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Tracking performance limitations of networked control systems with packet dropouts and logarithmic quantization 41

Tracking performance limitations of networked control system...

引用

第41届中国控制会议

作者： Jianhao Li Xiaowei Jiang Hao Chen Shaoying Wang Xianhe Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System College of Science Binzhou University School of Electrical Engineering and Automation Hubei Normal University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we focus on the tracking performance(TP) limitations of networked control systems(NCS s).The control system considered is a multi-input and multi-output(MIMO) discrete-time network control system with time delay,channel noise,packet dropouts,and logarithmic quantization communication *** modeling the channel,we assumed the channel noise to be additive white Gaussian noise(AWGN) and used a binary random process to model packet dropouts and a logarithmic quantizer to model the quantization *** explicit expression of the TP limitations of NCSs utilizing the two-degree-of-freedom(TDOF) controller is then derived using the frequency-domain approach,coprime decomposition,and Youla parameterization *** results show that the essential characteristics of the plant(locations and directions of nonminimum phase zeros and unstable poles) and network constraints(time delay,packet dropouts,quantization,and noise power spectral density) are closely related to the TP limitations.

关键词： Networked control systems tracking performance limitations packet dropouts logarithmic quantization

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NBA-guided heuristic sampling algorithm for multi-robot task planning under temporal logic 43

NBA-guided heuristic sampling algorithm for multi-robot task...

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43rd Chinese control Conference, CCC 2024

作者： Luo, Jiong Hua, Yongzhao Ding, Shunfu Dong, Xiwang Ren, Zhang Beihang University School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Institute of Unmanned System Beihang University Beijing100191 China

ISBN: (纸本)9789887581581

Aiming at the task planning problem of multi-robot under linear temporal logic (LTL), this paper proposes an improved sampling planning algorithm based on Nondeterministic Büchi automaton (NBA) guidance. Firstly, this paper defines a kind of LTL task planning problem, which mainly focuses on the meaningful key nodes in the task execution of each robot, and there are many sequential dependencies of task constraints. Then, an improved sampling algorithm framework is proposed, which uses a more efficient way to build a search tree and reduces the number of nodes in the tree. Furthermore, the sampling points are guided by NBA to ensure that sampling is conducive to the advancement of time-series tasks. And considering the task feasible region of each robot, the auction algorithm is introduced to optimize the local matching relationship, which greatly improves the quality of sampling points. Finally, numerical simulation is carried out to solve the LTL task planning problem. The optimality and stability of the algorithm are obviously improved, and the time consumption of this algorithm is obviously better than that of the existing advanced algorithms. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Trees (mathematics)

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Fault Diagnosis and Prognosis in Partially-Observed Discrete Event systems with Delayed Observations 63

Fault Diagnosis and Prognosis in Partially-Observed Discrete...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Wang, Jiwei Baldi, Simone Yu, Wenwu Yin, Xiang Southeast University School of Cyber Science and Engineering Nanjing210096 China Southeast University School of Mathematics Nanjing210096 China Shanghai Jiao Tong University Department of Automation Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9798350316339

Fault diagnosis and prognosis in discrete event systems are studied in the scenario where the observations are possibly received with delay. To address this scenario, two conditions for diagnosis and prognosis with delayed observations are proposed, where we show that the state-of-the-art notion of prognosability must be revised to avoid conservativeness. Diagnosability and prognosability conditions are then verified by introducing a delay observer and a new verification function. Theoretical analysis indicates the effectiveness of the verification method for fault diagnosis and prognosis in the system. © 2024 IEEE.

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Nonlinear control of Crane systems Based on Intelligence Computing of Disturbance Observer Under Mismatched Disturbance 22

Nonlinear Control of Crane Systems Based on Intelligence Com...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wang, Tianlei Wang, Yiyan Xu, Meng Tian, Jiajie Zhang, Jingling Hong, Zhiyong Wuyi University School of Electronic and Information Engineering Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China Wuyi University School of Mechanical and Automation Engineering Jiangmen China Guangdong Kejie Technology Co. Ltd Jiangmen529020 China

