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arXiv 2023年

作者： Yu, Huan Wang, Pengqin Wang, Jin Ji, Jialin Zheng, Zhi Tu, Jie Lu, Guodong Meng, Jun Zhu, Meixin Shen, Shaojie Gao, Fei The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China The Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China Robotics Institute of Zhejiang University Hangzhou310027 China Hong Kong The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China School of Electrical Engineering Zhejiang University Hangzhou310027 China The Hong Kong University of Science and Technology Guangzhou China

Catching high-speed targets in the flight is a complex and typical highly dynamic task. However, existing methods require manual setting of catching height or time, resulting in lacks of adaptability and flexibility and cannot deal with multiple targets. To bridge this gap, we propose a planning-with-decision scheme called Catch Planner. For sequential decision making, a lightweight policy search method based on deep reinforcement learning is proposed. It is jointly trained with the motion planning and decoupled from physics to speed up training. For motion planning, we propose a trajectory optimization method that jointly optimizes the highly coupled catching time and terminal state. The core is the flexible-terminal constraint transcription. It converts the three unique constraints of catching into differentiable metrics, including equality constraints for terminal position and time, and inequality constraints that enable reasonable terminal position offset and attitude relaxation. In addition, sparse parameterization based on MINCO class considers both dynamic feasibility and collision avoidance constraints. As a result, a generally constrained quadrotor planning problem is transformed into an unconstrained optimization that can be solved reliably and efficiently. We also propose an online iterative optimization method for predicting differentiable trajectories of targets. Catch Planner provides a new paradigm for the combination of learning and planning, where all algorithms can be run in real time onboard at 100hz. Extensive experiments are carried out in real-world and simulated scenes to verify the robustness and expansibility when facing a variety of high-speed flying targets. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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An Integral Sliding-Mode Parallel control Approach for General Nonlinear systems via Piecewise Affine Linear Models

arXiv

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arXiv 2021年

作者： Zhang, Chunyang Gao, Qing Deng, Yue Qiu, Jianbin School of Automation Science and Electrical Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China School of Astraunotics Beihang University Beijing China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

The fundamental problem of stabilizing a general non-affine continuous-time nonlinear system is investigated via piecewise affine linear models (PALMs) in this paper. A novel integral sliding-mode parallel control (ISMPC) approach is developed, where an uncertain piecewise affine system (PWA) is constructed to model a non-affine continuous-time nonlinear system equivalently on a compact region containing the origin. A piecewise sliding-mode parallel controller is designed to globally stabilize the PALM and, consequently, to semi-globally stabilize the original nonlinear system. The proposed scheme enjoys three favorable features: i) some restrictions on the system input channel are eliminated, thus the developed method is more relaxed compared with the published approaches;ii) it is convenient to be used to deal with both matched and unmatched uncertainties of the system;and (iii) the proposed piecewise parallel controller generates smooth control signals even around the boundaries between different subspaces, which makes the developed control strategy more implementable and reliable. Moreover, we provide discussions about the universality analysis of the developed control strategy for two kinds of typical nonlinear systems. Simulation results from two numerical examples further demonstrate the performance of the developed control approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Nonlinear systems

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Adaptive Current Protection Technology for Distribution Network with Distributed Power Sources Based on Local Information

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Mobile Information systems 2021年 2021卷

作者： Cui, Shijie Zeng, Peng Wang, Zhongfeng Zuo, Yue State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China State Grid Huludao Power Supply Company Huludao125000 China

Because the distributed power supply is connected to the distribution network, its structure has also changed the original traditional method. The original single power supply method was originally used. The network provided is a radial network and it is connected to the distributed power supply. Later, it became a structure with small- and medium-sized power supplies inside, and its network power supply became dual or multiple power supplies. Such a structural change affected the coordination of power distribution protection devices, which would impose current on the distribution network. Protection has a negative impact. This paper aims to study the adaptive current protection technology of distribution network with distributed power based on local information. On the basis of analyzing the characteristics of distributed power, the classification of distributed power, and the operation mode of distributed power, according to the access of IIDG, as well as different influences of relay protection of distribution network, an improved protection scheme with adaptive current quick-break protection as the main protection is proposed, so that different fault characteristics of the distribution network have different setting values, and then simulation experiments are carried out on this scheme. The experimental results show that the setting value of three-phase short-circuit fault proposed in this paper can well protect the protection range, which is larger than the traditional protection setting and self-adaptive setting. © 2021 Shijie Cui et al.

关键词： Electric power system protection

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Short-term Load Forecasting of Long-short Term Memory Neural Network Based on Genetic Algorithm

Short-term Load Forecasting of Long-short Term Memory Neural...

