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Chinese control and Decision Conference, CCDC

作者： Xinwei Li Dianyang Li Zhuang Cai Zhi Zhou Guangdong Zhao Jian Feng Power System and New Energy Technology Research Center State Grid LiaoNing Electric Power Supply Co. LTD Electric Power Research Institute Shenyang China Power Dispatching Control Center State Grid Liaoning Electric Power Co. LTD Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The integration of intermittent and fluctuating renewable energy sources such as wind and solar power into the power grid poses significant challenges for load balancing in the electricity system. To address the insufficient peak load regulation capability after the large-scale integration of renewable energy sources, a dual-layer coordinated optimization scheduling model is proposed by leveraging the rapidly evolving electrochemical energy storage device, namely the flow battery. In the first layer of the model, the objective is to enhance the overall peak load regulation capability of the power system by flattening load peaks and filling load valleys to improve the system's flexibility in load regulation and balance the supply-demand fluctuations. In the second layer of the model, the goal is to minimize the total cost of the power system operation while satisfying the optimization results of the first layer. This is achieved by re-planning the output of each component, including thermal power generation, renewable energy generation, and flow battery energy storage, to minimize the difference between costs and profits, thus reducing expenses. Finally, an improved IEEE 30-node system is used as a case study for simulation validation. The results demonstrate that with the integration of renewable energy sources, leveraging flow batteries for system load regulation enhances the overall peak load regulation capability and reduces the total cost of the system.

关键词： Renewable energy sources Costs Fluid flow Regulation Power grids Batteries Thermal loading

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Event-triggered Consensus control of Heterogeneous Multi-agent systems: Model- and Data-based Analysis

arXiv

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arXiv 2022年

作者： Wang, Xin Sun, Jian Wang, Gang Chen, Jie The School of Automation The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

This article deals with model- and data-based consensus control of heterogenous leader-following multi-agent systems (MASs) under an event-triggering transmission scheme. A dynamic periodic transmission protocol is developed to significantly alleviate the transmission frequency and computational burden, where the followers can interact locally with each other approaching the dynamics of the leader. Capitalizing on a discrete-time looped-functional, a model-based consensus condition for the closed-loop MASs is derived in form of linear matrix inequalities (LMIs), as well as a design method for obtaining the distributed controllers and event-triggering parameters. Upon collecting noise-corrupted state-input measurements during open-loop operation, a data-driven leader-following MAS representation is presented, and employed to solve the data-driven consensus control problem without requiring any knowledge of the agents' models. This result is then extended to the case of guaranteeing an H∞ performance. A simulation example is finally given to corroborate the efficacy of the proposed distributed event-triggering scheme in cutting off data transmissions and the data-driven design method. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kuo Guo Yifan Li Hao Chen Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

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Homomorphic encryption-enabled distance-based distributed formation control with distance mismatch estimators

arXiv

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arXiv 2021年

作者： Chaher, Mariano Perez Jayawardhana, Bayu Kim, Junsoo Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen9747AG Netherlands Division of Decision and Control System KTH Royal Institute of Technology Sweden

This paper considers the use of homomorphic encryption for the realisation of distributed formation control of multi-agent systems via edge computer. In our proposed framework, the distributed control computation in the edge computer uses only the encrypted data without the need for a reset mechanism that is commonly required to avoid error accumulation. Simulation results show that, despite the use of encrypted data on the controller and errors introduced by the quantization process prior to the encryption, the formation is able to converge to the desired shape. The proposed architecture offers insight on the mechanism for realising distributed control computation in an edge/cloud computer while preserving the privacy of local information coming from each agent. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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A dual-mode navigation algorithm based on Infrared and SAR information fusion

A dual-mode navigation algorithm based on Infrared and SAR i...

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IEEE International Conference on Radar

作者： Yanjiao Yang Guangzhou Qu Bo Li Meng Ma ShuaiShuai Liu Qingxi Chi Beijing Electro-Mechanical Engineering Institute Beijing China Control and Intelligent Agent Coopration Laboratory Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Beijing China

ISBN: (纸本)9781665468893

Focused on revising the cumulative error of the SINS, a dual-mode navigation algorithm based on the infrared and SAR information fusion is studied in this paper. The SAR system can provide the SAR image and the information including the distance and the beam direction of the SAR antenna, the infrared system can provide the infrared image and the optical axis direction of the infrared lens. Under the premise of the same direction of the SAR and infrared beam, a navigation algorithm by associating the matching results of the SAR image and infrared image with the digital map is studied, which can be applied to amend the aberration of the SINS in the middle phase. At the same time, the common aperture characteristic and GPS error isolation method are used to isolate the error sub area. The simulation results reveal the validity of the algorithm.

关键词： Satellites Spaceborne radar Simulation Optical imaging Radar polarimetry Hardware Optical sensors

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GPIO Based Iterative Learning control for Manipulators with Time-Varying Disturbances

GPIO Based Iterative Learning Control for Manipulators with ...

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Chinese control Conference (CCC)

作者： Huangyuwei Lu Zuo Wang Jiankun Sun Xi Xiao Shihua Li School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex systems of Engineering Ministry of Education Nanjing China Shenzhen Research Institute Southeast University Shenzhen China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing China

To improve the disturbance rejection ability and repetitive tracking accuracy of manipulators, a composite iterative learning control (ILC) scheme via generalized proportional integral observer (GPIO) is proposed. A high-order polynomial is first employed to model the time-varying disturbances that include unmodeled dynamics, parameter uncertainties and load fluctuations. A GPIO is then designed to estimate the disturbances and high-order time derivatives. By introducing estimations into a PD-type ILC at each iteration, a composite control scheme is obtained, such that the undesirable influence of time-varying disturbances can be effectively attenuated in the repetitive tracking process of manipulators. In this paper, the rigorous stability analysis of the closed-loop system is presented to guarantee that the tracking error exponentially converges to zero as the iteration number tends to infinity. The effectiveness of the proposed control scheme is finally verified by simulation results.