ISBN: (纸本)9798331509712

A control method based on disturbance observer is proposed to address the positioning drift issue of the gantry crane under unmatched disturbances, aiming at precise positioning of the trolley and effective payload swing angle attenuation in a variable-length two-dimensional lifting system. The control scheme comprises a combination of a proportional-derivative controller and a disturbance observer. Firstly, the design of the coupling function in this paper is based on the observation of the natural characteristics of the dynamical model. It cleverly designs the coupling function, which not only helps to effectively reduce the swing angle but also prevents the problem of positioning drift under non-matching disturbances, subsequently, design a controller by integrating intelligent computing with control theory. Secondly, the designed disturbance observer observes and compensates for the system output to achieve disturbance suppression. Thirdly, a thorough stability analysis has been conducted to demonstrate that the proposed controller satisfies the desired conditions. Finally, simulation results illustrate that the pro-posed method success-fully addresses the limitations of existing methods and exhibits superior performance and robustness. © 2024 IEEE.

关键词： Gantry cranes

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Multi-agent Deep Reinforcement Learning Based Multi-objective Charging control for Electric Vehicle Charging Station 7th

Multi-agent Deep Reinforcement Learning Based Multi-objectiv...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Gong, Jiabao Fu, Weiming Kang, Yu Qin, Jiahu Xiao, Feng Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733279

This paper proposes a multi-agent deep reinforcement learning (MADRL) based algorithm for charging control of multiple electric vehicles (EVs) in an electric vehicle charging station (EVCS) with dynamic operations. By taking the uncertainties of EV users’ behaviors and charging prices into consideration, a Markov game with multiple objectives is first established, where the individual objective of reducing EV charging costs and the collective objective of mitigating charging load fluctuations are both studied. Then, a multi-agent actor-double-critic (MAADC) algorithm with a unified advantage actor-critic framework is proposed, where two advantage functions are designed as the local and global critics, and the integrated actor is employed to balance the learning towards the individual and collective objectives. The simulation results obtained in a delicately built EVCS environment show the effectiveness of MAADC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Reinforcement Learning-Based Adaptive Search Strategy Combination Algorithm 22

Reinforcement Learning-Based Adaptive Search Strategy Combin...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Liu, Xiaotong Zeng, Zhiqiang Wan, Sicheng Tian, Jiajie Wang, Tianlei Hong, Zhiyong Wuyi University School of Mechanical and Automation Engineering Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China Wuyi University School of Electronic and Information Engineering Jiangmen China Guangdong Kejie Technology Co. Ltd. Jiangmen China

ISBN: (纸本)9798331509712

Integrating multiple search operators to leverage the characteristics of different search operators is one of the common methods to enhance the performance of evolutionary algorithms. Most of these combination algorithms adopt a fixed framework, which makes it difficult to adaptively select the appropriate search operator during the algorithm iteration process, resulting in poor performance on certain problems. To overcome the aforementioned shortcomings of combination algorithms, an adaptive search strategy combination algorithm based on reinforcement learning and neighborhood search (RLASCA) is proposed. During the algorithm iteration process, a reinforcement learning-based adaptive search operator selection method (RLAS) is designed, enabling the algorithm to adaptively select the appropriate search operator based on the state of the individual. Furthermore, to address the issue of premature convergence, a neighborhood search strategy based on differential evolution (NSDE) is designed to increase the diversity of the population. To verify the effectiveness of the proposed algorithm, a comprehensive testing was conducted using the CEC2017 test suite and two constrained engineering design problems. The algorithm was compared with four basic algorithms that compose RLASCA and four classical combination algorithms. The experimental results indicate that RLASCA outperforms the other eight algorithms in terms of convergence speed and accuracy. © 2024 IEEE.

关键词： Optimization algorithms

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Patchcore-SAM: Rail Defect Unsupervised Detection and Segmentation 27

Patchcore-SAM: Rail Defect Unsupervised Detection and Segmen...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： He, Shuyi Cao, Yuan Wang, Feng Sun, Yongkui Su, Shuai Yang, Weifeng Wang, Wenkun National Engineering Research Center of Rail Transportation Operation Control System School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Crrc Zhuzhou Institute Co Ltd Zhuzhou412001 China

ISBN: (纸本)9798331505929

The heavy-haul railways plays a crucial role in facilitating the transportation of large-scale goods, yet the challenging operational conditions significantly increase the occurrence of rail defects. Despite the growing importance of computer vision and artificial intelligence in detecting rail defects, current methods face issues of low accuracy and efficiency. The success of the Segment Anything Model (SAM) provides a promising solution to these problems. In this study, a novel two-stage rail defect evaluation methodology called Patchcore-SAM was proposed. Firstly, Patchcore enables the unsupervised detection and localization of defects. Subsequently, the detected anomalies are transformed into anomaly images and prompts, which serve as inputs for the fine-tuned SAM to achieve precise rail defect segmentation. The experimental results demonstrate the superior performance of this method, providing robust support for ensuring the safe operation of heavy-haul railways. © 2024 IEEE.

