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arXiv 2024年

作者： Kumar, Lalitesh Chen, Jian Wu, Chengshuai Chen, Yuzhu van der Schaft, Arjan The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China The State Key Laboratory of Fluid Power and Mechatronic Systems College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Automation Science and Engineering Xi'an Jiaotong University Xian710049 China The Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen9747 AG Netherlands

This paper proposes an extended interconnection and damping assignment passivity-based control technique (IDA-PBC) to control the pressure dynamics in the fuel delivery subsystem (FDS) of proton exchange membrane fuel cells. The fuel cell stack is a distributed parameter model which can be modeled by partial differential equations (PDEs). In this paper, the segmentation concept is used to approximate the PDEs model by ordinary differential equations (ODEs) model. Therefore, each segments are having multiple ODEs to obtain the lump-sum model of the segments. Subsequently, a generalized multi-input multi-output lumped parameters model is developed in port-Hamiltonian framework based on mass balance to minimize the modeling error. The modeling errors arises due to the difference between spatially distributed pressures in FDS segments, and also due to the difference between the actual stack pressure and the measured output pressure of the anode. The segments interconnection feasibilities are ensured by maintaining passivity of each segment. With consideration of re-circulation and bleeding of the anode in the modeling, an extended energy-shaping and output tracking IDA-PBC based state-feedback controller is proposed to control the spatially distributed pressure dynamics in the anode. Furthermore, a sliding mode observer of high order is designed to estimate the unmeasurable pressures in FDS with known disturbances. Performance recovery of output feedback control is accomplished with explicit stability analysis. The effectiveness of the proposed IDA-PBC approach is validated by the simulation results. © 2024, CC BY.

关键词： Anodes

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Robust control of Feeding Speed for Coal Mine Tunnel Drilling Machines

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Xiao Liu Luefeng Chen Min Wu Weihua Cao Chengda Lu Witold Pedrycz School of Automation China University of Geosciences Wuhan China School of Automation the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed.

关键词： Drilling Robust control Uncertainty Robust stability Simulation Coal Resonance

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Stick-Slip Vibration Suppression Based on Multi-modal Analysis and Equivalent - Input-Disturbance Method

Stick-Slip Vibration Suppression Based on Multi-modal Analys...

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Asian control Conference

作者： Lingfeng Mu Hengyu Huang Haitao Song Zhejiaqi Ma Chengda Lu Min Wu School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Automation China University of Geosciences Wuhan China CCTEG Xi 'an Research Institute (Group) Co. Ltd. Xi'an China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This work investigates the active suppression of stick-slip vibration based on the multi-modal analysis and equivalent-input-disturbance (EID) method. By building a lumped parameter model with a high degree of freedom, the multi-modal properties of the vibration are examined. The multi-modal disturbance signals of the drilling string system are analyzed using the variational modal decomposition method. The EID method is used to design a vibration suppression controller. The parameters in the EID method are adjusted based on the primary modal frequencies to obtain the equivalent disturbance at the appropriate frequency. According to the simulation results, the proposed approach improves the effectiveness of active vibration suppression of the drill string, which is of some significance in guiding the drilling operation.

关键词： Vibrations Drilling Accuracy Perturbation methods Modal analysis Simulation Buildings

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Efficient Multi-Robot Coverage of an Unknown Environment

Efficient Multi-Robot Coverage of an Unknown Environment

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第40届中国控制会议

作者： Zihao Chen Zhihong Peng Lei Jiao Yuanyuan Gui School of Automation Beijing Institute of Technology China State Key Laboratory of Intelligent Control and Decision of Complex System

This paper discusses the complete area coverage of multi-robots in unknown environments. Finding the optimal solution for the problem of area coverage with multiple robots has been proven to be NP-hard. This paper proposes the Multiagent Node Backtracking Area Coverage（MANBAC） algorithm, which uses improved BCD local area coverage method, and the node backtracking method that is based on the A* algorithm to achieve complete coverage of the entire unknown *** with the area coverage of a single robot, use multi robots can greatly improve the efficiency of area *** with the existing coverage algorithms in known area, this study can be applied to a wider range of area scenarios which are not known.

关键词： Area coverage Unknow environment Multirobot

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A History Information-Based A* Algorithm for the Path Planning with Multiple Alternative Destinations

A History Information-Based A* Algorithm for the Path Planni...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Ke Wang Wei Liang Huaguang Shi Jialin Zhang Qi Wang 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 School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

As a cornerstone technology in automation, path planning plays a crucial role across various domains, including logistics, autonomous vehicles, and robotics. A novel path planning challenge, focusing on routes with multiple alternative destinations, has emerged as a focal point in industrial settings. Traditional approaches such as breadth-first and heuristic searches lack specific optimizations for this challenge, resulting in suboptimal efficiency. This paper introduces a new approach to the path planning problem with multiple alternative destinations that utilizes historical data. It features three heuristic strategies: leveraging historical information for search acceleration, pruning based on the length of the current shortest path, and searching in ascending order of the Manhattan distance. These strategies enhance the search efficiency by leveraging historical information for faster searches and pruning based on real-time search outcomes. Simulation results show that the proposed algorithm outperforms the existing works in terms of the computational cost.

