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A Neural-Network-Based controller for Piezoelectric-Actuated Stick-Slip Devices

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2598-2607页

作者： Cheng, Long Liu, Weichuan Yang, Chenguang Huang, Tingwen Hou, Zeng-Guang Tan, Min State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Zienkiewicz Centre for Computational Engineering Swansea University Swansea U.K. Texas A&M University at Qatar Doha Qatar State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Piezoelectric-actuated stick-slip device (PASSD) is a highly promising equipment that is composed of one end-effector, one piezoelectric actuator (PEA) and one driving object adhered to the PEA. Since the endeffector can slip on the surface of the driving object, the PASSD is capable of realizing the macrolevel motion with the microlevel precision. Due to the following two reasons: the complicated relative motion between the end-effector and the driving object, and the inherent hysteresis nonlinearity in the PEA, the ultraprecision displacement control of the end-effector of PASSDs raises a real challenge, which is rarely reported in the literature. Toward solving this challenge, a neural-network-based controller is proposed in this paper. First, a neural-network-based model is proposed to capture the relative motion between the end-effector and the driving object. Second, a neural-network-based inversion model is developed to online calculate the desired position of the PEA under the predesigned reference of the end-effector. Third, a dynamic linearized neural-network-based model predictive control method, which can effectively handle the hysteresis nonlinearity, is employed to implement the displacement control of the PEA, which finally results in an overall high-precision controller of the end-effector. Finally, a PASSD prototype has been implemented and tested through experimental studies to demonstrate the effectiveness of the proposed approach.

关键词： End-effector neural network piezoelectric actuator (PEA) stick-slip

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A Self-identifying Checkerboard-like Pattern for Camera Calibration

A Self-identifying Checkerboard-like Pattern for Camera Cali...

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第三十九届中国控制会议

作者： Zhang Gao Mingzhu Zhu Junzhi Yu School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceBIC-ESATCollege of EngineeringPeking University

This paper proposes a new type of planar pattern for camera calibration featuring its checkerboard-like structure and self-identity. White and black blocks are arranged by a novel method to ensure the uniqueness of each local pattern, and meanwhile maintain all checkerboard corners. The pattern is generated by combining identification units based on a random search strategy. Positions of local patterns can be acquired by searching the global pattern, achieving the benefit of self-identity. Because of the checkerboard-like structure, no burden is introduced in sampling compared to traditional calibrations. Experimental results show the effectiveness of the proposed calibration method. The conceived pattern is especially suitable for the calibration of multi-camera system with limited overlapping scope.

关键词： Camera calibration pattern self-identity checkerboard-like

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Internet of Mind with General Intelligent Dispatch in Electrical Industrial: The Concept, Framework, Technology, and Future

Internet of Mind with General Intelligent Dispatch in Electr...

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Tianlu Gao Xiaoyan Gong Shiqian Ma Weichen Wang Xiaojing Yang Jian Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China Power Dispatching and Control center State Grid Tianjin Electric Power Company Tianjin China

ISBN: (数字)9781728176840

ISBN: (纸本)9781728176857

This paper proposes the concept of general intelligent dispatch, which is a novel framework of electrical power dispatch based on modern artificial intelligence (AI). The general intelligent dispatch system is based on knowledge automation, parallel system, ACP method, and fully integrated with cutting-edge technologies such as parallel deep learning, parallel dynamics programming. The general intelligent dispatch system is devised to be a comprehensive framework that can support the applications of various modern AI technologies in electrical power dispatch. General intelligent dispatch can accommodate the rapid development of the future smart grid, especially the extensive implementation of distributed energy resources and renewable energy. General intelligent dispatch also provides the practical technical path and framework for the future smart power dispatch services to achieve a higher level of automation and intelligence. Based on the general intelligent dispatch concept, this paper addresses power grid situational awareness, which provides an example for the applications of general intelligent dispatch.

关键词： automation Optimal scheduling Real-time systems Dispatching Smart grids Artificial intelligence Business

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The Application of Artificial Intelligence in Energy Internet

The Application of Artificial Intelligence in Energy Interne...

