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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Liming Zhang Zhimin Han Bo Wang Zhiyun Lin Xiangdong Xu School of Automation Hangzhou Dianzi University Hangzhou P. R. China Zhejiang CoTEK Robotics Co. Ltd Hangzhou P. R. China Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen P. R. China Peng Cheng Laboratory Shenzhen P. R. China Wenzhou Institute of Industry & Science Wenzhou P. R. China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

In this paper, we introduce an innovative method for the orientation-specific path planning of an Automated Guided Vehicle (AGV) with steering constraints, such as unmanned forklifts. Traditional shortest-path algorithms, which solely consider distance or travel time, are unsuitable for tasks where the AGV’s orientation is crucial. To address this, we have created an augmented graph that inherently incorporates the AGV’s steering constraints and orientation. By utilizing this enhanced graph structure, conventional shortest-path algorithms can be employed to ascertain a path that takes the AGV’s orientation into account. This novel method facilitates more adaptable and secure path planning, while preserving the immediacy required for real-time path planning execution.

关键词： Remotely guided vehicles Path planning Real-time systems Task analysis Standards Robots

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Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics

arXiv

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arXiv 2021年

作者： Wang, Ruigang Koelwijn, Patrick J.W. Manchester, Ian R. Tóth, Roland Australian Centre for Field Robotics Sydney Institute for Robotics and Intelligent Systems University of Sydney SydneyNSW2006 Australia Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, we present a virtual control contraction metric (VCCM) based nonlinear parameter-varying (NPV) approach to design a state-feedback controller for a control moment gyroscope (CMG) to track a user-defined trajectory set. This VCCM based nonlinear stabilization and performance synthesis approach, which is similar to linear parameter-varying (LPV) control approaches, allows to achieve exact guarantees of exponential stability and L2-gain performance on nonlinear systems with respect to all trajectories from the predetermined set, which is not the case with the conventional LPV methods. Simulation and experimental studies conducted in both fully- and under-actuated operating modes of the CMG show effectiveness of this approach compared to standard LPV control methods. © 2021, CC BY.

关键词： State feedback

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Deployment Schemes of In-Band Management Mode Based on Industrial SDN Network

Deployment Schemes of In-Band Management Mode Based on Indus...

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6G Communications and IoT Technologies (6GIoTT), International Conference on

作者： Jinnan Piao Dong Li Ming Yang Jindi Liu Xueting Yu Zhibo Li Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang 110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110169 China

When software defined network (SDN) is used to deploy the industrial control network, the deployment schemes are different in actual according to the different industrial production business, environment and capital cost. In this paper, we use the in-band management mode to deploy the SDN network in an industrial environment. We design four deployment schemes of the in-band management mode for the industrial SDN network according to the specific requirements of industrial environment and propose an algorithm for controller location selection in the in-band management network. For the four schemes and the algorithm, we use the controller and switches to build a simulation environment according to different schemes and compare the performance between the in-band management and out-of-band management, which shows that the in-band management mode can set up a normal SDN industrial network with applicable network scale and throughput.

关键词： 6G mobile communication Costs Industrial control Production Throughput control systems Internet of Things

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Joint Routing and Charging Problem of Electric Vehicles with Incentive-Aware Customers Considering Spatio-Temporal Charging Prices

SSRN

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SSRN 2022年

作者： Yao, Canqi Chen, Shibo Salazar, Mauro Yang, Zaiyue School of Mechatronics Engineering Harbin Institute of Technology Harbin150000 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering The Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Control Systems Technology Group Department of Mechanical Engineering Eindhoven University of Technology EindhovenMB 5600 Netherlands

