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第36届中国控制会议

作者： Hongbing Xia Bo Zhao Yuanchun Li Department of Control Science and Engineering Changchun University of Technology The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper tackles the optimal tracking control problem for reconfigurable manipulators based on critic-only policy iteration（Co PI） algorithm. By system transformation, the optimal tracking control problem is transformed into an optimal regulation problem. The optimal tracking controller is composed of the desired controller and the approximate optimal feedback one. The desired controller is developed to maintain the desired tracking performance at the steady-state, while the approximate optimal feedback controller is designed to stabilize the tracking error dynamics in an optimal manner. Then, a critic neural network is used to estimate the optimal performance index function, and the optimal feedback control is obtained by the Co PI algorithm. The convergence of the proposed method is analyzed and it is shown that the closed-loop system based on Co PI is uniformly ultimately bounded by using the Lyapunov approach. Finally, simulation studies are given to show the effectiveness of the developed method.

关键词： Adaptive dynamic programming Reconfigurable manipulators Optimal tracking control Policy iteration Neural networks

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Decentralized Adaptive Super-Twisting control for Modular and Reconfigurable Robots with Uncertain Environment Contact 36

Decentralized Adaptive Super-Twisting Control for Modular an...

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第36届中国控制会议

作者： Bo Dong Yan Li Keping Liu Yuanchun Li Department of Control Science and Engineering Changchun University of Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper presents a decentralized adaptive super-twisting control method for modular and reconflgurable robots（MRRs） with uncertain environment *** conventional methods that rely on robot-environment contact model or force/torque sensing,this paper addresses the problem of controlling MRRs in contact with uncertain environment that using only local dynamic information of each joint *** dynamic model of MRR is formulated as a synthesis of interconnected *** on the integral sliding mode control（ISMC） technique and the adaptive super-twisting algorithm（ASTA）,the decentralized controller is designed to compensate the model uncertainty in which the up-bound is *** stability of the MRR system is proved by using the Lyapunov *** last,simulations are conducted for 2-DOF MRRs with different configurations under the situations of dynamic contact and collision to investigate the advantage of the proposed approach.

关键词： Modular and reconfigurable robot Decentralized control strategy Uncertain environment contact Adaptive supertwisting algorithm Interconnected couplings

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Decentralized optimal control for modular and reconfigurable robots based on adaptive dynamic programming 36

Decentralized optimal control for modular and reconfigurable...

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第36届中国控制会议

作者： Zixu Wang Bo Dong Hongbing Xia Yuanchun Li Department of Control Science and Engineering Changchun University of Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper presents a decentralized optimal control method for modular and reconflgurable robots（MRRs） based on adaptive dynamic ***,the dynamic model of MRRs is formulated by using the Newton-Euler iterative algorithm,and then the state space description is ***,the optimal control policy of the MRRs system is obtained based on the policy iteration algorithm,which is used to solve the Hamilton-Jacobi-Bellman（HJB） equation via the critic neural ***,the stability of the closed-loop system is proved by using the Lyapunov ***,simulations are conducted to illustrate the effectiveness for the 2-DOF MRRs.

关键词： Modular and reconfigurable robot Decentralized control strategy Adaptive dynamic programming Neural network

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Investigating the dynamic memory effect of human drivers via ON-LSTM

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Science China(Information Sciences) 2020年第9期63卷 83-93页

作者： Shengzhe DAI Zhiheng LI Li LI Dongpu CAO Xingyuan DAI Yilun LIN Department of Automation Tsinghua University Shenzhen International Graduate School Tsinghua University Department of Mechanical and Mechatronics Engineering University of Waterloo State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

It is a widely accepted view that considering the memory effects of historical information(driving operations) is beneficial for vehicle trajectory prediction models to improve prediction accuracy. However,many commonly used models(e.g., long short-term memory, LSTM) can only implicitly simulate memory effects, but lack effective mechanisms to capture memory effects from sequence data and estimate their effective time range(ETR). This shortage makes it hard to dynamically configure the most suitable length of used historical information according to the current driving behavior, which harms the good understanding of vehicle motion. To address this problem, we propose a modified trajectory prediction model based on ordered neuron LSTM(ON-LSTM). We demonstrate the feasibility of ETR estimation based on ON-LSTM and propose an ETR estimation method. We estimate the ETR of driving fluctuations and lane change operations on the NGSIM I-80 dataset. The experiment results prove that the proposed method can well capture the memory effects during trajectory prediction. Moreover, the estimated ETR values are in agreement with our intuitions.

关键词： driving behavior memory effect trajectory prediction historical information ON-LSTM

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A Spatial-Temporal Attention Model for Human Trajectory Prediction

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 965-974页

作者： Xiaodong Zhao Yaran Chen Jin Guo Dongbin Zhao the School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083 the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing 100083China

Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

关键词： Attention mechanism long-short term memory(LSTM) spatial-temporal model trajectory prediction

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Accurate and Robust Eye Center Localization via Fully Convolutional Networks

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IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1127-1138页

作者： Yifan Xia Hui Yu Fei-Yue Wang the School of Creative Technologies University of PortsmouthPortsmouthPO1 2DJUK the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Eye center localization is one of the most crucial and basic requirements for some human-computer interaction applications such as eye gaze estimation and eye tracking. There is a large body of works on this topic in recent years, but the accuracy still needs to be improved due to challenges in appearance such as the high variability of shapes, lighting conditions, viewing angles and possible occlusions. To address these problems and limitations, we propose a novel approach in this paper for the eye center localization with a fully convolutional network(FCN),which is an end-to-end and pixels-to-pixels network and can locate the eye center accurately. The key idea is to apply the FCN from the object semantic segmentation task to the eye center localization task since the problem of eye center localization can be regarded as a special semantic segmentation problem. We adapt contemporary FCN into a shallow structure with a large kernel convolutional block and transfer their performance from semantic segmentation to the eye center localization task by *** experiments show that the proposed method outperforms the state-of-the-art methods in both accuracy and reliability of eye center localization. The proposed method has achieved a large performance improvement on the most challenging database and it thus provides a promising solution to some challenging applications.