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DC Overvoltage Analysis of GFM-controlled Wind Farm Connected via MMC-HVDC system Caused by Receiving-end AC Faults

DC Overvoltage Analysis of GFM-controlled Wind Farm Connecte...

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International Conference on Power Electronics and Motion control (IPEMC)

作者： Siying Chen Min Cheng Yingbiao Li Jiawei Yu Yihua Zhu Mingkang Wu Jiabing Hu Jianan Yan State Key Laboratory of Advanced Electromagnetic Technology and School of Electrical and Electronic Engineering Huazhong University of Science and Technology China Yunnan Power Grid China CSG Key Laboratory for Power System Simulation (Electric Power Research Institute China Southern Power Grid) State Key Laboratory of HVDC (Electric Power Research Institute China Southern Power Grid) National Energy Power Grid Technology R&D Centre Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System China

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

DC overvoltage occurs when short circuit fault exists at the receiving-end and is harmful to the safety of the system where wind farms are connected to the grid through high-voltage direct current (HVDC) transmission based on the modular multilevel converter (MMC). To address this issue, this paper first analyzed the mechanism of the DC overvoltage and innovatively proposed a strategy in which grid-forming-controlled wind farms are integrated into the AC grid through MMC-HVDC, whose sending-end applies grid-following control and constant DC voltage control. However, secondary DC overvoltage appears during the fault recovery stage and is mainly caused by the saturation of the PI controller of constant active power control. Simulations are carried out on a test with MATLAB/Simulink, and results verify the efficacy of the proposed methods in suppressing DC overvoltage that appeared during receiving-end faults and their recovery stage.

关键词： PI control Simulation Power control Wind farms Robustness Wind turbines Safety

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Motion-Guided Spatial Perception for Enhanced Multi-Object Tracking: A Hierarchical Framework

SSRN

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SSRN 2023年

作者： Zheng, Yujin Qi, Hang Li, Lei Li, Shan Huang, Yan He, Chu Wang, Dingwen School of Computer Science Wuhan University Hubei Wuhan430000 China The Beijing Electro-Mechanical Engineering Institute Beijing100074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Zmvision Technology Hubei Wuhan430000 China School of Electronic Information Wuhan University Hubei Wuhan430000 China

Multi-object tracking (MOT) aims to associate objects of interest across sequences, which has rapidly progressed with the development of object detection and optimal matching techniques. However, spatio-temporal modeling complex variations, such as camera motion and occlusion in crowded scenes, have been relatively underexplored in recent years. In this work, a temporal concern through motion-guided feature aggregation module is proposed to capture multi-frame variations for instances. Additionally,an occlusion-aware head and hierarchical spatial association are proposed to handle the challenges of occlusion through spatial perception. The above approach, namely MSPNet addresses the challenges posed by occlusion effectively, resulting in improved tracking performance. Evaluation on benchmark datasets shows that MSPNet achieves the state-of-the-art performance. Specifically, MSPNet achieves 75.4% MOTA, and 72.4% IDF1 on the test set of MOT16, 74.6% MOTA, and 71.9% IDF1 on MOT17, 75.9% MOTA, and 74.4% IDF1 on MOT20, respectively. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Learning Latent Causal Factors from the Intricate Sensor Feedback of Contact-Rich Robotic Assembly Tasks

SSRN

引用

SSRN 2023年

作者： Chen, Yurou Yu, Jiyang Lin, Zhenyang Shen, Liancheng Liu, Zhiyong The State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China

Achieving precise robotic assembly is paramount for safety and performance in industrial settings. Conventional assembly methods require tedious manual adjustment of many parameters, and it is challenging to meet the assembly requirements with tight clearance. Robot learning has been at the forefront of research, showing potential for automation and intelligence in robotic operations. However, intricate information such as force feedback is indispensable for high-precision robot assembly tasks, and current learning methods grapple with processing this complex observation data due to discontinuous force changes during operation and the inherent noise from the force sensor. To alleviate the low learning efficiency from the problematic observations, we propose introducing the concept of latent causal factors that cause the sensor observations and truly matter in assembly tasks. This paper analyses the impact of discontinuous and noisy observations and offers two ways to infer causal factors. Based on the implied latent factors, we propose a method that learns high-precision assembly policies from interacting with the environment. The algorithm's performance is evaluated in simulated and real-world nut assembly environments, demonstrating significant improvements over the previous methods. This research also underscores the promise of causal inference in addressing intricate industrial challenges%. © 2023, The Authors. All rights reserved.

关键词： Robotic assembly

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Phase Unwrapping Based on CFAR Detection for Multi-baseline InISAR

Phase Unwrapping Based on CFAR Detection for Multi-baseline ...

引用

IEEE International Conference on Radar

作者： Meng Ma Guangzhou Qu Shuaishuai Liu Yanjiao Yang Hucheng Pei Qingxi Chi Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781665468893

A phase unwrapping method based on constant false alarm rate (CF AR) detection is proposed for multi-baseline interferometric inverse synthetic aperture radar (InISAR). Aiming at the problem of ambiguity number estimation error under the condition of phase noise, Chinese remainder theorem (CRT) and cluster analysis are utilized to acquire the probability distribution of phase noise. By introducing CFAR detection, the novel method reduced the error probability of ambiguity number estimation. Through simulation, it is verified that the novel method can effectively suppress phase noise and obtain accurate phase unwrapping results.

关键词： Phase noise Phase measurement Radar measurements Radar Radar scattering Radar imaging Cathode ray tubes

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