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Eigenvalue-Oriented Data-Driven Small-Signal Stability Assessment for DC Microgrids

IEEE Transactions on Power Systems 2024年第4期40卷 3563-3575页

作者： Deng, Ruilong Jiang, Qiliang Zhou, Xu Wang, Yuhong Sun, Mingyang Zhejiang University State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China Sichuan University College of Electrical Engineering Chengdu610065 China Peking University Department of Industrial Engineering and Management College of Engineering Beijing100091 China

The stability issues of DC microgrids (DCmGs) are becoming increasingly important due to the widespread deployment of renewable distributed energy resources (DERs), which has led to a growing demand for DCmGs. Existing smallsignal white-box and gray-box models of DCmGs require system parameters and are severely limited in practical applicable scenarios, while black-box models cannot effectively assess DCmGs' stability. To this end, this paper proposes a novel eigenvalueoriented small-signal stability assessment approach for DCmGs, which achieves small-signal modeling and stability assessment of DCmGs through measurements of converters' interface. The contributions of this paper are as follows: 1) The Fed-Koopman network is proposed, mapping a multi-scenario DCmG to highdimensional linear space via a federated learning algorithm, and establishing the black-box small-signal model. 2) An improved elastic net regression (ENR) algorithm, named NeuENR is proposed, identifying the system matrix Asys. 3) Considering measurement errors during Asys identification, the small-signal stability criterion and stability margin are defined. The approach is implemented on the commercial electromagnetic transient simulation platform, CloudPSS, demonstrating its applicability to different operation conditions of DCmGs and superior assessment performance. © 1969-2012 IEEE.

关键词： Microgrids

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Hierarchically Distributed Optimization with a Flexible and Complexity-Reducing Algorithm

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Journal of Systems science & Complexity 2024年第6期37卷 2530-2555页

作者： LIANG Shu ZHANG Lei WEI Yiheng LIU Yemo Department of Control Science and Engineering Tongji UniversityShanghai 200092China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 200092China School of Mathematics Southeast UniversityNanjing 211189China Beijing Aerospace Automatic Control Institute Beijing 100854China

In this paper,the authors consider distributed convex optimization over hierarchical *** authors exploit the hierarchical architecture to design specialized distributed algorithms so that the complexity can be reduced compared with that of non-hierarchically distributed *** this end,the authors use local agents to process local functions in the same manner as other distributed algorithms that take advantage of multiple agents'computing ***,the authors use pseudocenters to directly integrate lower-level agents'computation results in each iteration step and then share the outcomes through the higher-level network formed by *** authors prove that the complexity of the proposed algorithm exponentially decreases with respect to the total number of *** support the proposed decomposition-composition method for agents and pseudocenters,the authors develop a class of *** operators are generalizations of the widely-used subgradient based operator and the proximal operator and can be used in distributed convex ***,these operators are closed with respect to the addition and composition operations;thus,they are suitable to guide hierarchically distributed design and ***,these operators make the algorithm flexible since agents with different local functions can adopt suitable operators to simplify their ***,numerical examples also illustrate the effectiveness of the method.

关键词： Complexity convergence convex optimization distributed algorithm hierarchical network

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Time delay recursive neural network-based direct adaptive control for a piezo-actuated stage

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science China(Technological sciences) 2023年第5期66卷 1397-1407页

作者： WANG YiFan ZHOU MiaoLei SHEN ChuanLiang CAO WenJing HUANG XiaoLiang Department of Control Science and Engineering Jilin UniversityChangchun130022China State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun130022China Department of Engineering and Applied Sciences Sophia UniversityTokyo102-8554Japan Department of Electrical Engineering Chalmers University of TechnologyGothenburg41296Sweden

Piezo-actuated stage is a core component in micro-nano manufacturing ***,the inherent nonlinearity,such as rate-dependent hysteresis,in the piezo-actuated stage severely impacts its tracking *** study proposes a direct adaptive control(DAC)method to realize high precision *** proposed controller is designed by a time delay recursive neural *** with those existing DAC methods designed under the general Lipschitz condition,the proposed control method can be easily generalized to the actual systems,which have hysteresis ***,a hopfield neural network(HNN)estimator is proposed to adjust the parameters of the proposed controller ***,a modular model consisting of linear submodel,hysteresis submodel,and lumped uncertainties is established based on the HNN estimator to describe the piezoactuated stage in this ***,the performance of the HNN estimator can be exhibited visually through the modeling *** proposed control method eradicates the adverse effects on the control performance arising from the inaccuracy in establishing the offline model and improves the capability to suppress the influence of hysteresis on the tracking accuracy of piezo-actuated stage in comparison with the conventional DAC *** stability of the control system is ***,a series of comparison experiments with a dual neural networks-based data driven adaptive controller are carried out to demonstrate the superiority of the proposed controller.

