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Stability and Stabilization of Sampled-Data Based LFC for Power Systems:A Data-Driven Method

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 291-293页

作者： Yu-Long Fan Chuan-Ke Zhang Yong He the School of Automation China University of Geosciences the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education IEEE

Dear Editor,This letter is concerned with stability analysis and stabilization design for sampled-data based load frequency control(LFC) systems via a data-driven method. By describing the dynamic behavior of LFC systems based on a data-based representation, a stability criterion is derived to obtain the admissible maximum sampling interval(MSI) for a given controller and a design condition of the PI-type controller is further developed to meet the required MSI. Finally, the effectiveness of the proposed methods is verified by a case study.

关键词： Sampled data control systems

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Parametric control of quasi-linear second-order systems with partitioned eigenstructure assignment by output feedback

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Science China(Information Sciences) 2023年第4期66卷 228-243页

作者： Weizhen LIU Guangren DUAN Dake GU Center for Control Theory and Guidance Technology Harbin Institute of Technology Center for Control Science and Technology Southern University of Science and Technology School of Automation Engineering Northeast Electric Power University

In this paper, a parametric design approach for stabilizing a quasi-linear second-order system with partitioned eigenstructure assignment(PESA) is investigated through output feedback control. The PESA approach is established by partitioning the desired eigenvalue matrix into two parts to separate the associated right and left eigenvectors into a subset of the generalized eigenvectors simultaneously. A parametric controller is established by solving two second-order generalized Sylvester matrix equations, and a certain form with the desired eigenstructure can be derived with the established quasi-linear output feedback controller. Unlike the prevailing approach that assigns the entire set of generalized eigenvectors, which is difficult to satisfy a large number of complicated constraints in practical systems by the normalized pair of right and left eigenvector matrices, a subset of the generalized eigenvectors is considered. In addition, the proposed PESA approach provides less computational load and is easy to use. A numerical example and application in spacecraft rendezvous are provided to verify the numerical economy and high efficiency of the proposed approach.

关键词： quasi-linear second-order systems partitioned eigenstructure assignment parametric approach Sylvester matrix equation output feedback

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Static calibration and dynamic compensation of the SCORBOT robot using sensor fusion and LSTM networks

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Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A 2023年第8期46卷 881-894页

作者： Kuo, Yong-Lin Hsieh, Chia-Hang Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei Taiwan Center of Automation and Control National Taiwan University of Science and Technology Taipei Taiwan

This paper presents both static calibration and dynamics compensation to reduce the positioning errors of the SCORBOT robot. First, a sensor fusion scheme is proposed to estimate the position and attitude of the end-effector of a robot instead of using laser trackers or coordinate measuring machines. The scheme integrates an extended Kalman filter (EKF) with the models of an inertial measurement unit (IMU) and a depth camera. Second, a static calibration scheme is presented to reduce the mechanism errors of robots. The scheme modifies the Denavit-Hartenberg (D-H) parameters provided by the manufacturer based on the least squares method. Third, a dynamic compensation scheme is proposed to reduce the errors caused by robot motions. The scheme establishes a long short-term memory (LSTM) network to compensate the joint angles, where the robot dynamics is integrated into the scheme. Finally, both simulations and experiments are performed to validate the proposed schemes. © 2023 The Chinese Institute of Engineers.

关键词： Long short-term memory

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Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive control

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 723-733页

作者： Xiongbo Wan Chaoling Zhang Fan Wei Chuan-Ke Zhang Min Wu IEEE the School of Automation China University of Geosciencesthe Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systemsand the Engineering Research Center of Intelligent Technology for Geo-ExplorationMinistry of EducationWuhan 430074China

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic *** addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI *** the designed controller,the involved fuzzy system is ensured to be asymptotically *** examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

关键词： Dynamic event-triggered mechanism(DETM) hybrid dynamic variables model predictive control(MPC) robust positive invariant(RPI)set T-S fuzzy systems

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Fixed-time stabilization of discontinuous spatiotemporal neural networks with time-varying coefficients via aperiodically switching control