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Zhigang Huang Tianlu Gao Xiaoyan Gong Qian Li Wenzhong Gao Power Dispatching and Control Center State Grid Tianjin Electric Power Company Tianjin China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China

ISBN: (数字)9781728176840

ISBN: (纸本)9781728176857

The energy internet is a cyber-physical system (CPS) to connect directly and indirectly the devices of information, communication, control, and protection to effectively generate, transmit, use, and store various energy by merging new technologies such as artificial intelligence (AI), cloud computing, and blockchain, etc. Recently, AI technology has become increasingly popular in many research and industrial fields because it can perform tasks well. However, applying AI technologies to energy internet is relatively less. Therefore, in this paper, the potential applications of AI technologies in the energy internet will be reviewed and discussed. In section II and section III, the operating and monitoring of power systems are the main parts to discuss. Firstly, applying AI technologies to the operation of control, optimization, and decision-making in power systems will be detailed in section II from three aspects such as existing problems, potential demands, and valuable improvement. Secondly, in section III, detecting faults and analyzing stability by energy internet monitoring is mainly discussed. Finally, in section IV, it shows an example using the neural network to analyze the power flow of a standard IEEE 39 nodes power system compared with traditional Newton-Raphson method.

关键词： Decision making Power system stability Stability analysis Power grids Artificial intelligence Monitoring Optimization

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TLogic: A Tangible Programming Tool to Help Children Solve Problems 12th

TLogic: A Tangible Programming Tool to Help Children Solve P...

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Deng, Xiaozhou Wang, Danli Jin, Qiao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030237110

In this paper, we present TLogic, a tangible programming tool for children aged 6–8. The tool contains two parts: tangible programming blocks and visual tasks with different difficulty levels. Children could use tangible programming blocks to complete visual tasks shown on computer screen. To evaluate our tool, a user study was conducted with 10 children. Results of user study show that the tool could reduce children’s cognitive load while solving the tasks and children are more interested in challenging tasks than easy tasks in our tool. © 2019, Springer Nature Switzerland AG.

关键词： Computer programming

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Three-Dimensional Path Following control of an Underactuated Robotic Dolphin Using Deep Reinforcement Learning

Three-Dimensional Path Following Control of an Underactuated...

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

作者： Jincun Liu Zhenna Liu Zhengxing Wu Junzhi Yu State Key Laboratory for Turbulence and Complex Systems BIC-ESAT College of Engineering Peking University Beijing China Shandong Labor Vocational and Technical College Jinan China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

In this paper, a novel improved deep reinforcement learning (DRL) strategy is utilized to solve the three-dimensional path-following control problem for an underactuated robotic dolphin in order to meet the complex and intractable hydrodynamic model. Firstly, a brief overview of the developed robotic dolphin consisting of two-degree-of-freedom paired pectoral fins and the slider-crank-based flapping two-joint tail is introduced. After that, path-following controller includes a novel lookahead-based guidance law, decoupling motion strategy, and a deep deterministic policy gradient algorithm based on the the prior knowledge is proposed. The novel lookahead based 3-D LOS guidance law is employed to transform the 3-D waypoints to the desired heading and pitch angles with its simplicity, intuitiveness, and small computational footprint. Finally, the effectiveness of the proposed controller is demonstrated after numerous simulations. It will offer a rich vein of insight for the real-time task execution in bioinspired underwater robots.

关键词： Robots Dolphins Sports Oscillators Biological system modeling Turning Aerospace electronics

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Learning Network-Invariant and Label-Discriminative Representations for Cross-Network Node Classification

Learning Network-Invariant and Label-Discriminative Represen...

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Chinese automation Congress (CAC)

作者： Linyao Yang Yancai Xu Jiachen Hou Yuxin Dai Chen Lv The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Institute of Systems Engineering Macau University of Science and Technology Macau China School of Electrical Engineering and Automation Wuhan University Wuhan China China Electric Power Research Institute Beijing China

ISBN: (纸本)9781665426480

Networks are ubiquitous data structures in the real world. The accurate and efficient analysis of networks is critical to realizing many intelligent network-based services. However, most existing network analysis methods are developed for single networks and require a lot of labeled data, which is costly and time-consuming to acquire. Transfer learning has been widely accepted as an effective paradigm for tackling this problem by reusing the model trained on a supervised task. However, transfer learning on the non-euclidean network data has been investigated by no more than a few studies. To realize accurate node classification based on the knowledge learned from the labeled source network, this paper proposes to learn network-invariant and label-discriminative representations based on graph embedding and linear discriminant analysis. Specifically, we embed the source and target networks into adjacent vector spaces based on the graph attention network by minimizing the Sinkhorn distributional distances between their embeddings. To obtain label-discriminative features for learning better classification models, we then utilize a transferable linear discriminative analysis method to project the embeddings into label-discriminative subspaces. In the end, a support vector machine model trained on the labeled source network is utilized to classify the target nodes. Experiments on two pairs of networks illustrate that our method achieves the best performance and evaluates the effectiveness of the proposed modules.

