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arXiv 2020年

作者： Cheng, Xiaodong Yu, Lanlin Ren, Dingchao Scherpen, Jacquelien M.A. Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Institute of Advanced Technology Westlake Institute for Advanced Study Westlake University Hangzhou310024 China Department of Automation University of Science and Technology of China Hefei230026 China Jan C. Willems Center for Systems and Control Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands

This paper studies reduced-order modeling of dynamic networks with strongly connected topology. Given a graph clustering of an original complex network, we construct a quotient graph with less number of vertices, where the edge weights are parameters to be determined. The model of the reduced network is thereby obtained with parameterized system matrices, and then an edge weighting procedure is devised, aiming to select an optimal set of edge weights that minimizes the approximation error between the original and the reduced-order network models in terms of H2-norm. The effectiveness of the proposed method is illustrated by a numerical example. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks

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Resilient 2-∞ filtering with dwell-time-based communication scheduling

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Nonlinear Analysis: Hybrid systems 2020年 37卷 100901-100901页

作者： Sun, Ying Ding, Derui Dong, Hongli Wei, Guoliang Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China Institute of Complex Systems and Advanced Control Key Laboratory of Networking and Intelligent Control Northeast Petroleum University Daqing163318 China College of Science University of Shanghai for Science and Technology Shanghai200093 China

This paper is concerned with the resilient filtering problem for a class of discrete-time stochastic nonlinear systems with both time-varying delays and probabilistic distributed delays. In order to save the limited communication resource, stochastic communication protocols (SCPs) with multiple rules governed by a switching signal are employed to schedule the data transmission between the sensors and the filter for complying with different network scenarios and preserving the desired performance. An approach based on average dwell time (ADT) is utilized to establish the rule of SCPs for different scenarios. The purpose of the filtering problem is to design a resilient filter such that, under the regulation of SCPs, the dynamics of the filtering error is exponentially mean-square stable, and satisfies a prescribed disturbance attenuation level in a 2-∞ sense. A sufficient condition for the exponential mean-square stability is first derived and the 2-∞ performance is then guaranteed. In terms of certain linear matrix inequalities, the solvability of the addressed problem is discussed and the explicit expression of the desired resilient filter is also parameterized. Finally, a numerical example is provided to demonstrate the validity of the proposed design approach. © 2020 Elsevier Ltd

关键词： Linear matrix inequalities

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Resilient Multi-Dimensional Consensus in Adversarial Environment

arXiv

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arXiv 2020年

作者： Yan, Jiaqi Li, Xiuxian Mo, Yilin Wen, Changyun Department of Automation BNRist Tsinghua University Beijing China Department of Control Science and Engineering College of Electronics and Information Engineering Institute for Advanced Study Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

This paper considers the multi-dimensional consensus in networked systems, where some of the agents might be misbehaving (or faulty). Despite the influence of these misbehaviors, the benign agents aim to reach an agreement while avoiding being seriously influenced by the faulty ones. To this end, this paper first considers a general class of consensus algorithms, where each benign agent computes an "auxiliary point" based on the received values and moves its state toward this point. Concerning this generic form, we present conditions for achieving resilient consensus and obtain a lower bound on the exponential convergence rate. Assuming that the number of malicious agents is upper bounded, two specific resilient consensus algorithms are further developed based on the obtained conditions. Particularly, the first solution, based on Helly's Theorem, achieves the consensus within the convex hull formed by the benign agents' initial states, where the auxiliary point can be efficiently computed through linear programming. On the other hand, the second algorithm serves as a "built-in" security guarantee for standard average consensus algorithms, in the sense that its performance coincides exactly with that of the standard ones in the absence of faulty nodes while also resisting the serious influence of the misbehaving ones in adversarial environment. Some numerical examples are provided in the end to verify the theoretical results. © 2020, CC BY.

关键词： Linear programming

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LPV active fault-tolerant control strategy of large civil aircraft under elevator failures

LPV active fault-tolerant control strategy of large civil ai...

