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63rd IEEE Conference on Decision and control, CDC 2024

作者： Duan, Mengmeng Zhu, Shanying Yang, Ziwen Chen, Cailian Guan, Xinping The authors are with the Department of Automation Shanghai Jiao Tong University Shanghai 200240 ChinaKey Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China and also Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

ISBN: (纸本)9798350316339

In this paper, we investigate the distributed optimization problem for heterogeneous linear multi-agent systems with unknown disturbances. By applying the primal-dual method and the time-scale separation technique, a distributed dynamic controller without using any disturbance information is proposed. Based on the optimal condition, the relationship between the optimal solution and the equilibrium point of the system is established, and it is shown that the distributed optimization problem is solved provided that the closed-loop system is stable. Then, inspired by the nonsingular perturbation analysis, it is proved that the closed-loop system is exponentially input-to-state stable with respect to the derivative of the disturbance. Numerical examples are provided to illustrate the theoretical results. © 2024 IEEE.

关键词： Closed loop systems

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Lifespan Prediction of Electronic Card in Nuclear Power Plant Based on Few Samples

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Journal of Shanghai Jiaotong University (Science) 2023年 1-7页

作者： Xu, Yong Cai, Yunze Song, Lin Department of Automation Shanghai Jiao Tong University Shanghai200240 China Fujian Fuqing Nuclear Power Co. Ltd. Fujian Fuqing350318 China Key Laboratory of System Control and Information Processing of the Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

A lifespan prediction model was developed based on a few samples to provide decision-making information for operation and maintenance, as well as improve the economy and safety of nuclear power plant (NPP) operations. This paper applies a Weibull model to forecast the lifespan of electronic cards with a few samples in NPPs. Relationship between the lifespan prediction of electronic cards and the ambient temperature is revealed using the Arrhenius equation. Censored samples are used to compensate for the lack of fault electronic card data. Scale parameter and shape parameter of the Weibull model are optimized by adjusting the weight ratio between the censored data and the fault data. Characteristic life is then obtained using the rank regression fitting equation. Parameters of the Arrhenius equation can be calculated by dividing the samples into groups according to the ambient temperature. A case study of the intermediate range high-voltage electric card of ex-core neutron detectors demonstrates that the lifespan prediction of electronic cards in NPPs can be successfully predicted with a few samples by combining the Weibull model and the Arrhenius model. This can help provide preventive maintenance recommendations for electronic cards. Finally, operation suggestions for the electronic card’s ambient temperature can be made by utilizing the temperature-life model. © 2023, Shanghai Jiao Tong University.

关键词： Nuclear power plants

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A Cloud-Edge-Based Multi-Label Learning and Fast Clustering Method for Emerging New PV Faults 13

A Cloud-Edge-Based Multi-Label Learning and Fast Clustering ...

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13th IEEE International Conference on CYBER Technology in automation, control, and Intelligent systems, CYBER 2023

作者： Liu, Qi Yang, Bo Liu, Yuxiang Ma, Kai Guan, Xinping Shanghai Jiao Tong University Ministry of Education Key Laboratory of System Control and Information Processing Department of Automation Shanghai200240 China School of Electrical Engineering Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9798350315196

Aiming at accurate identification of emerging new faults in PV systems, a novel PV fault diagnosis framework based on cloud-edge collaboration is designed. Model updates are deployed on the cloud servers, while real-time diagnosis is implemented at the edge devices. A multi-label learning model is developed to extract fault features efficiently, and a threshold scheme is further designed for feature enhancement. The obtained features are processed by K-Means clustering and the One-Class Support Vector Machine (OCSVM) methods, which can accurately diagnose existing types and identify unknown faults. For analysis and demonstration, the Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) methods are used for dimensionality reduction and visualization. Numerical results validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： K-means clustering

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Improved Double Deep Q Network Algorithm Based on Average Q-Value Estimation and Reward Redistribution for Robot Path Planning

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Computers, Materials & Continua 2024年第11期81卷 2769-2790页

作者： Yameng Yin Lieping Zhang Xiaoxu Shi Yilin Wang Jiansheng Peng Jianchu Zou Key Laboratory of Advanced Manufacturing and Automation Technology Guilin University of TechnologyEducation Department of Guangxi Zhuang Autonomous RegionGuilin541006China Guangxi Key Laboratory of Special Engineering Equipment and Control Guilin University of Aerospace TechnologyGuilin541004China Guilin Mingfu Robot Technology Company Limited Guilin541199China Key Laboratory of AI and Information Processing Education Department of Guangxi Zhuang Autonomous RegionHechi UniversityYizhou546300China

By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile ***,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality *** those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was ***,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q ***,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory ***,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality ***,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random *** experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average *** performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random *** not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation.

