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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Liu, Zhenting Liu, Shan College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

This paper proposes an improved residual deep reinforcement learning method for robot arm dynamic obstacle avoidance and position servo. The proposed method first simplifies the state space by constructing key points and pre-trains a model capable of completing obstacle avoidance tasks using the simplified state. Then, when training with real state information, a guiding network is used to help accumulate good samples, which improves the training efficiency. To overcome the convergence difficulty of residual DQN in robot arm obstacle avoidance, this paper incorporates the action of the feedback controller into the action space and uses incremental reward values to evaluate the actions. Simulation results demonstrate that the proposed method can effectively achieve robot arm dynamic obstacle avoidance and position servo. © 2024 IEEE.

关键词： Deep reinforcement learning

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Node-Decomposition Privacy-Preserving for Finite-Time Secondary control of DC Microgrids

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IEEE Transactions on Smart Grid 2025年第4期16卷 3295-3304页

作者： Wu, Han Zhu, Si-Min Chai, Li Zhu, Zhen-Hua Tian, Yu-Chu Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology School of Artificial Intelligence and Automation Wuhan430081 China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou China Queensland University of Technology School of Computer Science BrisbaneQLD4001 Australia

In the distributed secondary control of DC microgrids, achieving both rapid convergence and robust information security is critical for enhancing control performance and maintaining power quality. However, current privacy-preserving approaches often compromise either control performance or privacy preservation performance. To address this challenge, this paper presents a node-decomposition privacy-preserving (NDPP) method for fully distributed, finite-time secondary control in DC microgrids. By splitting each node in the communication graph into multiple sub-nodes, the method enhances both convergence speed and privacy preservation without sacrificing control accuracy. Sub-nodes communicate within groups, and the topology of the generated graph is concealed from malicious nodes and eavesdroppers. Theoretical analysis is provided to prove finite-time voltage regulation and current sharing with privacy preservation against malicious nodes and external eavesdroppers. Simulations are conducted to verify the effectiveness of the method in maintaining control performance and power quality. © 2010-2012 IEEE.

关键词： Privacy by design

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An improved adaptive minimum entropy deconvolution method in bearing fault detection 4

An improved adaptive minimum entropy deconvolution method in...

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4th International Conference on Mechanical, Electronics, and Electrical and Automation control, METMS 2024

作者： Liao, Zihao Xu, Jinglun Jin, Minglv Cao, Yuqi Cao, Yunqi Hou, Dibo Huang, Pingjie State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (数字)9781510679887

ISBN: (纸本)9781510679870

Bearings, a crucial element in rotating machinery, are most susceptible to failure during operation, making up over half of all malfunctions. Detecting bearing faults in a timely manner can be quite challenging due to the frequent influence of strong noise on the fault features. To address the issue, an adaptive parameter optimization based on Minimum Entropy Deconvolution (MED) method in conjunction with Infogram is proposed. First, the Infogram is created through spectral negentropy, followed by the construction of a filter that utilizes the center frequency and the frequency band exhibiting the highest spectral negentropy. This filter is then applied to the original vibration signal in order to isolate the frequency band that contains the most significant bearing fault information. Then, based on MED method, the autocorrelation function and L1 norm are introduced to evaluate the filtered signal to realize the adaptive search of deconvolution filter length. Finally, the characteristic frequency components are extracted by envelope spectrum analysis. The validation of the effectiveness of the proposed method is conducted on a public dataset across various bearing operating conditions. The verification results show that this method can accurately detect the conditions of bearings, and extract more distinctive characteristic frequency components compared with some relevant techniques. © 2024 SPIE.

关键词： Spectrum analysis

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Class-Imbalance and Client-Imbalance Federated Learning for Fault Diagnosis 13

Class-Imbalance and Client-Imbalance Federated Learning for ...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Sun, Shu Qian, Jinchuan Zhang, Xinmin Song, Zhihuan College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

With the development of big data, data-driven fault diagnosis models have experienced rapid growth. However, as the complexity of models increases, there is a corresponding rise in the demand for data. To obtain a substantial amount of data, traditional approaches involve centralizing datasets from multiple factories for training. However, due to heightened emphasis on data privacy from enterprises and regulatory bodies, this method becomes more challenging to implement and poses risks of data leakage. In response to this challenge, this paper designs a novel federated learning-based fault diagnosis modeling approach. Each local device trains its model on its local dataset and subsequently uploads updated model parameters to a central server for global model aggregation and updating. However, it is worth noting that in industrial processes, there may be concurrent class imbalance and data client imbalance, which brings challenges to fault diagnosis modeling based on federated learning. To address this challenge, we further propose a dynamic client aggregation fault diagnosis method based on category loss in federated learning. Experimental results indicate that the proposed approach not only protects data privacy in imbalanced class and client scenarios but also achieves outstanding fault recognition accuracy and robustness. © 2024 IEEE.

关键词： Federated learning

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Research on Multi-Task Scheduling in Smart Warehouse Systems Based on Dual-Task Chains 21

Research on Multi-Task Scheduling in Smart Warehouse Systems...

