tracking differentiator-based prescribed performance control scheme is proposed to enforce tracking deviations of geometric and dynamic objectives before a specified time. The developed scheme allows for cooperative behavior over a general curve and arbitrary designation of desired settling time for each robot, while ensuring convergence of all error variables.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Chao Zhang, Xuemei Ren, Jing Na, Dongdong Zheng
Summary: This article proposes a safe dual-layer nested adaptive prescribed performance control approach for nonlinear systems, which ensures predefined transient and steady-state performances for the discontinuous reference signal. A monitoring mechanism and a novel dual-layer nested adaptive sliding mode compensation technique are introduced to handle system uncertainties effectively.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Zhijiang Gao, Pak Kin Wong, Jing Zhao, Zhixin Yang, Yingbo Huang, Jing Na
Summary: This article addresses the optimal control problem for magnetorheological fluid-based semiactive suspension systems with input saturation and time-varying delay. A robust switched H∞ method based on the Takagi-Sugeno fuzzy theory is proposed to handle this problem. A novel hybrid model incorporating the fluid flow mechanism and hysteresis phenomenon model is used to separate the passive and active components of the MRF damper. Linear matrix inequality conditions are derived to capture the features of input saturation and time-varying delay, and a Lyapunov-Krasovskii function is employed to ensure stability. Numerical examples demonstrate the effectiveness of the proposed method in dealing with the MRF-SAS system with input saturation and time-varying delay.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.
