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第40届中国控制会议

作者： Yaming Ou Zhuoliang Zhang Chao Zhou Bo Zhou School Of Automation Southeast University State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

All the time,the measurement of swimming speed has long been a major research challenge in the field of robotic *** artificial lateral line system can accurately measure the speed without modifying the structure of the robot fish too *** this paper,we developed a cantilever artificial lateral line（ALL） sensor based on piezoresistive effect by imitating the "SNs" cell structure of real fish,and established an accurate physical model for it considering the complex underwater boundary layer effect,which is conducive to the selection of hardware parameters such as sensor size and *** addition,a flowmeter-based speed calibration experimental platform was developed,based on which we proposed an "end to end" speed calibration algorithm based LSTM without *** a potential mapping relationship between the electrical signal of the sensor and the speed value of the flowmeter was obtained,which laid a strong foundation for the subsequent measurement of free-swimming speed of robotic fish by artificial lateral line system composed of this sensor.

关键词： velocity measurement robotic fish artificial lateral line sensor data processing

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Learning Greenhouse Climate control Policy from Monitored Data

Learning Greenhouse Climate Control Policy from Monitored Da...

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2022 Chinese Automation Congress, CAC 2022

作者： Zhao, Xiaoxuan Wang, Haoyu Wang, Xiujuan Lewlomphaisarl, Udom Li, Dong Hua, Jing Kang, Mengzhen The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technologies Beijing China National Electronics and Computer Technology Center Pathum Thani Thailand Institute of Digital Agriculture Zhejiang Academy of Agricultural Sciences Hangzhou China Qingdao AgriTech Co. Ltd. Qingdao China

ISBN: (纸本)9781665465335

The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar configuration. © 2022 IEEE.

关键词： Greenhouses

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Research on Attention-Based Predictive Model in Multi-Agent Reinforcement Learning

Research on Attention-Based Predictive Model in Multi-Agent ...

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2022 Chinese Automation Congress, CAC 2022

作者： Hong, Weixin Wu, Huaiyu Shen, Zhen Xiong, Gang Hu, Bin Dong, Xisong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China

ISBN: (纸本)9781665465335

Agent actions planning is a challenging problem in multi-agent reinforcement learning. Recent methods typically build their predictive models by full connection layers, but the shortage of utilizing action and observation information in complex multi-agent environment limits the performance of those methods. To address this issue, we propose a novel predictive model based on self-attention mechanism which can effectively use information in the environment. Specifically, we use the MADDPG algorithm to make a dataset where the input is the observations and actions and the output is the predicted observations and rewards. We also introduce the multi-head attention mechanism into the predictive model so that the predictive model can better combine different information for prediction. We perform extensive experiments to assess the proposed method and compare it with the representative baseline approach. Results demonstrate that our method outperforms competitors with higher accuracy in different tasks. Our implementation will be publicly available. © 2022 IEEE.

关键词： Reinforcement learning

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Hot Spot Detection of Photovoltaic Module Infrared Near-field Image based on Convolutional Neural Network 4

Hot Spot Detection of Photovoltaic Module Infrared Near-fiel...

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2022 4th International Conference on Energy systems and Electrical Power, ICESEP 2022

作者： Fan, Fu Na, ZhiXiong Zhang, ChangZhi Li, HaoRan Tong, ChaoLiu School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China State Grid e-commerce Co. Ltd Beijing100053 China State Grid Tianjin Electric Power Company Tianjin300010 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

With the installation and use of large-scale photovoltaic systems around the world, the detection of photovoltaic system operation and maintenance has become increasingly important. This research uses a convolutional neural network training model to detect and classify the infrared near-field images of photovoltaic modules from small-scale photovoltaic plants in the laboratory. This model classifies the images into two categories: with and without hot spots, with a classification accuracy of 96.58%. The experimental results show that the convolutional neural network training model has a good classification result. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Convolutional neural networks

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Asynchronous $\mathcal{L}_{1}$-gain Filtering of Nonlinear Positive Semi-Markov Jump systems with Time-varying Delays

Asynchronous $\mathcal{L}_{1}$-gain Filtering of Nonlinear P...

