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Hermite broad-learning recurrent neural control with adaptive learning rate for nonlinear systems

Soft Computing 2024年第7-8期28卷 6307-6326页

作者： Hsu, Chun-Fei Chen, Bo-Rui Department of Electrical Engineering Tamkang University New Taipei City25137 Taiwan Department of Electrical and Computer Engineering Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

Although conventional control systems are simple and widely used, they may not be effective for complex and uncertain systems. This study proposes a Hermite broad-learning recurrent neural network (HBRNN) with a wide network structure and an internal feedback loop that enables good approximation of system dynamics without the need for a large number of training parameters. Furthermore, a Hermite broad-learning recurrent neural control (HBRNC) with HBRNN as the main controller is proposed, which requires no prior knowledge about the system dynamics and has no off-line learning phase. All the HBRNN network parameters are updated according to parameter learning laws through the gradient descent approach. To prevent network parameter overtraining of the HBRNN, a discrete-type Lyapunov function is used to determine the least upper bound for the learning rates. Additionally, an adaptive learning rate (ALR) approach is designed to dynamically fine-tune the learning rates within these specified limits, thereby achieving an optimal convergence speed between network parameters and tracking error. Finally, the HBRNC system with ALR is applied to a chaotic circuit and a reaction wheel pendulum, and its effectiveness is validated through simulation and experimentation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Learning systems

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Switching event-triggered self-adjusting prescribed performance force control of electrohydraulic load simulator

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ISA Transactions 2025年 162卷 217-226页

作者： Li, Jiafeng Ji, Ruihang Yan, Hao Shan, Jiefeng Chen, Zanwei School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore

This paper addresses the problem of switching event-triggered self-adjusting prescribed performance force control of electro-hydraulic load simulator (EHLS). A reduced-order fuzzy speed state observer for speed estimation is designed and a self-adjusting performance boundary is devised to mitigate the commutation jitter problem. The switching event-triggered mechanism dynamically adjusts the triggering conditions to optimize resource utilization. To reduce the complexity of the traditional inverse stepping control, the dynamic surface technique is adopted. The results demonstrate that the proposed control method guarantees the boundedness of the closed-loop system states and confines the tracking error to a small range near the equilibrium point within a finite time horizon. Finally, the effectiveness of the proposed method is demonstrated through experimentation. © 2025 International Society of Automation

关键词： Closed loop control systems

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A Data-Driven Adaptive control Approach for Enhancing the Dynamic Response of VSGs in Varying Grid Conditions

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IEEE Transactions on Power Delivery 2025年第3期40卷 1421-1433页

作者： Fahad, Shah She, Buxin Yin, Junjie Li, Fangxing Cui, Hantao Bo, Rui Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Senior Power Systems Engineer ETAP Irvine CA USA Optimization & Control Group Parcific Northwest National Laboratory Richland USA Department of Electrical Engineering and Computer Science University of Tennessee at Knoxville Knoxville TN USA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Conventionally, a virtual synchronous generator (VSG) is designed for islanded mode (IM) operation to meet specific operational requirements such as the rate of change of frequency (RoCoF). However, the operation of VSG designed for IM may not meet the operational and control criteria in grid connected mode (GCM) when the grid conditions vary. In addition, conventional VSG control technology does not consider the influence of the presynchronization scheme when connected to a weak grid, which degrades the RoCoF in IM. To overcome the aforementioned challenges, the proposed study presents a twin-delayed deep deterministic policy gradient (TD3) algorithm to improve the seamless transition performance of VSG from IM to GCM and vice versa. In the first step, the VSG-based power system model is used as a foundation for problem formulation of the proposed TD3 algorithm. Secondly, a reward function is designed according to the performance requirements, i.e., frequency and RoCoF requirements, of the VSG in order to guide the training of the agent in varying load, power reference, and grid conditions. Finally, the superiority of the proposed algorithm over existing methods is validated in MATLAB/SIMULINK and RTDS based real-time simulation environment. © 1986-2012 IEEE.

关键词： Deep reinforcement learning

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Nanogenerator-based bidirectional pressure sensor array and its demonstration in underwater invasive species detection

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Nano Research 2023年第9期16卷 11822-11831页

作者： Yunqi Cao Hongyang Shi Xiaobo Tan Nelson Sepúlveda State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMichigan 48824USA

Lakes areas,which cause catastrophic damages in both commercial fishery and ecological ***,current assessment strategies may pose challenges for lake-wide abundance estimation and non-target anadromous species ***,we demonstrate an efficacious species-specific non-destructive sensing system based on porous ferroelectret nanogenerator for in-situ monitoring of lamprey spawning migration using their unique suction *** show that the porous structure enables a redistribution of surface charges under bidirectional deformations,which allows the detection of both positive and negative *** quasi-piezoelectric effect is further validated by quantitative analysis in a wide pressure range of−50 to 60 kPa,providing detailed insights into transduction working *** reliable lamprey detection,a 4×4-pixel sensor array is developed and integrated with a complementary metal-oxide-semiconductor(CMOS)based signal processing array thus constituting a sensing panel capable of recording oral suction patterns in an underwater environment.

