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Multibody simulation for tracking error reduction in solar tracking robots

AIP Conference Proceedings 2023年第1期2872卷

作者： Aldo Hernández-Díaz Diego A. Flores-Hernández Alberto Luviano-Juárez Sergio Isai Palomino-Resendiz Itzamá López-Yáñez National Polytechnic Institute - UPIITA Applied Dynamic Systems Laboratoy Mexico City 07340 Mexico National Polytechnic Institute - UPIITA Advanced Robotics Laboratory Mexico City 07340 Mexico National Polytechnic Institute - ESIME-ZAC Department of Control and Automation Mexico City 07738 Mexico National Polytechnic Institute - CIDETEC Mexico City 07738 Mexico

Currently, the use of renewable energy sources such as solar energy, collected by photovoltaic technology, has gained ground in the energy sector. Its use has been popularised due to the fact that it is considered a clean energy, that is a type of energy is produced without burning fossil fuels which are a cause of global warming, among other environmental damages. In order to optimize the energy harvesting through photovoltaic technology, it is necessary to maintain a perpendicular incidence of the sun rays with the photovoltaic module, leading to the use of solar tracking systems that demand the application of trajectory tracking control strategies that minimize the tracking errors while ensuring the lowest energy consumption of the robotic system. In this work, a comprehensive multibody simulation was carried out to obtain a concrete appreciation of the behavior of the tracking system prior to an experimental implementation. This type of simulations allows us to simulate complete systems formed by solid links and rigid joints that were previously designed, and with a calculated analytical model (Kinematic and Dynamic). Furthermore, the obtained simulator allows us to apply a variety of control strategies that are represented by means of 3D simulations which are very similar to the actual experimental results, so that it would be possible to estimate the ªtracking error results° without the necessity of a physical implementation. From the kinematic and dynamic models of a solar tracker of two degrees of freedom, released in an azimuthal-elevation configuration, a PID controller and a passivity-based controller were implemented in Matlab-Simscape®, obtaining simulations of a solar trajectory tracking minimizing the error between the reference and the solar tracker.

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Reconfigurable Magnetic Microswarm for Thrombolysis under Ultrasound Imaging

Reconfigurable Magnetic Microswarm for Thrombolysis under Ul...

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IEEE International Conference on robotics and automation (ICRA)

作者： Qianqian Wang Ben Wang Jiangfan Yu Kathrin Schweizer Bradley J. Nelson Li Zhang Department of Mechanical and Automation Engineering The Chi-nese University of Hong Kong (CUHK) Shatin NT Hong Kong China Institute of Robotics and Intelligent Systems ETH Zurich Zurich Switzerland

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

We propose thrombolysis using a magnetic nanoparticle microswarm with tissue plasminogen activator (tPA) under ultrasound imaging. The microswarm is generated in blood using an oscillating magnetic field and can be navigated with locomotion along both the long and short axis. By modulating the input field, the aspect ratio of the microswarm can be reversibly tuned, showing the ability to adapt to different confined environments. Simulation results indicate that both in-plane and out-of-plane fluid convection are induced around the microswarm, which can be further enhanced by tuning the aspect ratio of the microswarm. Under ultrasound imaging, the microswarm is navigated in a microchannel towards a blood clot and deformed to obtain optimal lysis. Experimental results show that the lysis rate reaches -0.1725 ± 0.0612 mm 3 /min in the 37°C blood environment under the influence of the microswarm-induced fluid convection and tPA. The lysis rate is enhanced 2.5-fold compared to that without the microswarm (-0.0681 ± 0.0263 mm 3 /min). Our method provides a new strategy to increase the efficiency of thrombolysis by applying microswarm-induced fluid convection, indicating that swarming micro/nanorobots have the potential to act as effective tools towards targeted therapy.

关键词： Ultrasonic imaging Magnetic resonance imaging Convection Coagulation Coils

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Sparse-3D Lidar Outdoor Map-Based Autonomous Vehicle Localization

Sparse-3D Lidar Outdoor Map-Based Autonomous Vehicle Localiz...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Syed Zeeshan Ahmed Vincensius Billy Saputra Saurab Verma Kun Zhang Albertus Hendrawan Adiwahono Department of Robotics and Automation Systems Institute of Infocomm Research A*STAR Singapore

Difficulties in capturing unique structures in the outdoor environment hinders the map-based Autonomous Vehicles (AV) localization performance. Accordingly, this necessitates the use of high resolution sensors to capture more information from the environment. However, this approach is costly and limits the mass deployment of AV. To overcome this drawback, in this paper, we propose a novel outdoor map-based localization method for Autonomous Vehicles in urban environments using sparse 3D lidar scan data. In the proposed method, a Point-to-Distribution (P2D) formulation of the Normal Distributions Transform (NDT) approach is applied in a Monte Carlo Localization (MCL) framework. The formulation improves the measurement model of localization by taking individual lidar point measurements into consideration. Additionally, to apply the localization to scalable outdoor environments, a flexible and efficient map structure is implemented. The experimental results indicate that the proposed approach significantly improves the localization and its robustness in outdoor AV environments, especially with limited sparse lidar data.