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Chinese control Conference (CCC)

作者： Chao Ma Hang Fu Yidao Ji Wei Wu School of Mechanical Engineering University of Science and Technology Beijing China State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

In this paper, the $\mathcal{L}_{1}$ -gain based filtering problem for nonlinear positive semi-Markov jump systems is investigated by proposing a novel asynchronous design approach. More precisely, the mode-dependent... 详细信息

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper, the $\mathcal{L}_control$ -gain based filtering problem for nonlinear positive semi-Markov jump systems is investigated by proposing a novel asynchronous design approach. More precisely, the mode-dependent filters are designed in terms of practical observed modes instead of true system modes, such that less conservatism can be achieved. In addition, the effect of time-varying delays is taken into account for more robustness and applicability. By selecting suitable stochastic Lyapunov-Krasovskii functions and applying the linear programming method, sufficient conditions are established to fulfill the desired $\mathcal{L}_control$ -gain performance. Eventually, the illustrative simulation is performed to verify the effectiveness of our developed control scheme.

关键词： Maximum likelihood detection Sufficient conditions Filtering Hidden Markov models Stochastic processes Nonlinear filters Linear programming

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A Novel Coiled Cable-Conduit-Driven Hyper-Redundant Manipulator for Remote Operating in Narrow Spaces

A Novel Coiled Cable-Conduit-Driven Hyper-Redundant Manipula...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Mingrui Luo Yunong Tian En Li Minghao Chen Cunfeng Kang Guodong Yang Min Tan School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Laboratory of Industrial Vision and Intelligent Equipment Technology Chinese Academy of Sciences Beijing China Beijing-University of Technology Beijing China

Operating in narrow spaces is an important challenge in the development of robots. Redundant manipulators are one way to solve this problem, but their mechanism design and control method still have much room for improvement. In this paper, we propose a coiled cable-conduit-driven hyper-redundant manipulator (C-CDHRM) with great slenderness and flexibility. In terms of mechanism design, it considers both compactness and operability. By imitating the structure and behavior of a constricting snake, it can be uncoiled sequentially from a coiled storage state, led by the head. In terms of control methods, we propose a multi-layer control system that can make remote operations more accurate and reliable. On the one hand, guiding, segmenting, and following the path overcome the planning ambiguity caused by redundancy. On the other hand, conduit transmission modeling and cable length correction overcome the nonlinear mapping of cable-driven joints and were verified in experiments. Through tests, the mobile integrated system composed of C-CDHRM has an excellent performance in operation precision and accuracy, ensuring safety and accessibility in narrow spaces. Finally, in field experiments, the inspection and cleaning of various types of electrical equipment have been successfully completed, showing excellent application prospects.

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Temporal-difference spatial sampling and aggregating graph neural network for crowd flow forecasting 1

Temporal-difference spatial sampling and aggregating graph n...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Bu, Xiaojie Wei, Zebing Li, Zhishuai Wang, Xiao Chen, Yuanyuan Song, Yunquan Lv, Yisheng College of Science China University of Petroleum Qingdao China Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Institute of Automation Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China Qingdao Academy of Intelligent Industries Qingdao China College of Science China University of Petroleum Qingdao China Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665433372

With the development of traffic demand management, crowd flow forecasting arouse increasing interest. In order to tackle this spatial-temporal prediction problem, GNN (Graph Neural Networks) have been recently employed to model spatial dependencies, and usually spatial graphs are constructed based on geodetic distances or direct connections between nodes. However, it might be insufficient for spatial graphs to model real dependencies because they neglect spatial-temporal correlations. In this paper, a novel Temporal-Difference Spatial Sampling and Aggregating graph neural network (TDSSA) is proposed to model spatial-temporal dependencies. Firstly, a new sub-sparse spatial-temporal graph is constructed to represent spatial-temporal relationships among different nodes, then a TDSSA block is designed to extract features by spatial sampling and aggregating, and difference is utilized in TDSSA blocks to exploit temporal trend and increase robustness. Experiments show that the proposed method outperforms baseline methods. © 2021 IEEE.