关键词： Double Deep Q Network path planning average Q-value estimation reward redistribution mechanism reward-prioritized experience selection method

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Quantum gyroscopes based on double-mode surface-acoustic-wave cavities

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Physical Review Applied 2024年第1期22卷 014061-014061页

作者： Yuting Zhu Shibei Xue Fangfang Ju Haidong Yuan Department of Automation Key Laboratory of System Control and Information Processing Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 People’s Republic of China School of Physics and Electronics Department of Mechanical and Automation Engineering

Recent progress shows that a surface-acoustic-wave (SAW) cavity can not only induce quantum acoustic dynamics, but can also form optomechanical-like systems. Its operating frequencies in the microwave band make it resistant to the thermal noise of surrounding environments, while its radiation-pressure couplings make it susceptible to weak forces. Based on these advantages, we propose a gyroscope comprising coupled microwave-SAW cavities. In this paper we systematically consider the range, signal-to-noise ratio, and sensitivity, which are the three most important indices to gyroscopes, but only partially considered in existing works. Additionally, we establish the fundamental limits of sensitivity when the quantum input is in the vacuum state and the squeezed vacuum state. We find that squeezing improves sensitivity and can surpass the standard quantum limit. However, this improvement can only reach up to 2/2 even as the squeezed parameter approaches infinity, which is rarely noted in recent works. Finally, we also offer analytical constraints for cooperativity and squeezed parameters. These constraints can be utilized to design gyroscopes based on coupled cavities in experiments.

关键词： Open quantum systems & decoherence Quantum sensing Rotation sensing Coupled oscillators Cavity resonators Homodyne & heterodyne detection Microwave techniques Surface acoustic wave

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Learning-Based Motion Planning with Mixture Density Networks

Learning-Based Motion Planning with Mixture Density Networks

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IEEE International Conference on Robotics and automation (ICRA)

作者： Yinghan Wang Xiaoming Duan Jianping He Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The trade-off between computation time and path optimality is a key consideration in motion planning algorithms. While classical sampling based algorithms fall short of computational efficiency in high dimensional planning, learning based methods have shown great potential in achieving time efficient and optimal motion planning. The SOTA learning based motion planning algorithms utilize paths generated by sampling based methods as expert supervision data and train networks via regression techniques. However, these methods often overlook the important multimodal property of the optimal paths in the training set, making them incapable of finding good paths in some scenarios. In this paper, we propose a Multimodal Neuron Planner (MNP) based on the mixture density networks that explicitly takes into account the multimodality of the training data and simultaneously achieves time efficiency and path optimality. For environments represented by point clouds, MNP first efficiently compresses point clouds into a latent vector by encoding networks that are suitable for processing point clouds. We then design multimodal planning networks which enables MNP to learn and predict multiple optimal solutions. Simulation results show that our method outperforms SOTA learning based method MPNet and advanced sampling based methods IRRT* and BIT*.

关键词： Point cloud compression Learning systems Training Simulation Neurons Training data Encoding

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Distributed Task Allocation with Time Constraints Under Limited Communication Resources in Multi-Agent system

Distributed Task Allocation with Time Constraints Under Limi...

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Asian control Conference

作者： Weilin Li Jing Wu Chengnian Long Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, a new distributed allocation algorithm based on performance impact with collective influence (PI-CI) is proposed. Considering inefficiency of creating time slots in PI-MaxAss, two completely independent phases with different communication frequencies are designed to ensure allocation both real-time and conflict-free under limited communication. To solve the misconvergence of the baseline algorithm PI-MaxAss in allocation results, an evaluation function is put forward in consensus phase to directly determines the final attribution of repeatedly assigned tasks, guaranteeing convergence of allocation. Comparison experiments validate the performance improvement of the PI-CI algorithm over the baseline algorithm.