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automation of Life Data Analysis Processes

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IOP Conference Series: Materials Science and Engineering 2021年第1期1140卷

作者： Jonathan Krämer Gregor Thiele Theresa Johanni Jörg Krüger Department for Industrial automation at the Institute for Machine Tools and Factory Management Technical University of Berlin Department for Process automation and Robotics Fraunhofer Institute for Production Systems and Design Technology

Life data analysis is an effective statistical process to gain information on the reliability of technical facilities. The Weibull analysis is a widely-used method for modeling the probabilities of different functional failures. For its application in modern manufacturing and maintenance processes, an automation of this analysis is expedient. An exemplary process of data collection and fitting of a parametric model with continuous time scale is analyzed in this article. Its automation with different statistic software tools is described and comparatively evaluated. With the ambition of minimizing manual effort in the process, a highly automated solution was developed in this research. We describe this self-implemented solution in 'R' and justify that this procedure automatizes the process more than the examined commercial implementations. The here presented work proves to be a practical real-time assistance, right now applied in aircraft maintenance.

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Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industrial Process

Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industria...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Shaojie Yang Peng Li Shuai Li Xiaofeng Zhou Shanghong Jiang Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Information Center of Ansteel Mining Limited Company Anshan China

ISBN: (纸本)9781665401166

Aiming at the characteristics of multi-scale, time series and nonlinear of the complex industrial process data, in order to extract process information more efficiently for improving the diagnosibility, a novel fault diagnosis method based on multi-scale long short term memory- fully convolutional networks (LSTM-FCNs) for industrial process is proposed. Firstly, variational mode decomposition (VMD) is used to obtain the multi-scale components of the process data. Then a parallel LSTM and FCNs optimized by batch normalization and random inactivation is built to extract features for each component. Finally, based on the fusion of multi-scale features, a fully connected layer is used for diagnosis. The LSTM-FCNs method is evaluated by the Tennessee Eastman (TE) benchmark industrial process, and compared with other methods, its effectiveness and superiority are verified.

关键词： Fault diagnosis Fault detection Time series analysis Benchmark testing Feature extraction Safety Data mining

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Fault Diagnosis Based on RseNet-LSTM for Industrial Process

Fault Diagnosis Based on RseNet-LSTM for Industrial Process

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IEEE Advanced Information Technology, Electronic and automation Control Conference (IAEAC)

作者： Peifu Yao Shaojie Yang Peng Li China Copper Co.LTD Kunming China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation 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

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

Aiming at the problems that conventional data-driven diagnosis methods are difficult to adaptively extract effective features from industrial process data, and do not make full use of the time series characteristics of process data, in this paper, a fault diagnosis method based on residual convolutional neural networks and long short-term memory networks (ResNet-LSTM) is developed. Firstly, the local spatial features of process data are captured by the deep residual convolution network. Then, the time series characteristics of process data are extracted by LSTM. Finally, the output of the fault category is performed through the softmax classifier. This method can extract features adaptively, more fully extract features in time series fault data, and effectively reduce the difficulty of deep neural network training. The benchmark Tennessee-Eastman (TE) process is used to validate performance of the proposed method. The ResNet-LSTM model is compared with the CNN, LSTM, ResNets, CNN-LSTM models, and the experiment results show that the ResNet-LSTM method achieves better performance.

关键词： Fault diagnosis Training Time series analysis Neural networks Process control Feature extraction Data mining

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Design and analysis of a flying-crawling spherical robot for multi-mode movement

Design and analysis of a flying-crawling spherical robot for...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Lu, Pengfei Xu, Kun Ding, Xilun Jiang, Shuiqing Tang, Zixin Wang, Yaobing Beihang University Robotics Institute of School of Mechanical Engineering and Automation Beijing China Beijing Institute of Spacecraft System Engineering Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing China

ISBN: (纸本)9781728163215

A novel spherical robot named flying-crawling spherical robot is designed in this paper. It can fly or roll on ground with its single rotor and eight tail fins. When its limbs stretch out it can crawl on the supporting surface. Besides, with its limbs the robot can realize carrying objects, landing on uneven ground and walking on the supporting surface. Dynamic model of the robot in flight mode is established and the motion characteristics are analyzed. A PID control system is designed to enable the robot to achieve stable flight. Trot gait planning of the robot is given and a PID controller is designed for tail fins assistant in crawling mode. Corresponding simulations are carried out and the simulation results verify the proposed methods. © 2019 IEEE.