关键词： Service robots Navigation Simulation Focusing Path planning Real-time systems Computational efficiency

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Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning

arXiv

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arXiv 2022年

作者： Zhang, Lu Qi, Lu Yang, Xu Qiao, Hong Yang, Ming-Hsuan Liu, Zhiyong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China The School of Artificial Intelligence University of Chinese Academy of Sciences China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China University of California Merced United States

This paper tackles the problem of novel category discovery (NCD), which aims to discriminate unknown categories in large-scale image collections. The NCD task is challenging due to the closeness to the real world scenarios, where we have only encountered some partial classes and images. Unlike other works on the NCD, we leverage the prototypes to emphasize the importance of category discrimination and alleviate the issue with missing annotations of novel classes. Concretely, we propose a novel adaptive prototype learning method consisting of two main stages: prototypical representation learning and prototypical self-training. In the first stage, we obtain a robust feature extractor, which could serve for all images with base and novel categories. This ability of instance and category discrimination of the feature extractor is boosted by self-supervised learning and adaptive prototypes. In the second stage, we utilize the prototypes again to rectify offline pseudo labels and train a final parametric classifier for category clustering. We conduct extensive experiments on four benchmark datasets, and demonstrate the effectiveness and robustness of the proposed method with the state-of-the-art performance. The source code and trained models will be made available at this github site. Copyright © 2022, The Authors. All rights reserved.

关键词： Image recognition

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Resource Scheduling Base on Bayesian Cramer-Rao Lower Bound for Multi-Target Tracking in Netted Colocated MIMO Radar systems

Resource Scheduling Base on Bayesian Cramer-Rao Lower Bound ...

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第40届中国控制会议

作者： Sijian Liao Zhihong Peng Junqi Cai School of Automation Beijing Institute of Technology China State Key Laboratory of Intelligent Control and Decision of Complex System

In this paper, an effective joint beam selection and power allocation（JBSPA） method is proposed to solve the online resource scheduling problem for multi-target tracking in distributed MIMO radar system. Overall target tracking performance can be improved by optimizing radar system beam selection and power allocation, since each MIMO radar can generate a timevarying beam, and adjust the quality of the observed signal by controlling the transmit power of each beam. In our method the feedback information of the target tracking period is used to complete the optimal radar-targets assignment and power allocation,the objective function is formulated based on the Bayesian Cramér-Rao lower bound, as the formulated JBSPA problem is nonconvex, a three steps solution method based on reward matrix is proposed to solve it effectively. Numerical results show that the proposed JBSPA method can deliver better performance than the competitors in overall MTT performance.

关键词： Resource Scheduling BCRLB Target Tracking Power Allocation MIMO Radar systems

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

SSRN

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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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Blockchain-based Dependable Task Offloading and Resource Allocation for IIoT via Multi-Agent Deep Reinforcement Learning

Blockchain-based Dependable Task Offloading and Resource All...

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IEEE Conference on Vehicular Technology (VTC)

作者： Peifeng Zhang Chi Xu Changqing Xia Xi Jin 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

Task offloading and resource allocation are fundamental and crucial for the edge computing-enhanced industrial Internet of things, where the security and credibility among massive heterogeneous devices are being challenged. This paper first proposes a novel blockchain consensus scheme named replicated and Byzantine fault tolerant, which can enhance the trust among nodes with the low communication cost. Then, with the objective of minimizing the task completion time, which includes credible verification, task offloading and transaction record, a joint task offloading and resource allocation problem with respect to blockchain verification ratio, offloading decision, communication and computing resources is formulated. Due to its non-convexity and the decentralized characteristic of blockchain, a multi-agent deep reinforcement learning algorithm with deterministic policy gradient is proposed to appropriate the optimal solution. Experiment results confirm the effectiveness of the proposed scheme in guaranteeing the timeliness and security.

关键词：

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Existence of a Heterogeneous Pathway in Palladium-Catalyzed Carbon–Carbon Coupling Reaction:Evidence from Ag@Pd_(3)Cu Intermetallic Nanoplates

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CCS Chemistry 2022年第2期4卷 671-682页

作者： Rui Liu Cuihong Xu Yanan Chen Huachao Zhao Jiefang Sun Zuoliang He Wanyu Shan Gang Li Qiantao Shi Liping Fang Jingfu Liu Guibin Jiang State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085 School of Environment Hangzhou Institute of Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024 Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and Management Institute of Eco-Environmental and Soil SciencesGuangdong Academy of SciencesGuangzhou 510650 College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049 Beijing Center for Disease Prevention and Control Beijing 100013 Center for Environmental Systems Stevens Institute of TechnologyHobokenNJ 07030

Palladium(Pd)-catalyzed cross-coupling reaction is a widely studied process with significant economic interest and represents one of the most successful nanocatalytic *** to the observed leaching of Pd ions from both monometallic and short-range-ordered Pd bimetallic nanocatalysts,there is ambiguity regarding whether the coupling reaction has a heterogeneous ***,facilitated by theoretical calculations,we discovered that the Pd ion leaching-free Pd_(3)Cu intermetallic compound was able to catalyze the cross-coupling reaction heterogeneously with activity comparable with that of Pd ions leached from monometallic *** observation that Pd_(3)Cu acted as a heterogeneous catalyst was further confirmed by the reaction-promoting effect of hot electrons generated by the Ag core in Ag@Pd_(3)Cu nanoplates upon photon adsorption and could only participate in the reaction on the surface of the ***,it demonstrated experimentally the effectiveness of the heterogeneous route in the coupling *** study shows that the combination of plasmonic catalysis and intermetallic compounds is a promising strategy for designing robust nanocatalysts to synthesize value-added chemicals.

关键词： plasmonic catalysis intermetallics carbon-carbon coupling reaction mechanism heterogeneous pathway

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