关键词： piezo-actuated stage direct adaptive control time delay recursive neural network hopfield neural network estimator

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Synchronous control of H-type Linear Motor System via Super-Twisting Sliding Mode

Synchronous Control of H-type Linear Motor System via Super-...

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2024 China Automation Congress, CAC 2024

作者： Liu, Jiawei Li, Xiaolei Tang, Feilong Kui, Qixin Spasic, Miodrag D. Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Control Systems Faculty of Electronic Engineering University of Nis Nis Serbia

ISBN: (纸本)9798350368604

In this paper, a synchronous control strategy based on super-twisting sliding mode algorithm is proposed to enhance the tracking accuracy and robustness of H-type linear motor systems. Such systems are widely utilized in precision manufacturing owing to their high precision and fast response. Firstly, the dynamic model is established based on the coupling dynamics analysis of the H-type system. Secondly, a super-twisting sliding mode controller (STSMC) with linear terms is designed, which effectively reduces the chattering associated with conventional sliding mode control, while maintaining good robustness. Thirdly, a super-twisting extended state observer (STESO) is introduced to handle large fluctuations in the early estimation process of linear extended state observer (LESO), so as to effectively observe and compensate the uncertainties and external disturbances. Finally, experimental results demonstrate that the STESO-based STSMC greatly improves the anti-disturbance ability and tracking accuracy of the H-type system. © 2024 IEEE.

关键词： Sliding mode control

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Estimation of RoA for Nonlinear Systems Based on Gaussian Processes 6

Estimation of RoA for Nonlinear Systems Based on Gaussian Pr...

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6th International Conference on Electronic engineering and Informatics, EEI 2024

作者： Chen, Yucheng Zhu, Yupeng Zhu, Hongming Zhang, Quanqi Wu, Chengwei Harbin Institute of Technology Department of Control Science and Engineering Harbin China

ISBN: (纸本)9798350353594

This paper employs Gaussian Processes (GPs) to estimate uncertainty dynamics inherent in nonlinear systems. A control policy ensuring the system satisfies initial stability constraints is derived using linear quadratic regulator (LQR). By modeling the Lyapunov function based on uncertain dynamics as GPs, the stability of the system dynamics in the Lyapunov sense is ensured. To discretize the Lyapunov function with Lipschitz continuity, a finite number of discrete points are chosen to represent the properties of the function. The region of attraction (RoA) based on Lyapunov function is widely utilized as a safe constraint in the field of control. Additionally, the maximum uncertainty dynamics are chosen as the boundary for exploration to expand the evaluation of the RoA. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Lyapunov functions

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Decentralized Cooperative control for Multi-Reconfigurable Manipulator Based on Adaptive Dynamic Programing

Decentralized Cooperative Control for Multi-Reconfigurable M...

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2024 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2024

作者： Dong, Bo Cao, Yu An, Tianjiao He, Xinyi Dong, Hongbo Ma, Bing Changchun University of Technology Department of Control Science and Engineering Changchun China

ISBN: (纸本)9798331504755

This paper proposes a decentralized cooperative control for multi-reconfigurable manipulator (MRM) based on Adaptive Dynamic Programing (ADP). The control method can achieve both motion path tracking and control the force between the manipulator and the controlled object. The dynamic model of reconfigurable manipulator and controlled object is established using the Newton-Euler algorithm and load distribution. When facing unknown terms in the model, radial basis function neural network (RBFNN) is used for approximation. By taking advantage of the ADP algorithm to obtain the optimal control strategy. The closed-loop MRM system is proved to be stable by using the Lyapunov theory. Finally, the effectiveness of the control method is verified through numerical simulation. © 2024 IEEE.

关键词： Radial basis function networks

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Curriculum-Guided Preference-based Reinforcement Learning: An Exploratory Approach 25

Curriculum-Guided Preference-based Reinforcement Learning: A...

