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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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Design of Capsule Robot for Magnetic Gastric Biopsy and Drug Administration 20

Design of Capsule Robot for Magnetic Gastric Biopsy and Drug...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Xu, Zhihui Li, Zongze Liu, Shoubin Hu, Chengzhi Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Harbin Institute of Technology School of Mechanical and Electrical Engineering Shenzhen China

ISBN: (纸本)9798350320831

Capsule robots have emerged as a promising tool for the early diagnosis of gastrointestinal diseases. Unlike conventional gastroscopy, they offer the potential for minimally invasive or even non-invasive treatment, and can alleviate discomfort and pain in patients. However, current capsule robots have some limitations. For instance, they lack stomach tissue biopsy and drug administration functions, and the capsule robot used for biopsy does not consider the tolerance of gastrointestinal tissue. Similarly, the capsule robot used for drug administration does not account for the outlet speed of drug administration. This paper proposes a new biopsy method using a magnetically manipulated capsule robot and analyzes the principles governing the robot's manipulation during the biopsy and drug administration processes. Furthermore, an ANSYS gastric model was established and used to simulate and experimentally analyze damage to the gastric tissue model from different types of biopsy needles and their moving speeds. The appropriate type of biopsy needle and moving speed were determined to prevent damage to the gastric tissue. The study also used FLUENT to determine the appropriate thrust at the entrance of the drug cavity and the impact of the drug density on the drug outlet velocity to ensure complete adherence of the drug to the lesion. © 2023 IEEE.

关键词： Biopsy

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Whole Body Balance Control for Bipedal Robots Based on Virtual Model Control 16th

Whole Body Balance Control for Bipedal Robots Based on Virtu...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Gao, Chengzhi Xie, Ye Zhu, Yiting Liang, Dingkun Kong, Lingyu Wang, Xin Xie, Anhuan Gu, Jianjun Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Zhejiang University Zhejiang Hangzhou311100 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9789819964918

Due to the lack of accurate stability theoretical basis, the dynamic balance control of bipedal robots is a challenging topic. In this paper, we propose a controller that combines virtual model control and whole body control. In our framework, the whole system is simplified into a spring inverted pendulum model. Decoupling control is carried out for this model, and a virtual model is introduced to establish the relationship between the joint torque of the support leg and the state of the center of mass. The distribution of reaction force is obtained through decoupling closed-loop control. The whole body control part first uses multi task whole body control to determine the kinematics information, and then solves the joint feedforward torque through dynamical model, thus obtaining the desired force position hybrid control. Proposed algorithm improved the problem of incomplete decoupling between states and increased the stiffness of the support leg. The newly designed control framework was tested in Simscape dynamic simulation and verified the anti-interference ability and fast walking ability of Whole Body Balance Control for bipedal robots based on Virtual Model Control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Inverted pendulum

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AGVs vs AMRs: A Comparative Study of Fleet Performance and Flexibility

AGVs vs AMRs: A Comparative Study of Fleet Performance and F...

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Iberian robotics Conference (ROBOT)

作者： Rita Tomé Silva Marina Brilhante Héber Sobreira Diogo Matos Pedro Costa Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto Portugal Centre of Robotics in Industry and Intelligent Systems INESC TEC Porto Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

Autonomous Mobile Robots (AMRs) and Automated Guided Vehicles (AGVs) have emerged as key innovations in the industry world, with AMRs offering flexibility a nd adaptability for dynamic environments, while AGVs provide high accuracy for repetitive tasks; thus, this research proposes a study of fleets of both AGVs and AMRs to enhance productivity and efficiency in industrial settings. Several tests were performed where the duration of a mission, the success and collision rate, and the average number of disputes per mission were analyzed in order to obtain results. In conclusion, while AGVs tend to be more reliable and consistent in task completion, AMRs offer greater flexibility a nd speed.

关键词： Productivity Technological innovation Remotely guided vehicles Navigation Scalability Path planning Mobile robots Reliability Vehicle dynamics Autonomous robots

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Fast Walking of Position-Controlled Biped Robot Based on Whole-Body Compliance Control 16th

Fast Walking of Position-Controlled Biped Robot Based on Who...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Zhu, Yiting Xie, Ye Gao, Chengzhi Liang, Dingkun Kong, Lingyu Wang, Xin Xie, Anhuan Gu, Jianjun Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Zhejiang University Zhejiang Hangzhou311100 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9789819964918

When a biped robot walks at a high speed, only relying on the open-loop gait trajectory based on the linear inverted pendulum model (LIPM), the robot cannot maintain stability. Sudden contact with the ground when landing may cause the robot to be unstable. Simultaneously, when the robot walks faster, the horizontal position of the center of mass (CoM) varies greatly during a walk cycle, and the expected CoM trajectory cannot be accurately tracked, resulting in a deviation from the zero-moment point (ZMP). Aiming at the above situation that may occur in fast walking, a whole-body compliance controller with emphasis on making robot’s walking more stable and faster is presented in this paper. The whole-body compliance controller consists of three parts: the CoM compliance controller, the vertical compliance controller and the foot compliance controller, which are used to improve the tracking effect of CoM, reduce the force impact of foot, and adjust the posture of the foot respectively. The simulation results show that with the whole-body compliance controller proposed in this paper, the tracking accuracy of the CoM becomes better, the posture of the robot is more stable during walking, and the landing compliance during landing are greatly improved. Finally, the walking speed of the robot can reach 1.8m/s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Compliance control

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Graph Models of Electric Power Transmission Networks Using Synchrophasor Data

Graph Models of Electric Power Transmission Networks Using S...