关键词： Deep learning eye center localization eye gaze estimation eye tracking fully convolutional network (FCN) humancomputer interaction

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Design and 3D Motion Modeling of a 300-m Gliding Robotic Dolphin

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IFAC-PapersOnLine 2017年第1期50卷 12685-12690页

作者： Yuan, Jun Wu, Zhengxing Yu, Junzhi Zhou, Chao Tan, Min University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

This paper presents the mechatronic design and three-dimensional (3D) motion modeling of a gliding robotic dolphin capable of diving as deep as 300 m. Propulsion modes of bio-inspired robotic dolphin and traditional underwater gliders are integrated into the robot in order to endow it with both high maneuverability and long endurance simultaneously. It is realized by introducing a buoyancy adjusting mechanism on the basis of a bio-inspired robotic dolphin. Particular design on mechanical seal and compressive strength is implemented to achieve large diving depth. Further, a full-state dynamic model for 3D gliding motion is established for motion analysis. In comparison with traditional underwater gliders, the robot's flippers and fluke are controllable and thereby enrich the state-regulating modes of gliding motion, which is considered specially in the dynamic model. Simulations are conducted to analyze the robot's 3D gliding motion. Experiments are also carried out to validate the robot's gliding performance and the effectiveness of the formulated dynamic model. © 2017

关键词： 3D modeling Biomimetics Compressive strength Dolphins (structures) Dynamic models Dynamics Machine design Motion control Robotics Robots Adjusting mechanism Bio inspired robotics Mechatronic design Robotic dolphin state regulating Three dimensional (3D) motion Underwater gliders Underwater robotics

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Real-time manufacturing integration and intelligence solution: Case study in global chemical company

Journal of Software

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Journal of Software 2012年第8期7卷 1727-1737页

作者： Xiong, Gang Dong, XiSong Liu, XiWei Nyberg, Timo R. State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China BIT Research Centre Aalto University Aalto Finland

Global manufacturing enterprises meet a lot of challenges on enterprise level, plant level and shop floor level. To solve those challenges, a kind of real- time Manufacturing Integration and Intelligence (Mil) solution is proposed, and its standard interfaces with ERP modules and shop floor systems, standard integration functionalities, and typical use cases and typical benefits, are described. By using of the practical project executed in one global chemical company as background, the paper introduces the project's "AS IS" situation, and "TO BE" target. Then, the Mil solution is discussed in more detail, including its business scope, customer requirements, server selection, high availability solution, core technologies, and its main functions: load;unload;transfer;special instruction;inventory;blending;KPI dashboard. The tactical benefits, which have been made for the customer with the Mil solution, are summarized. Finally, some conclusions are drawn out. © 2012 ACADEMY PUBLISHER.

关键词： Blending

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A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation

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Machine Intelligence Research 2022年第4期19卷 331-346页

作者： Song Zhang Nan Zheng Dan-Li Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Due to the popularity of group activities in social media,group recommendation becomes increasingly *** aims to pursue a list of preferred items for a target *** deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and ***,these methods may suffer from data sparsity *** for the interaction between groups and users,there also exist other interactions that may enrich group representation,such as the interaction between groups and *** interactions,which take place in the range of a group,form a local view of a certain *** addition to local information,groups with common interests may also show similar tastes on ***,group representation can be conducted according to the similarity among groups,which forms a global view of a certain *** this paper,we propose a novel global and local information fusion neural network(GLIF)model for group *** GLIF,an attentive neural network(ANN)activates rich interactions among groups,users and items with respect to forming a group′s local ***,our model also leverages ANN to obtain a group′s global representation based on the similarity among different ***,it fuses global and local representations based on attention mechanism to form a group′s comprehensive ***,group recommendation is conducted under neural collaborative filtering(NCF)*** experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

关键词： Group recommendation attentive neural network(ANN) global information local information recommender system

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Convection-UNet: A Deep Convolutional Neural Network for Convection Detection based on the Geo High-speed Imager of Fengyun-4B

Convection-UNet: A Deep Convolutional Neural Network for Con...

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2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms, PRMVIA 2023

作者： Wang, Yufei Xiao, Baihua Chinese Academy of Sciences University of Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9798350346596

Deep convection can cause a variety of severe weather conditions such as thunderstorms, strong winds, and heavy rainfall. Satellite observations provide all-weather and multi-directional observations, facilitating the timely detection of such weather systems, which is crucial to saving lives and property. However, previous methods based on channel feature extraction and threshold filtering did not make full use of information in satellite images, which led to limitations on such complex problems as strong convection detection. In this study, we propose a novel framework of a deep learning-based model Convection-UNet to detect convection. We use channel 4 to 7 of FY-4B GHI that we select according to the microphysical properties of convection as input and radar reflectivity as label. We combine the detailed training time and test time data augmentation strategies and build a deep neural network to automatically extract spatial context features and achieve end-to-end learning. Results show that the performance of our method far exceeds the previous channel extraction combined with threshold filtering methods such as BT and BTD at least 0.24 on Fi-measure. We also show that our channel selection and data augmentation strategies are of great significance to detect convection. © 2023 IEEE.

关键词： Feature extraction

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