引用

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Wanting Li Chuanzhi Zang Ding Liu Peng Zeng Key Laboratory of Networked Control Systems State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728196060

ISBN: (纸本)9781728196077

Accurate load forecasting is conducive to the reasonable arrangement of power grid dispatching plans. Traditional load forecasting methods cannot handle the time series and nonlinear characteristics of load well. Long-short term memory (LSTM) neural networks can record long-term and short-term information, which can effectively solve this kind of problem. But the parameters of LSTM network are difficult to determine. For this reason, this paper proposes a long-short term neural network based on genetic algorithm. The learning rate and iteration number of the LSTM network are used as chromosomes, and the genes are continuously selected, crossed, and mutated to obtain more good genes. Comparing this method with the standard LSTM network, the simulation results show that the LSTM network using genetic algorithm for parameter optimization improves the prediction accuracy of the standard LSTM network by 63%.

关键词： Load forecasting Neural networks Time series analysis System integration Standards Biological cells Genetic algorithms

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Observability Analysis of Graph SLAM-Based Joint Calibration of Multiple Microphone Arrays and Sound Source Localization

arXiv

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arXiv 2022年

作者： He, Yuanzheng Wang, Jiang Su, Daobilige Nakadai, Kazuhiro Wu, Junfeng Huang, Shoudong Li, Youfu Kong, He Shenzhen518055 China The Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities SUSTech Shenzhen518055 China College of Engineering China Agricultural University Beijing China The Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan The School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China The Robotics Institute University of Technology Sydney Sydney Australia The Department of Mechanical Engineering City University of Hong Kong Hong Kong

Multiple microphone arrays have many applications in robot audition, including sound source localization, audio scene perception and analysis, etc. However, accurate calibration of multiple microphone arrays remains a challenge because there are many unknown parameters to be identified, including the Euler angles, geometry, asynchronous factors between the microphone arrays. This paper is concerned with joint calibration of multiple microphone arrays and sound source localization using graph simultaneous localization and mapping (SLAM). By using a Fisher information matrix (FIM) approach, we focus on the observability analysis of the graph SLAM framework for the above-mentioned calibration problem. We thoroughly investigate the identifiability of the unknown parameters, including the Euler angles, geometry, asynchronous effects between the microphone arrays, and the sound source locations. We establish necessary/sufficient conditions under which the FIM and the Jacobian matrix have full column rank, which implies the identifiability of the unknown parameters. These conditions are closely related to the variation in the motion of the sound source and the configuration of microphone arrays, and have intuitive and physical interpretations. We also discover several scenarios where the unknown parameters are not uniquely identifiable. All theoretical findings are demonstrated using simulation data. © 2022, CC BY.

关键词： Microphone array

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A biofuel risk management model for biomass-based heat sources considering supply-demand-side uncertainties

A biofuel risk management model for biomass-based heat sourc...

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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022)

作者： D. Fu T. Zhou T. Yang T. Zhang J. Dong Key Laboratory of Networked Control Systems Institutes for Robotics and Intelligent Manufacturing Digital Factory Department Shenyang Institute of Automation Chinese Academy of Sciences Shenyang People's Republic of China Technical Management Division Guodian Technology & Environment Group Co Ltd. Beijing People's Republic of China Digital Factory Department Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang People's Republic of China

Biofuel-based heat sources are gradually penetrated into heating systems in Northern China in the background of carbon neutrality. It is essential to conduct biofuel risk management study, not only for enhancing the ability of continuous heat supply for heating systems, but also improving the ability of effective cost control. A biofuel risk management model is thus proposed innovatively for biomass-based heat sources considering supply-demand-side uncertainties. The developed model integrates Chance-constrained programming, Two-stage stochastic programming and the traditional linear programming into a general framework. After that, the developed model is applied in to a real heat-source case of biofuel risk management under uncertainties and the corresponding solutions on biofuel risk management are generated. The beneficial results of biofuel allocation pattern can be obtained for decision-makers to provide the fuel management support under different risk levels. Besides, a trade-off analysis between the constraint-violation risk and the system cost can also be provided to conduct in-depth analysis.

关键词：

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Position Sensorless control of Permanent Magnet Synchronous Motor Based on Adaptive Filtering Sliding Mode Observer

Position Sensorless Control of Permanent Magnet Synchronous ...

引用

Chinese control and Decision Conference, CCDC

作者： Anna Wang Yingjie Xiong Tao Zhang College Of Information Science and Engineering Northeastern University Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665478977

In the position sensorless control strategy of permanent magnet synchronous motor (PMSM), the back-EMF method based on sliding mode observer (SMO) is favored because of its robustness. In this paper, a SMO based on the piecewise sigmoid function is designed. Based on the back-EMF observation error, the gain adaptation of the SMO is realized, which effectively reduces the high-frequency chattering problem of the conventional SMO. The low-pass filter of the conventional SMO is improved, and an adaptive filter based on the back-EMF low-pass filter is proposed. According to the back-EMF observation error, the cut-off frequency of the low-pass filter is changed in real-time, and the estimation of rotor position is improved. After Matlab/Simulink simulation verification, the control strategy is feasible and achieves a better control effect.