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IEEE Southeastcon

作者： Ganesh K. Venayagamoorthy Dulip Madurasinghe Rajan Ratnakumar Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University SC USA

The electric power transmission network (EPTN) is a connected, geographically distributed network, which can be represented with a graph model with nodes and edges. The graph represents the physical connectivity of the EPTN. Deriving the graph model of the EPTN solely based on continually measured data is important for reliable power system operational situation awareness and decreasing uncertainties driven by weather, electrification, and interconnections. In this paper, an exhaustive search algorithm is presented to derive the EPTN graph model (TNGM) solely based on synchrophasor measurements retrieved from the substations without prior knowledge of the connectivity. Furthermore, an evaluation metric for any data-driven TNGM is introduced. The transmission network of the two-area, four-machine power system is used as the test system, and typical TNGMs for different power plant operating conditions are presented. Typical graphs obtained for network impedance and power flows are shown to be accurate, on average within 2% error margin.

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Automation 5.0: The Key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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Data-Driven Adaptive Control for Frequency Perturbations in Power systems with PV-SmartParks

Data-Driven Adaptive Control for Frequency Perturbations in ...

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Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES Companion), IEEE Symposia on

作者： Ali Arzani Ganesh K. Venayagamoorthy Holcombe Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University SC USA Department of Electrical Electronic and Computer Engineering University of Pretoria South Africa

ISBN: (数字)9798331519681

ISBN: (纸本)9798331519698

Seamless operation of PV plants during both transient and dynamic conditions is imperative for all stakeholders including energy control centers. Fast and dampened transient response, low steady-state error, and stability are sought for power system operation with PV plants. For PV power perturbation at point of interconnection (POI), this can be further supported by deploying auxiliary fast-acting non-synchronous generation such as SmartParks (SPs). SmartParks are parking lots with a large number of electric vehicles performing bidirectional power transfers, namely, vehicle-to-grid and grid-to-vehicle. In this study, a data-driven adaptive frequency controller (DD-AFC) was developed for SPs to compensate for frequency perturbations at PV-POI. The DD-AFC was incorporated into the controller of SPs to provide a fast response for frequency perturbations caused by PV-site power perturbations. The adaptiveness of the frequency droop comes from data-driven empirical characterization of the PV plant operation at the PV-site location using time-series data analytics. The DD-AFC supports fast-frequency control response by SPs for mitigating fast power perturbation at PV-POIs. The effectiveness of the DD-AFC was illustrated via real-time simulations of a bulk power system with a 210MW p PV plant and 100MW p of SmartParks in RTDS.

关键词： Automatic frequency control Time-frequency analysis Vehicle-to-grid Transient response Data analysis Perturbation methods Power system stability Adaptive control Transient analysis Vehicle dynamics

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Inverse Kinematics of Robotic Manipulators Using a New Learning-by-Example Method

Inverse Kinematics of Robotic Manipulators Using a New Learn...

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

作者： Jacket Demby’s Ramy Farag Guilherme N. DeSouza Department of Electrical Engineering and Computer Science (EECS) Vision-Guided and Intelligent Robotics (ViGIR) Laboratory University of Missouri-Columbia Columbia Missouri

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Inverse Kinematics (IK) is one of the most fundamental challenges in robotics. It refers to the process of determining the joint configurations required to achieve the desired position and orientation (pose) of a robot end-effector. Although numerous Data-Driven (DD) IK solvers have demonstrated encouraging results, they have not achieved the same accuracy when compared to other IK methods for complex robot configurations (e.g., numerical methods for higher Degrees of Freedom (DoF)). In this work, we propose a new Learning-by-Example method, and show that such a scheme considerably improves the IK learning results when compared to other DD learners. In our approach, the network input incorporates an example of joint-pose pair along with the query pose to predict the desired robot joint configuration. We show that the example joint-pose pair does not need to be too close to the query – i.e. example and query can be as far as 20 degrees apart in the joint configuration space. Furthermore, we investigate the utilization of residual and dense skip connections in Multilayer Perceptron for DDIK solvers and employ the resulting networks for two redundant robotic manipulators: a 7-DoF-7R commensurate robot and a 7DoF-2RP4R incommensurate robot. Our experimental results show that the resulting DDIK solver can reliably predict IK solutions with accuracy better than 1mm in position and 1deg in orientation.

关键词： Hands Accuracy Kinematics Network architecture Multilayer perceptrons Numerical models Reliability Collision avoidance Robots intelligent robots

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Resilient Event-Triggered Model Predictive Control for Adaptive Cruise Control Under Sensor Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 807-809页

作者： Zhijian Hu Rong Su Kai Zhang Zeyuan Xu Renjie Ma School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technologyand the Center of Ultra-Precision Optoelectronic Instrument EngineeringHarbin Institute of TechnologyHarbin 150001China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China IEEE

Dear Editor,This letter addresses the resilient model predictive control(MPC)problems for adaptive cruise control(ACC)systems under sensor *** the light of vulnerabilities of ACC systems to sensor attacks,an intrusion detection mechanism is proposed at the controller side to distinguish abnormal ***,the robust control gains are derived to design the terminal region constraint for MPC.

关键词： Trigger Event constraint

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