关键词： bidirectional pressure sensing nanogenerators self-powered sensor array ferroelectret invasive species detection

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Anti-Windup for Internal Model control With Asymmetric Input Saturation

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IEEE control Systems Letters 2025年 9卷 126-131页

作者： Adegbege, Ambrose A. Owolabi, Opeyemi S. Ogunba, Kolawole S. Jubril, Abimbola M. The College of New Jersey Laboratory for Embedded Control and Optimization Department of Electrical and Computer Engineering EwingNJ08628 United States Obafemi Awolowo University Department of Electronic and Electrical Engineering Ife220282 Nigeria

In this letter, we introduce a novel anti-windup design approach for internal model control (IMC) that addresses the issue of asymmetric input saturation. To enhance closed-loop performance during periods of saturation, we utilize information about the asymmetrical limits of input saturation in the anti-windup design. This is achieved through a combination of static matrix scaling, a constant offset term, and a generalized sector condition that guarantees both global and regional stability of the closed loop. Our approach generalizes existing techniques for incorporating directionality compensation into anti-windup designs and can be interpreted as a search over a class of left-coprime factorization of the linear internal model controller. We demonstrate the effectiveness of this design approach on a well-known and widely used anti-windup example. © 2017 IEEE.

关键词： Matrix factorization

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An artificial systems,computational experiments and parallel execution-based surface electromyogram-driven anti-disturbance zeroing neurodynamic strategy for upper limb human-robot interaction control

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 511-525页

作者： Yongbai Liu Keping Liu Gang Wang Jiliang Zhang Yao Chou Zhongbo Sun Department of Mechanical Engineering Changchun University of TechnologyChangchunChina Department of Control Engineering Changchun University of TechnologyChangchunChina School of Electrical and Information Engineering Jilin Engineering Normal UniversityChangchunChina College of Information Science and Engineering Northeastern UniversityShenyangChina Department of Electrical and Computer Engineering Brigham Young UniversityProvoUtahUSA

In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ***,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be *** deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI *** upper limb-robotic exoskeleton system is re-modelled as an artificial *** on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical ***,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and *** results substantiate the global convergence and robustness of the proposed controller in the presence of different external *** addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.

关键词： neural network pattern recognition robotics signal processing

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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

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Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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Social NSTransformers: Low-Quality Pedestrian Trajectory Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5575-5588页

作者： Jiang, Zihan Ma, Yiqun Shi, Bingyu Lu, Xin Xing, Jian Goncalves, Nuno Jin, Bo Suzhou215000 China Northeast Forestry University College of Computer and Control Engineering Harbin150000 China University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra3030-290 Portugal

This article introduces a novel model for low-quality pedestrian trajectory prediction, the social nonstationary transformers (NSTransformers), that merges the strengths of NSTransformers and spatiotemporal graph transformer (STAR). The model can capture social interaction cues among pedestrians and integrate features across spatial and temporal dimensions to enhance the precision and resilience of trajectory predictions. We also propose an enhanced loss function that combines diversity loss with logarithmic root mean squared error (log-RMSE) to guarantee the reasonableness and diversity of the generated trajectories. This design adapts well to complex pedestrian interaction scenarios, thereby improving the reliability and accuracy of trajectory prediction. Furthermore, we integrate a generative adversarial network (GAN) to model the randomness inherent in pedestrian trajectories. Compared to the conventional standard Gaussian distribution, our GAN approach better simulates the intricate distribution found in pedestrian trajectories, enhancing the trajectory prediction's diversity and robustness. Experimental results reveal that our model outperforms several state-of-the-art methods. This research opens the avenue for future exploration in low-quality pedestrian trajectory prediction. © 2024 IEEE.

关键词： Stars

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Microscopy-Guided 3-D Reconstruction of Nanodendrites in Biosensors

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IEEE Sensors Journal 2023年第9期23卷 9025-9032页

作者： Fan, Sue-Yuan Huang, Yi-Pin Khuntia, Sucharita Chang, Jen-Wen Liou, Ci-Ruei Zhang, Bing Tai, Li-Chia Institute of Electrical and Control Engineering Department of Electrical and Computer Engineering Hsinchu30010 Taiwan Lam Research FremontCA94538 United States

Nanodendritic structures have gained increasing popularity in electrochemical sensors. However, it is still rare to generate a 3-D model in a short period of time to understand the structure-function relationship of the sensors. Here, we report the construction of a 3-D model for nanodendritic, metallic structures frequently grown on top of bioelectronics. This is achieved by merging two sources of 2-D dendritic information, which includes top-view images from scanning electron microscopy (SEM) and side-view visualization from Monte Carlo simulations. The microscopy images provide the boundary conditions to tune the Monte Carlo simulations to construct the 3-D dendritic morphology. We validated the 3-D model by comparing the dendritic area densities predicted via this model with those computed from microscopy images. In addition, tuning the simulation parameters in the 3-D model can be used to find the optimized dendritic density, which is an essential indicator for sensitivity enhancement. The success of this model provides a means to understand the sensitivity limits of bioelectronics through dendritic growth without the need for time-consuming sensor fabrication and testing. Furthermore, our SEM-guided Monte Carlo technique provides a dendritic model with a significant resemblance to experimental images. It possesses the potential for applications in 3-D morphological investigations for future biosensor design. © 2001-2012 IEEE.

关键词： Scanning electron microscopy

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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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