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Science China(Information Sciences) 2023年第5期66卷 182-195页

作者： Xiaofang HU Leimin WANG Chuan-Ke ZHANG Xiongbo WAN Yong HE School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper focuses on the challenge of fixed-time control for spatiotemporal neural networks(SNNs) with discontinuous activations and time-varying coefficients. A novel fixed-time convergence lemma is proposed, which facilitates the handling of time-varying coefficients of SNNs and relaxes the restriction on the non-positive definiteness of the derivative of the Lyapunov function. Besides, a more flexible and economical aperiodically switching control technique is presented to stabilize SNNs within a fixed time,efectively reducing the amount of information transmission and control costs. Under the newly established fixed-time convergence lemma and aperiodically switching controller, many more general algebraic conditions are deduced to ensure the fixed-time stabilization of SNNs. Numerical examples are provided to manifest the validity of the results.

关键词： spatiotemporal neural networks discontinuous activations time-varying coefficients fixed-time stabilization aperiodically switching control

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Deep reinforcement learning based integrated evasion and impact hierarchical intelligent policy of exo-atmospheric vehicles

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Chinese Journal of Aeronautics 2025年第1期38卷 409-426页

作者： Leliang REN Weilin GUO Yong XIAN Zhenyu LIU Daqiao ZHANG Shaopeng LI Xi’an Research Institute of High Technology Xi’an 710025China China Xi’an Satellite Control Center Xi’an710043China Department of Automation Tsinghua UniversityBeijing 100084China

Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision *** address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was ***,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision ***,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed *** results show that the proposed model has good performance and low computational resource *** minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.

关键词： Exo-atmospheric vehicle Integrated evasion and impact Deep reinforcement learning Hierarchical intelligent policy Single-chip microcomputer Miss distance

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Fault Diagnosis for Railway Point Machines Using VMD Multi-Scale Permutation Entropy and ReliefF Based on Vibration Signals

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Chinese Journal of Electronics 2025年第1期34卷 204-211页

作者： Yongkui Sun Yuan Cao Peng Li Shuai Su National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Automation and Intelligence Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

The railway point machine plays an important part in railway systems. It is closely related to the safe operation of trains. Considering the advantages of vibration signals on anti-interference, this paper develops a novel vibration signal-based diagnosis approach for railway point machines. First, variational mode decomposition(VMD)is adopted for data preprocessing, which is verified more effective than empirical mode decomposition. Next, multiscale permutation entropy is extracted to characterize the fault features from multiple scales. Then ReliefF is utilized for feature selection, which can greatly decrease the feature dimension and improve the diagnosis accuracy. By experiment comparisons, the proposed approach performs best on diagnosis for railway point machines. The diagnosis accuracies on reverse-normal and normal-reverse processes are respectively 100% and 98.29%.

关键词： Vibrations Fault diagnosis Accuracy Data preprocessing Switches Feature extraction Rail transportation Entropy Maintenance Monitoring

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Based on The Multi-index Quantitative Evaluation Model of Health Status of Distributed Distribution Network

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Distributed Generation and Alternative Energy Journal 2024年第5期39卷 961-988页

作者： Zhang, Yao Zhou, Zhongqiang Lian, Yin Zhou, Kun Shi, Qihong Electric Power Dispatching and Control Center Department of Distribution Control Guizhou Power Grid Guiyang550000 China Electric Power Dispatching and Control Center Automation Department Guizhou Power Grid Guiyang550000 China Xingyi Power Supply Bureau Electric Power Dispatching and Control Center Xingyi562400 China Bijie Power Supply Bureau Electric Power Dispatching and Control Center Bijie551700 China Anshun Power Supply Bureau Electric Power Dispatching and Control Center Anshun561000 China