关键词： Support vector machines Knowledge engineering Analytical models automation Transfer learning Network analyzers Data structures

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CoProStory: A tangible programming tool for children’s collaboration 13

CoProStory: A tangible programming tool for children’s coll...

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13th International Conference on Computer Supported Collaborative Learning - A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, CSCL 2019

ISBN: (纸本)9781732467231

In this paper, we present Co Pro Story, a collaborative tangible programming tool to support children learning programming. In recent years, many collaborative tangible programming tools were developed for children. Compared to the other tools, Sync Blocks which were designed to coordinate children’s programming were used in our tool. The tool consists of two parts: Tangible programming blocks and game tasks. Game tasks are provided with 3D cartoon story scenes on computer screen and need to be finished by collaborative programming with our programming blocks. To evaluate our tool, a contrast user study was conducted with 28 children aged 6 to 10. The results demonstrated that CoProStory could support children to program collaboratively and children had a more positive attitude toward programming with Sync Blocks. © ISLS.

关键词： Computer games

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Dynamic power systems line outage detection using particle filter and partially observed states

arXiv

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arXiv 2021年

作者： Yang, Xiaozhou Chen, Nan Zhai, Chao The Department of Industrial Systems Engineering and Management National University of Singapore Singapore117576 Singapore ETH Zurich Future Resilient Systems Singapore-ETH Centre 1 CREATE Way CREATE Tower Singapore138602 Singapore The School of Automation China University of Geosciences Wuhan430074 China The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Real-time transmission line outage detection is difficult because of partial phasor measurement unit (PMU) deployment and varying outage signal strength. Existing detection approaches focus on monitoring PMU-measured nodal algebraic states, i.e., voltage phase angle and magnitude. The success of such approaches, however, is largely predicated on strong outage signals and the presence of PMUs in the outage location's vicinity. To overcome these limitations, a unified framework is proposed in this work by utilizing both nodal voltage information and generator dynamic states, e.g., rotor angular position. The proposed scheme is shown to be faster and more robust to unknown outage locations through the incorporation of generator dynamics. Using the IEEE 39-bus system simulation data, the proposed scheme's properties and performances compared to existing approaches are presented. The new approach could help improve operators' real-time situational awareness by detecting outages faster and providing a breakdown of outage signals for diagnostic purposes, making future power systems more resilient. Copyright © 2021, The Authors. All rights reserved.

关键词： Monte Carlo methods

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A Real-Time Multi-Task Framework for Guidewire Segmentation and Endpoint Localization in Endovascular Interventions

A Real-Time Multi-Task Framework for Guidewire Segmentation ...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Yan-Jie Zhou Shi-Qi Liu Xiao-Liang Xie Xiao-Hu Zhou Guan-An Wang Zeng-Guang Hou Rui-Qi Li Zhen-Liang Ni Chen-Chen Fan State Key Laboratory 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 CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China

Real-time guidewire segmentation and endpoint localization play a pivotal role in robot-assisted minimally invasive surgery, which is helpful to reduce radiation dose and procedure time. Nevertheless, the tasks often come with the challenge of limited computational resources. For this purpose, a real-time multi-task framework with two stages is developed. In the first stage, a Fast Attention-fused Network (FAD-Net) is proposed to obtain accurate guidewire segmentation masks. In the second stage, a lightweight localization network and a post-processing algorithm are designed to robustly predict the guidewire endpoint position. Quantitative and qualitative evaluations on intraoperative X-ray sequences from 30 patients demonstrate that the developed framework outperforms the previously-published results for the tasks, achieving state-of-the-art performance. Moreover, the inference rate of the developed framework is approximately 10.6 FPS, which meets the real-time requirement of X-ray fluoroscopy. These results indicate the proposed approach has the potential to be integrated into the robotic navigation framework for endovascular interventions, enabling robotic-assisted minimally invasive surgery.

关键词： Location awareness Minimally invasive surgery Navigation Prediction algorithms Real-time systems Robustness Inference algorithms

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