关键词： Three term control systems

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An Accelerated Iterative Learning Control Approach for X-Y precision plane motion stage

An Accelerated Iterative Learning Control Approach for X-Y p...

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作者： Tao Zhang Hongyu Yan Danping Jia Hualiang Zhang Bingjie Zhao Xu Lu Yang Liu Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Dalian University of Technology School of Artificial Intelligence Shenyang University of Technology School of Information Science and Engineering Shenyang University of Technology

X-Y precision plane motion stage is widely used in precision motion control fields such as lithography machine workpiece stage,CNC machine tool,Laser measuring system,etc.,which requires high accuracy of the position *** X-Y precision plane motion stage uses two permanent magnet linear synchronous motors(PMLSM) as drivers to drive the motion table to perform XY two-degree-of-freedom *** open-closedloop PD-type iterative learning controller(ILC) approach based on accelerated convergence was proposed to improve trajectory tracking performance of *** interval was changed by real-time correction of the exponential gain matrix in the interval to accelerate the convergence *** to the λ norm,the monotone convergence of the proportion differentiation(PD) was developed for the *** controller consists of two terms:a PID controller which enhances robustness to disturbances and system parameter variability and an ILC which generally improves the performance in the repetitive tasks by using the information from previous *** experimental results show that tracking precision and convergence speed of PMLSM can be effectively improved by the scheme.

关键词： X-Y precision plane motion stage Iterative learning control tracking precision convergence rate

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On the Robustness of Power IOT against Attack Based on General Framework

On the Robustness of Power IOT against Attack Based on Gener...

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Power and Energy Engineering (ICPEE), International Conference on

作者： Tianyu Wang Weiwei Miao Jianming Zhao Bowen Zhang Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China State Grid Jiangsu Electric Power Co. Ltd Information & Telecommunication Branch Nanjing China

ISBN: (纸本)9781665420471

Power Internet of Things (IOT) is the application of Internet of things in smart grid and the result of the development of information and communication technology to a certain stage. It will effectively integrate communication infrastructure resources and power system infrastructure resources, and improve the informationize level of power system. However, real-world complex systems are often vulnerable under disruptions. Power IOT is facing with different types of attack, which contains natural disasters and human intentional attacks. Motivated by this point, we propose a general framework of Power IOT robustness with different structures against diverse attacks. Evaluation on example IOT networks shows the effectiveness of our framework, and the characteristics of different structure against attacks. Our study contributes to a better understanding of Power IOT robustness, and reveals the designation of the Power IOT network improves the robustness of the network.

关键词： Robustness Power systems Smart grids Information and communication technology Internet of Things Complex systems

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Speech-Vision Based Multi-Modal AI Control of a Magnetic Anchored and Actuated Endoscope

Speech-Vision Based Multi-Modal AI Control of a Magnetic Anc...

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IEEE International Conference on robotics and Biomimetics

作者： Jixiu Li Yisen Huang Wing Yin Ng Truman Cheng Xixin Wu Qi Dou Helen Meng Pheng Ann Heng Yunhui Liu Shannon Melissa Chan David Navarro-Alarcon Calvin Sze Hang Ng Philip Wai Yan Chiu Zheng Li Department of Surgery The Chinese University of Hong Kong Hong Kong Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The Department of Mechanical and Automation Engineering T stone Robotics Institute The Chinese University of Hong Kong Hong Kong Department of Mechanical Engineering The Hong Kong Polytechnic University Hong Kong Department of Surgery and the Chow Yuk Ho Technology Centre for Innovative Medicine The Chinese University of Hong Kong Hong Kong Department of Surgery Chow Yuk Ho Technology Centre of Innovative Medicine Li Ka Shing Institute of Health Science and Multi-scale Medical Robotics Center Ltd. The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781665481106

In minimally invasive surgery (MIS), controlling the endoscope view is crucial for the operation. Many robotic endoscope holders were developed aiming to address this prob-lem,. These systems rely on joystick, foot pedal, simple voice command, etc. to control the robot. These methods requires surgeons extra effort and are not intuitive enough. In this paper, we propose a speech-vision based multi-modal AI approach, which integrates deep learning based instrument detection, automatic speech recognition and robot visual servo control. Surgeons could communicate with the endoscope by speech to indicate their view preference, such as the instrument to be tracked. The instrument is detected by the deep learning neural network. Then the endoscope takes the detected instrument as the target and follows it with the visual servo controller. This method is applied to a magnetic anchored and guided endoscope and evaluated experimentally. Preliminary results demonstrated this approach is effective and requires little efforts for the surgeon to control the endoscope view intuitively.

关键词： Deep learning Visualization Target tracking Endoscopes Instruments Magnetic resonance imaging Surgery

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