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Data-driven output regulation control for constrained linear systems

Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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

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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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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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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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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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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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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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Distortion Evaluation of EMP Sensors Using Associated-Hermite Functions

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computers, Materials & Continua 2023年第1期74卷 1093-1105页

作者： Rupo Ma Siping Gao College of Command and Control Engineering Army Engineering UniversityNanjing210007China Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Department of Electrical and Computer Engineering National University of Singapore119260Singapore

Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and *** is challenging for an EMP sensor to measure a wideband electromagnetic pulse without distortion for the whole ***,analyzing the distortion of EMP measurement is crucial to evaluating the sensor distortion characteristics and correcting the measurement *** fidelity is usually employed to evaluate the distortion of an ***,this metric depends on specific signal waveforms,thus is unsuitable for evaluating and analyzing the distortion of EMP *** this paper,an associated-hermite-function based distortion analysis method including system transfer matrices and distortion rates is proposed,which is general and independent from individual *** system transfer matrix and distortion rate can be straightforwardly calculated by the signal orthogonal transformation coefficients using associated-hermite *** of a sensor *** is then visualized via the system transfer matrix,which is convenient in quantitative analysis of the *** of a current probe,a coaxial pulse voltage probe and a B-field sensor were performed,based on which the feasibility and effectiveness of the proposed distortion analysis method is successfully verified.

关键词： Electromagnetic pulse associated-hermite functions system transfer matrix distortion rate

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