关键词： Time-frequency analysis Real-time systems Resource management Time factors Multi-agent systems Radio spectrum management Convergence

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Multi-Agent Path Planning for Finite Horizon Tasks with Counting Time Temporal Logics

Multi-Agent Path Planning for Finite Horizon Tasks with Coun...

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IEEE International Conference on automation Science and Engineering (CASE)

作者： Peng Lv Shaoyuan Li Xiang Yin Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we consider the multi-agent path planning problem for high-level tasks with finite horizons. In many situations, there is the need to count how many times a sub-task is satisfied in order to achieve the overall task. However, existing temporal logic languages, such as linear temporal logic, may not be efficient in describing such requirements. To address this issue, we propose a new temporal logic language called Counting Time Temporal Logic (CTTL) that extends linear temporal logic by explicitly counting the number of times that some tasks are satisfied. To solve the CTTL path planning problem, we use integer linear programming to encode the satisfaction of the task. We demonstrate that our approach is both sound and complete. To validate our results, we present numerical experiments to show the scalability of the proposed approach. Furthermore, we provide a simulation case study of a team of autonomous robots to illustrate the synthesis procedure.

关键词： Computer aided software engineering automation Scalability Integer linear programming Path planning Planning Delays Logic Standards Autonomous robots

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Hybrid Traffic Scheduling Based on Adaptive Time Slot Slicing in Time-Sensitive Networking

Hybrid Traffic Scheduling Based on Adaptive Time Slot Slicin...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Shouliang Xu, Qimin Zhang, Yanzhou Xu, Lei Chen, Cailian Shanghai Jiao Tong University Department of Automation Shanghai China 200240 Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781728119489

The development of the Industrial Internet of Things (IIoT) is increasingly relying on the low-latency and low-jitter information exchange. Time-Sensitive Networking (TSN) guarantees the deterministic transmission by a set of mechanisms, such as Time-Aware Shaper (TAS) and Cyclic Queuing and Forwarding (CQF). However, the deterministic scheduling of hybrid traffic with different Quality of Service (QoS) is still unresolved since traffic performance is hard to meet by a single mechanism or an integrated mechanism with fixed time slot. In this paper, to realize the low-latency and low-jitter hybrid traffic scheduling, an adaptive time slot slicing (ATSS) method is proposed integrating TAS and CQF. With dynamically slicing the time slot to centralize time-triggered (TT) traffic, ATSS eliminates the irregular distribution of TT flow to support multi-hop networks. A resource calculation method based on slot occupancy judgment is devised to reduce the scheduling complexity, which raises the analysis objects into flows instead of frames. To reduce the complexity further, an incremental algorithm with the search boundary of resources is designed. The evaluation results show that ATSS reduces the jitter by 12 times and time cost by 200 times when handling over 125 mixed flows in different network topologies compared to previous work. © 2022 IEEE.

关键词： Jitter

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Jointly Learning Time-Structure-History Graph Embedding for Temporal Knowledge Graph Reasoning 19

Jointly Learning Time-Structure-History Graph Embedding for ...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Duan, Hao Jin, Haoyu Chen, Kang Du, Shaochong Fang, Tao Huo, Hong Shanghai Jiao Tong University Department of Automation Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9798350346558

The Temporal Knowledge Graph(TKG) reasoning is an imperative task for various applications of TKG. However, reasoning over TKGs which aims to predict future facts has been less explored. Intuitively, entities and relations in TKG change differently over time, and the variations are different. It also can be observed that what happens in the past may affect the future. Apart from these, structure information that exists in the neighborhood of a given entity in a KG has been proven to be important. Inspired by these, a novel graph embedding method for TKG reasoning, called time-structure-history graph embedding (TSHGE), is proposed in this paper. TSHGE consists of three progressive learning units. The first time information learning unit employs a novel time-aware mechanism to encode the time influence on entities and relations. Then a relation-aware GAT (Graph Attention Network) is used in the structure information learning unit to model the 1-hop neighborhoods of all entities in a KG at each timestamp in TKG. Finally, the history information learning unit utilizes GRU (Gated Recurrent Unit) to learn history information from various facts. Then the ultimate embedding representations of entities and relations can be obtained for the prediction of future facts. The evaluations on three public TKG datasets have demonstrated the effectiveness and superiority of TSHGE for predicting future facts. © 2022 IEEE.

关键词： Forecasting

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