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25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Wang, Zihao Zhu, Yuanyang Wei, Jingwen Chen, Chunlin Nanjing University Department of Control Science and Intelligence Engineering Nanjing China

ISBN: (纸本)9798350366600

Conveying complex objectives to reinforcement learning (RL) agents can often be difficult, involving meticulous design of reward functions that are sufficiently informative yet easy enough to provide. Human-in-the-loop RL methods allow practitioners to instead interactively teach agents through tailored feedback;however, such approaches have been challenging to scale since human feedback is very expensive. In this work, we propose a preference-based reinforcement learning method based on the idea of curriculum learning to improve the utilization of human preference feedback, namely Curriculum Guided Preference-based Reinforcement (CG-PbRL). CG-PbRL designs a difficulty metric to assess the difficulty of preference feedback queries, and learns the preference set based on the difficulty of preference queries through updating the training set, thereby training the reward estimation model from easy to difficult. Experimental results show that CG-PbRL can effectively improve the performance of preference-based reinforcement learning methods on a range of tasks. © 2024 IEEE.

关键词： Federated learning

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Friction Modeling and control Compensation for Electromechanical Braking Systems 8

Friction Modeling and Control Compensation for Electromechan...

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8th CAA International Conference on Vehicular control and Intelligence, CVCI 2024

作者： Houhua, Jing Qinggan, Lin Mingqi, Liu Harbin Institute of Technology Department of Control Science and Engineering Harbin China

ISBN: (纸本)9798331504892

Influenced by the electrification and intelligence of automobile chassis, electromechanical braking has become the future brake-by-wire trend choice by virtue of its simpler mechanism and faster servo performance, while system friction, as a key factor of its servo control, must be handled properly. This work studies the system friction modeling of electromechanical braking system and the corresponding control compensation approach. First, by evaluating the structure and working principle of the electromechanical braking system, the system model is produced, the braking control test rig is built and the system characteristic test is carried out. Then, the friction model parameters were identified from the experimental data based on the examination of system features. Finally, the brake control performance after friction compensation was experimentally validated by the physical rig, which verified the effectiveness of the friction model and its control compensation. © 2024 IEEE.

关键词： Braking performance

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The Investigation of Data Voting Algorithm for Train Air-Braking System Based on Multi-Classification SVM and ANFIS

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Chinese Journal of Electronics 2024年第1期33卷 274-281页

作者： Juhan WANG Ying GAO Yuan CAO Tao TANG School of Electronics and Information Engineering Beijing Jiaotong University Graduate Department China Academy of Railway Science National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University

The pressure data of the train air braking system is of great significance to accurately evaluate its operation state. In order to overcome the influence of sensor fault on the pressure data of train air braking system, it is necessary to design a set of sensor fault-tolerant voting mechanism to ensure that in the case of a pressure sensor fault, the system can accurately identify and locate the position of the faulty sensor, and estimate the fault data according to other normal data. A fault-tolerant mechanism based on multi-classification support vector machine(SVM) and adaptive network-based fuzzy inference system(ANFIS) is introduced. Multi-classification SVM is used to identify and locate the system fault state, and ANFIS is used to estimate the real data of the fault sensor. After estimation, the system will compare the real data of the fault sensor with the ANFIS estimated data. If it is similar,the system will recognize that there is a false alarm and record it. Then the paper tests the whole mechanism based on the real data. The test shows that the system can identify the fault samples and reduce the occurrence of false alarms.

关键词： Multi-classification support vector machine Adaptive network-based fuzzy inference system Train air braking system Fault-tolerant voting Multi-sensors

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Hyperparameter Optimization of Object Detection Networks Based on Large Language Models 50

Hyperparameter Optimization of Object Detection Networks Bas...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Hu, Yushuo Wang, Qiang Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9781665464543

Since the emergence of deep learning, the configuration of hyperparameters has been one of the most significant problems that people have paid attention to. However, traditional hyperparameter optimization techniques typically have drawbacks such as expensive calculation consumption and lengthy training time. With the development of large language models (LLMs), particularly models such as generative pretrained transformer (GPT), it is achievable to utilize the models’ analysis ability to guide parameter space search. This paper investigates the use of LLMs for hyperparameter optimization in machine learning problems. To evaluate the effectiveness of this strategy, four object detection models, including YOLOv5, YOLOv8, Faster R-CNN, and R-FCN, were used to conduct experiments. At the same time, we compared the distinctions between this approach and traditional ways. The experimental results indicate that combining LLMs with the hyperparameter optimization process can perform comparably or better than conventional methods. © 2024 IEEE.

关键词： Deep learning

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