关键词： Adaptation models Low-pass filters Adaptive filters Rotors Sensorless control Observers Permanent magnet motors

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Research on Distributed Intelligent Protection Strategy of distribution network based on Reinforcement Learning

Research on Distributed Intelligent Protection Strategy of d...

引用

Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Shijie Cui Peng Zeng Ende Hu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China State Grid Shaoxing Power Supply Company Shaoxing China

ISBN: (纸本)9781665428781

In the context of building a new power system with new energy as the main part, the operation mode of distribution network is more complex, and the traditional distribution network protection strategy can hardly meet the demand, so this paper proposes a distributed intelligent protection strategy for distribution network based on reinforcement learning. This paper analyzes the impact of new energy resources access on distribution network relay protection, and presents an intelligent protection algorithm model based on deep reinforcement learning. The structure of intelligent protection device is designed, and the distribution network distributed protection scheme based on intelligent protection device is proposed. Finally, the algorithm training process is given, and the experimental environment is built.

关键词： Training Uncertainty Power supplies Protective relaying Distribution networks Reinforcement learning Power system stability

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ODYSSEE: Oyster Detection Yielded by Sensor systems on Edge Electronics

arXiv

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arXiv 2024年

作者： Lin, Xiaomin Mange, Vivek Suresh, Arjun Neuberger, Bernhard Palnitkar, Aadi Campbell, Brendan Williams, Alan Baxevani, Kleio Mallette, Jeremy Vera, Alhim Vincze, Markus Rekleitis, Ioannis Tanner, Herbert G. Aloimonos, Yiannis Maryland Robotics Center University of Maryland College ParkMD20742 United States Center for Autonomous and Robotic Systems University of Delaware NewarkDE19711 United States Automation and Control Institute TU Wien Vienna1040 Austria School of Marine Science and Policy University of Delaware LewesDE19958 United States University of Maryland Center for Environmental Science Horn Point Laboratory CambridgeMD21613 United States College of Engineering and Applied Science University of Cincinnati OH45221 United States Montreal QC Canada

Oysters are a vital keystone species in coastal ecosystems, providing significant economic, environmental, and cultural benefits. As the importance of oysters grows, so does the relevance of autonomous systems for their detection and monitoring. However, current monitoring strategies often rely on destructive methods. While manual identification of oysters from video footage is non-destructive, it is time-consuming, requires expert input, and is further complicated by the challenges of the underwater environment. To address these challenges, we propose a novel pipeline using stable diffusion to augment a collected real dataset with photorealistic synthetic data. This method enhances the dataset used to train a YOLOv10-based vision model. The model is then deployed and tested on an edge platform;Aqua2, an Autonomous Underwater Vehicle (AUV), achieving a state-of-the-art 0.657 mAP@50 for oyster detection. Copyright © 2024, The Authors. All rights reserved.

关键词： Shellfish

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Cancer Breakpoint Hotspots Versus Individual Breakpoints Prediction by Machine Learning Models 1

引用

16th International Symposium on Bioinformatics Research and Applications, ISBRA 2020

作者： Cheloshkina, Kseniia Bzhikhatlov, Islam Poptsova, Maria Laboratory of Bioinformatics Faculty of Computer Science National Research University Higher School of Economics 11 Pokrovsky boulvar Moscow101000 Russia Faculty of Control Systems and Robotics ITMO University 49 Kronverksky Pr. St. Petersburg197101 Russia

ISBN: (数字)9783030578213

ISBN: (纸本)9783030578206

Genome rearrangement is a hallmark of all cancers. Cancer breakpoint prediction appeared to be a difficult task, and various machine learning models did not achieve high prediction power. We investigated the power of machine learning models to predict breakpoint hotspots selected with different density thresholds and also compared prediction of hotspots versus individual breakpoints. We found that hotspots are considerably better predicted than individual breakpoints. While choosing a selection criterion, the test ROC AUC only is not enough to choose the best model, the lift of recall and lift of precision should be taken into consideration. Investigation of the lift of recall and lift of precision showed that it is impossible to select one criterion of hotspot selection for all cancer types but there are three to four distinct groups of cancer with similar properties. Overall the presented results point to the necessity to choose different hotspots selection criteria for different types of cancer. © 2020, Springer Nature Switzerland AG.

关键词： Machine learning

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