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21st International Conference on Networking, Sensing and control, ICNSC 2024

作者： Zhang, Chenwei Wu, Weimin Institute of Cyber-Systems and Control Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350365221

With the advancement of warehousing technology, the allocation of multiple tasks in multi-AGV systems has become an important factor in order to decrease task completion time in the warehouse. This paper develops a multi-task scheduling algorithm that employs a dual-task chain approach to maximize task execution benefits. For the multi-task allocation problem, this paper proposes a dual-task chain-based task allocation algorithm (DTTA), which uses the dual-task chain strategy as a mechanism for selecting multiple tasks, taking into consideration derived tasks to enhance solution quality. Simulation experiments show that the proposed algorithm and model perform better than other methods. © 2024 IEEE.

关键词： Scheduling algorithms

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Optimal Traffic-Power Flow of Transportation-Microgrid Coupled System Based on Spatiotemporal Coupling

Optimal Traffic-Power Flow of Transportation-Microgrid Coupl...

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2024 China Automation Congress, CAC 2024

作者： Li, Bin Li, Jia Li, Qincheng Liu, Zhitao Su, Hongye The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798350368604

The growing adoption of electric vehicles and dynamic wireless charging has strengthened the spatiotemporal interconnection between the transportation network (TN) and the microgrid (MG). This paper presents an optimal traffic-power flow (OTPF) model for the transportation-microgrid coupled system (TMCS) to enhance the coordination of its operations. The OTPF model is transformed into bi-level programming, with the optimal power flow model of MGs at the upper level and the dynamic traffic assignment model of the TN at the lower level. The upper level is a mixed-integer second-order cone programming, while the lower level is formulated as a dynamic user equilibrium. A fixed-point algorithm is developed to tackle this bi-level programming. Case studies show that the OTPF model of the TMCS can be efficiently solved to capture the spatiotemporal variations of traffic flow and power flow. © 2024 IEEE.

关键词： Second-order cone programming

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Optimal Prices for Coordinated Operation of Coupled Transportation and Power Systems

Optimal Prices for Coordinated Operation of Coupled Transpor...

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2024 China Automation Congress, CAC 2024

作者： Li, Jia Li, Bin Li, Qincheng Liu, Zhitao Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798350368604

The growing adoption of electric vehicles (EVs) necessitates an in-depth analysis of the effects of centralized charging on both traffic congestion and power distribution networks (PDNs). This study presents a coupled model of transportation and power systems that incorporates EV travel behavior and charging costs, emphasizing their interaction between the urban transportation network and the PDN via dynamic wireless energy transmission technology. The equilibrium of the coupled system was then determined through an iterative algorithm aimed at minimizing the total social cost. Example results validate the model’s accuracy and effectiveness. Furthermore, the study demonstrates that the proposed approach can significantly alleviate traffic congestion while simultaneously satisfying the charging needs of EVs and maintaining the stability of the power distribution network. © 2024 IEEE.

关键词： Power distribution networks

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Path Planning Algorithm for Manipulator Based on Manipulability-Maximizing 13

Path Planning Algorithm for Manipulator Based on Manipulabil...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Zhang, Rui Liu, Shan College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

An improved path planning algorithm based on RRT∗ is proposed for the manipulator to generate a collision-free path for end-effector in this paper, which maximizes the manipulator's manipulability throughout the entire path. The algorithm introduces manipulability information as prior information to guide the RRT node toward desired regions. By introducing hybrid cost into improved RRT* algorithm, the path length is minimized while the expected manipulability is maximized. Finally, the end-effector path is transformed into a configuration trajectory by solving a multi-objective optimization problem. The simulation results compared with RRT* demonstrates the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： End effectors

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Detection of Oscillations in Process control Loops From Visual Image Space Using Deep Convolutional Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 982-995页

作者： Tao Wang Qiming Chen Xun Lang Lei Xie Peng Li Hongye Su the Department of Electronic Engineering the School of InformationYunnan UniversityKunming 650091China DAMO Academy Alibaba GroupHangzhou 311100China the State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027China

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant *** numerous automatic detection techniques have been proposed,most of them can only address part of the practical *** oscillation is heuristically defined as a visually apparent periodic ***,manual visual inspection is labor-intensive and prone to missed *** neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction *** this work,an exploration of the typical CNN models for visual oscillation detection is ***,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation *** feasibility and validity of this framework are verified utilizing extensive numerical and industrial *** with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and *** addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

关键词： Convolutional neural networks(CNNs) deep learning image processing oscillation detection process industries

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Impact Analysis of MTD on the Frequency Stability in Smart Grid

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 275-277页

作者： Zhenyong Zhang Ruilong Deng the State Key Laboratory of Public Big Data and the College of Computer Science and Technology Guizhou UniversityGuiyang 550025China IEEE the State Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China

Dear Editor, In order to accommodate the effects of false data injection attacks(FDIAs), the moving target defense(MTD) strategy is recently proposed to enhance the security of the smart grid by perturbing branch susceptances. However, most pioneer work only focus on the defending performance of MTD in terms of detecting FDIAs and the impact of MTD on the static factors such as the power and economic losses.

关键词： Smart MTD Impact

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