关键词： Railroads

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Rail Fastener Defect Detection of Heavy Haul Railway Based on Improved YOLOv8 27

Rail Fastener Defect Detection of Heavy Haul Railway Based o...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Li, Xinman Cao, Yuan Wang, Feng Sun, Yongkui Su, Shuai Yang, Weifeng Wang, Wenkun School of Automation and Intelligence Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation Control System Beijing100044 China Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Crrc Zhuzhou Institute Co Ltd Zhuzhou412001 China

ISBN: (纸本)9798331505929

Rail fasteners play a crucial role in the heavy haul railway lines, serving as vital components of railway infrastructure. However, due to the intricate nature of the heavy haul railway environment, fasteners can often be obscured, leading to misjudgments and inaccuracies for the existing defect detection algorithms. To address this challenge, a novel method to detect rail fastener defects based on an improved YOLOv8 model is proposed. Initially, a new dataset specifically tailored for fastener defect detection in heavy haul railway lines is constructed utilizing onsite data. To enrich feature information and enhance detection accuracy, multiple improvements are introduced, including the addition of a P6 feature layer, the design of a cross-layer connection neck (CCNeck) feature fusion network, and the introduction of the DySample up-sampling operator. Furthermore, the Quality Focal Loss (QFL) function is introduced to address class imbalance problem within the datasets. Finally, experiments are conducted and the results demonstrate significant enhancements in detection mean average precision (mAP) compared to existing state-of-the-art networks. © 2024 IEEE.

关键词： Rails

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Data-driven Fault Diagnosis Method Based on Fusion of Multi-direction and Multi-source Signals 5

Data-driven Fault Diagnosis Method Based on Fusion of Multi-...

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5th International Conference on Intelligent Autonomous systems, ICoIAS 2022

作者： Yang, Yang Wang, Junda Li, Ning Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

As a pivotal mechanical component of turbine drive-chain, gearbox suffers from various accidents and fault diagnosis could remarkably enhance its reliability and maintain running safety and lifetime. Vibration-based analysis on single-channel is mainly adopted in previous diagnostic, which cannot catch full vibration inner information in gearbox sensors and bring model performance down. In this paper, a novel method based on the fusion of multi-direction and multi-source signals and the convolutional neural network (MDMS-CNN) is presented. This method can obtain multi-dimensional feature maps from MDMS and preserve the time correlation among different signal sequences. First, features from MDMS are extracted via a corrosion conversion method that converts signals into two-dimensional (2-D) gray images. Then, homologous images are spliced into same channels, while heterogeneous images are superimposed to different channels. Finally, fusion features are input into CNN for fault classification. In experiments, the method is validated on a gearbox diagnosis case. Results have shown that the proposed method achieves 99.86% recognition accuracy and faster convergence speed compared to others. © 2022 IEEE.

关键词： Failure analysis

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Differentiator-Based Adaptive H8 Tracking control of Fully Actuated systems 3

Differentiator-Based Adaptive H8 Tracking Control of Fully A...

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3rd Conference on Fully Actuated system Theory and Applications, FASTA 2024

作者： Feng, Yuxin Li, Zhiqiang Liu, Yang He, Zhaoshui Li, Hongyi Guangdong University of Technology School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China

ISBN: (纸本)9798350373691

This article addresses an adaptive tracking control problem for uncertain high-order fully actuated (HOFA) systems with unknown parameters and disturbances. Under the framework of backstepping, the unknown parameter is estimated and the external disturbance is handled by using adaptive control and H8 technique, respectively. Moreover, the Levant differentiator is employed to reduce the computation burden. Additionally, the boundedness of signals in the closed-loop system is proven using the Lyapunov theory. Finally, the effectiveness of the proposed scheme is validated through the simulation study. © 2024 IEEE.

关键词： Backstepping

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