This paper investigates the scheduling problem of a fleet of electric vehicles, providing mobility as a service to a set of time-specified customers, where the operator needs to solve the routing and charging problem jointly for each EV. Hereby we consider incentive-aware customers and propose that the operator offers monetary incentives to customers in exchange for time flexibility. In this way, the fleet operator can achieve a routing and charging schedule with lower costs, whilst the customers receive monetary compensation for their flexibility. Specifically, we first propose a bi-level optimization model whereby the fleet operator optimizes the routing and charging schedule accounting for the spatio-temporal varying charging price, jointly with a monetary incentive to reimburse the delivery time flexibility experienced by the customers. Concurrently the customers choose their own time flexibility by minimizing their own cost. Second, we cope with the computational burden coming from this nonlinear bi-level optimization model with an accurate reformulation approach consisting of the KKT optimality conditions, a Big-M-based linearization method, and the zero duality gap of convex optimization problems. This way, we convert the proposed problem into a single-level optimization problem, which can be solved by a strengthened generalized Benders decomposition method holding a faster convergence rate than the generalized Benders decomposition method. To evaluate the effectiveness of the proposed mathematical model, we carry out numerous simulation experiments by using the VRP-REP data of Belgium. The numerical results showcase that the proposed mathematical model can reduce the delivery fees for the customers together with the cost of operation incurred by the fleet operator. © 2022, The Authors. All rights reserved.

关键词： Sales

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Joint Routing and Charging Problem of Electric Vehicles with Incentive-aware Customers Considering Spatio-temporal Charging Prices

arXiv

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arXiv 2022年

关键词： Sales

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Position control of X-Y Precision Planar Motion Stage Based on Iterative Learning and Cross-coupling

Position Control of X-Y Precision Planar Motion Stage Based ...

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Data Driven control and Learning systems (DDCLS)

作者： Jingzhe Fang Tao Zhang Xiyou Chen Chen Qi Guopeng Zhang Hualiang Zhang Hongyu Yan School of Electrical Engineering Shenyang University of Technology Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian 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

In some precision machining applications, not only should we pay attention to the tracking accuracy of the X-Y precision plane motion table, but the more important criterion is the contour accuracy. This paper proposes an adaptive iterative learning control that adjusts in real time with the system's working state for the X-Y precision plane motion stage control system. Then on the basis of adaptive iterative learning control, the relationship between the two axes is established by cross-coupling control, the contour error of the system is calculated in real time, and the stability of the system is improved. The final experimental results show that the improved algorithm has smaller contour error and better robustness.

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Design of a Novel Autonomous Docking System With a Supporting Robotic Fish

Design of a Novel Autonomous Docking System With a Supportin...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Huijie Dong Chunyang Zhao Tiezhu Qiao Jian Wang Di Chen Yan Meng Junzhi Yu Ministry of Education Key Laboratory of Advanced Transducers and Intelligent Control System Taiyuan University of Technology Taiyuan China Institute of Automation Laboratory of Cognitive and Decision Intelligence for Complex System Chinese Academy of Sciences Beijing China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China College of Electronics and Information Engineering Guangdong Ocean University Zhanjiang China

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named ‘CourierFish’. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks.

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Research on Distributed intelligent Protection Strategy of distribution network based on Reinforcement Learning 2

Research on Distributed Intelligent Protection Strategy of d...

引用

2nd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Cui, Shijie Zeng, Peng Hu, Ende Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics 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 Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China State Grid Shaoxing Power Supply Company Shaoxing312099 China

ISBN: (纸本)9781665428767

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. © 2021 IEEE.

关键词： Reinforcement learning

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Path-tracking of Mobile Robot Using PD-type Iterative Learning control with Forgetting Factor

Path-tracking of Mobile Robot Using PD-type Iterative Learni...

引用

Chinese control and Decision Conference, CCDC

作者： Tao Zhang Jingzhe Fang Yun Teng Xintong Wang Yiqiang Zhang Xiyou Chen School of Electrical Engineering Dalian University of Technology Dalian China 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 School of Electrical Engineering Shenyang University of Technology Shenyang China

In this paper, a discrete iterative learning control strategy is proposed for the path-tracking problem of mobile robot trajectory tracking with repeated trajectories. The algorithm adds the forgetting factor to the PD-type discrete iterative learning control and considers the correction of the initial state of the control. The convergence of the algorithm is proved rigorously by using the paradigm theory, and the sufficient conditions of the paradigm form to make the algorithm converge are obtained. Finally, the effectiveness of the proposed algorithm is verified by simulation.

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