关键词： Graph neural networks

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Cooperative Filtering with Range Measurements: A Distributed Constrained Zonotopic Method

arXiv

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arXiv 2023年

作者： Ding, Yu Cong, Yirui Wang, Xiangke Cheng, Long College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

This article studies the distributed estimation problem of a multi-agent system with bounded absolute and relative range measurements. Parts of the agents are with high-accuracy absolute measurements, which are considered as anchors;the other agents utilize low-accuracy absolute and relative range measurements, each derives an uncertain range that contains its true state in a distributed manner. Different from previous studies, we design a distributed algorithm to handle the range measurements based on extended constrained zonotopes, which has low computational complexity and high precision. With our proposed algorithm, agents can derive their uncertain range sequentially along the chain topology, such that agents with low-accuracy sensors can benefit from the high-accuracy absolute measurements of anchors and improve the estimation performance. Simulation results corroborate the effectiveness of our proposed algorithm and verify our method can significantly improve the estimation accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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Short-term PV power prediction model based on weather feature clustering and Adaboost-GA-BP

Short-term PV power prediction model based on weather featur...

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第35届中国控制与决策会议

作者： Yujun Liu Xinrui He Shutong Duan Bo Wang Hongqing Wang College of Science Beijing Forestry University School of Economics & Management Beijing Forestry University State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute

Based on the weather feature clustering,an optimized algorithm called Adaboost-GA-BP is presented for solving the problem that BP neural network is easy to fall into local extremum and over-fitting in PV power short-term ***,the K-means algorithm is applied to cluster the historical data into sunny,cloudy and overcast&rainy *** is then the GA that optimize initial weights,forming the GA-BP neural *** idea of Adaboost algorithm is applied to train GA-BP as different weak predictors so that the Adaboost-GA-BP strong predictor is ***,this paper provides a detailed performances comparison of various *** AdaboostGA-BP predicts a 4.4% decrease in MAE and a 4.1% decrease in RMSE for the sunny test set,a 9.0% decrease in MAE and a13.5% decrease in RMSE for the cloudy test set,and a 2.7%decrease in MAE and a 9.1% decrease in RMSE for the rainy test set,outperforming the BP model,the GA-BP model,and the Adaboost-BP *** comparative analysis,the model has better prediction accuracy and generalization ability especially for predicting cloudy weather.

关键词： photovoltaic power short-term forecast backpropagation neural network genetic algorithm Adaboost algorithm weather clustering

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Parallel governance for decentralized autonomous organizations enabled by blockchain and smart contracts 1

Parallel governance for decentralized autonomous organizatio...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Ding, Wen Wen Liang, Xiaolong Hou, Jiachen Wang, Ge Yuan, Yong Li, Junqing Wang, Fei-Yue Institute of Systems Engineering Macau University of Science and Technology China School of Mathematics Renmin University of China Beijing China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

Decentralized autonomous organizations(DAOs) enabled by blockchain and smart contracts is regarded as an effective tool to solve corporate governance problems. It can minimize the contract risks, principal-agent dilemmas, and other issues in corporate governance. However, DAOs also face a variety of governance issues, such as internal governance issues and external governance issues caused by the technical attributes of blockchain and smart contracts. DAO is a typical complex system with the characteristics of both social and engineering complexity. It is difficult for existing governance theories to solve governance problems in DAOs. The parallel intelligence theory based on the artificial systems + computational experiments + parallel execution (ACP) method is a suitable research and practical approach to solve management and control problems for complex systems. Therefore, we propose a parallel governance framework for DAO based on the parallel intelligence theory and discuss its key research issues. Finally, we construct a parallel governance system for GnosisDAO, and conduct computational experiments on its voting mechanism. The results show the defects of GnosisDAO governance mechanism and we thus propose further research directions. We consider that parallel governance represents the future trend of DAO research and practice. This paper is aimed at stimulating further effort and providing helpful guidance and reference for future research works. © 2021 IEEE.

关键词： Smart contract

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