With the rapid development of smart grids, accurate and timely assessment of the health status of distribution networks is crucial. This article proposes a multi-indicator quantitative evaluation model based on the health status of distributed distribution networks. The model incorporates diverse pivotal metrics about the distribution network, encompassing voltage stability, equipment senescence, and fault occurrence frequencies. Leveraging sophisticated data analytics techniques facilitates a quantitative appraisal of the network’s overall health status. To delve deeper into the efficacy of intelligent distribution network situational awareness, this study presents a comprehensive evaluation framework encompassing a robust index system and evaluation methodologies. The comprehensive evaluation index system includes five primary indicators, as well as a total of 17 secondary indicators, to objectively quantify the effectiveness of intelligent distribution network situational awareness. This paper uses a subjective and objective mixed evaluation method based on the binomial coefficient and multi-objective planning methods to weight the comprehensive evaluation index system. The comprehensive evaluation model for the implementation effect of intelligent distribution network situational awareness proposed in this article objectively and accurately reflects the operation status of the distribution network, eliminates the one-sidedness of a single evaluation method, effectively improves the observability of the intelligent distribution network, and realizes the analysis of weak links and potential risks in the distribution network. © 2024 River Publishers.

关键词： Power distribution networks

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Torque Ripple and Acoustic Noise Suppression for Sensorless PMSM Drive Based on Biaxial Random Signal Injection

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7287-7299页

作者： Qian, Zhe Li, Xin Wang, Qunjing Deng, Wenzhe Sun, Zehui Chen, Qixu Anhui University School of Electrical Engineering and Automation National Engineering Laboratory of Energy-Saving Motor and Control Technology Hefei230601 China Anhui University School of Electrical Engineering and Automation Hefei230601 China Anhui University School of Electrical Engineering and Automation National Engineering Laboratory of Energy-Saving Motor and Control Technology Anhui Collaborative Innovation Center of Industrial Energy-Saving and Power Quality Control Hefei230601 China

The signal injection method shows good performance in estimating the rotor position of permanent magnet synchronous motor (PMSM) at low speed, while the traditional uniaxial fixed-phase signal injection (UFPSI) method will produce extra torque ripple and noise. This article proposes a novel sensorless PMSM drive method based on biaxial random phase signal injection (BRPSI), which can simultaneously suppress both torque ripple and noise. To achieve this, an additional injected q-axis voltage, with its amplitude calculated in real time to satisfy different operating conditions, is used to counteract the torque ripple induced by the injected d-axis voltage. Meanwhile, the phase of the injected voltages is randomly switched to achieve a smooth current power spectral density (PSD) and attenuated noise level. The effectiveness of the proposed method is verified on a rapid control prototyping (RCP) platform, which demonstrates that the proposed method exhibits good speed and position tracking performance while simultaneously suppressing the torque ripples and noise caused by the injected signals. © 2015 IEEE.

关键词： Permanent magnets

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TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations

TimberVision: A Multi-Task Dataset and Framework for Log-Com...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Steininger, Daniel Simon, Julia Trondl, Andreas Murschitz, Markus Ait Austrian Institute of Technology Center for Vision Automation & Control Austria

ISBN: (纸本)9798331510831

Timber represents an increasingly valuable and versatile resource. However, forestry operations such as harvesting, handling and measuring logs still require substantial human labor in remote environments posing significant safety risks. Progressively automating these tasks has the potential of increasing their efficiency as well as safety, but requires an accurate detection of individual logs as well as live trees and their context. Although initial approaches have been proposed for this challenging application domain, specialized data and algorithms are still too scarce to develop robust solutions. To mitigate this gap, we introduce the Timbervision dataset, consisting of more than 2k annotated RGB images containing a total of 51k trunk components including cut and lateral surfaces, thereby surpassing any existing dataset in this domain in terms of both quantity and detail by a large margin. Based on this data, we conduct a series of ablation experiments for oriented object detection and instance segmentation and evaluate the influence of multiple scene parameters on model performance. We introduce a generic framework to fuse the components detected by our models for both tasks into unified trunk representations. Furthermore, we automatically derive geometric properties and apply multi-object tracking to further enhance robustness. Our detection and tracking approach provides highly descriptive and accurate trunk representations solely from RGB image data, even under challenging environmental conditions. Our solution is suitable for a wide range of application scenarios and can be readily combined with other sensor modalities. © 2025 IEEE.

关键词： Logging (forestry)

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