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CSAA/IET International Conference on Aircraft Utility systems 2020, AUS 2020

作者： Zhou, Zhiyuan Wang, Xingjian Wang, Shaoping Zhang, Yuwei Gavrilov, Alexander I. Nagamune, Ryozo School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing100191 China Ningbo Institute of Technology Beihang University Ningbo315800 China Bauman Moscow State Technical University Department of Automatic Control Systems Moscow105005 Russia Department of Mechanical Engineering The University of British Columbia VancouverBCV6T1Z4 Canada

ISBN: (纸本)9781839535635

This paper proposes a scheme to achieve fault tolerant control to elevator failure of large aircraft. Meanwhile, the model of aircraft is expressed by linear-parameter-varying (LPV), and an LPV controller is designed. The approaches of H-infinity and pole assignment using linear matrix inequalities (LMIs) tools are used to design the states feedback controller, keeping the stability, rapidity and robustness. Moreover, an additional scheduling parameter of effectiveness of the elevator is introduced to the model and controller, keeping the desired performance when elevator failure happens by the nominal controller, avoiding the retuning the controller. The simulation are shown to prove the method. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： controllers

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Pioneering the future of dentistry: AI-driven 3D bioprinting for next-generation clinical applications

Translational Dental Research

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Translational Dental Research 2025年第1期1卷

作者： Zihui Liang Xiaohong Liao Huiyi Zong Xinyao Zeng Hong Liu Congcong Wu Kavya Keremane Bed Poudel Jun Yin Kai Wang Jin Qian Huanjiang Laboratory Zhejiang University Zhuji 311800 China National Local Joint Laboratory for Advanced Textile Processing and Clean Production Wuhan Textile University Wuhan 430073 China School of New Energy and Electrical Engineering Hubei University Wuhan 430073 China Department of Materials Science and Engineering The Pennsylvania State University University Park PA 16802 USA The State Key Laboratory of Fluid Power Transmission and Control Systems School of Mechanical Engineering Zhejiang University Hangzhou 310027 China

Artificial intelligence (AI) is revolutionizing dentistry by enhancing the analysis of complex medical imaging data, automating the identification of anatomical landmarks, and improving diagnostic accuracy and treatment planning, etc. When combined with three-dimensional (3D) bioprinting, AI significantly optimizes the design and manufacturing processes, leading to highly precise and functional 3D-printed dental devices. AI-driven 3D bioprinting represents a transformative leap beyond traditional prosthetics, with the potential to create living tissues capable of regenerating oral structures. This technology pushes the boundaries of dental care by facilitating the development of biocompatible materials and complex tissue constructs that seamlessly integrate with interdisciplinary research, spanning advanced materials, future manufacturing techniques, and innovative clinical methodologies. This review examines the cutting-edge advancements in AI-assisted 3D bioprinting, focusing on its roles in tissue engineering, regenerative dentistry, and personalized treatment planning. Although many of these innovations remain in the research phase, they signify the dawn of a new era in dental medicine, where AI-driven bioprinting is poised to make the regeneration of teeth, gums, and bone structures a clinical reality. This paper provides a concise overview of these developments, emphasizing their potential to revolutionize dental practice.

关键词： Artificial intelligence 3D bioprinting Regenerative dentistry AI-driven manufacturing in prosthetics advanced materials for dental regeneration

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On PID control for synchronization of complex dynamical network with delayed nodes

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Science China(Technological Sciences) 2019年第8期62卷 1412-1422页

作者： GU HaiBo Lv JinHu LIN ZongLi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University Beijing 100083 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 USA

Over the past two decades, synchronization, as an interesting collective behavior of complex dynamical networks, has been attracting much attention. To reveal and analyze the inherent mechanism of synchronization in complex dynamical networks with time delays in nodes, this paper attempts to use PD and PI control protocols to achieve synchronization. Based on a classical network model, we investigate the PD and PI control for synchronization of complex dynamical networks with delayed nodes and obtain some sufficient conditions. By using Lyapunov functions and appropriate state transformations, we prove that global synchronization can be achieved via the above control protocols. Finally, some simulation examples are illustrated to validate the effectiveness of the proposed theoretical results.

关键词： synchronization PID control complex dynamical network networked control system delayed node

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Adaptive future frame prediction with ensemble network

arXiv

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arXiv 2020年

作者： Kim, Wonjik Tanaka, Masayuki Okutomi, Masatoshi Sasaki, Yoko Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Meguto-kuTokyo152-8550 Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Koto-ku Tokyo135-0064 Japan

Future frame prediction in videos is a challenging problem because videos include complicated movements and large appearance changes. Learning-based future frame prediction approaches have been proposed in kinds of literature. A common limitation of the existing learning-based approaches is a mismatch of training data and test data. In the future frame prediction task, we can obtain the ground truth data by just waiting for a few frames. It means we can update the prediction model online in the test phase. Then, we propose an adaptive update framework for the future frame prediction task. The proposed adaptive updating framework consists of a pre-trained prediction network, a continuous-updating prediction network, and a weight estimation network. We also show that our pre-trained prediction model achieves comparable performance to the existing state-of-the-art approaches. We demonstrate that our approach outperforms existing methods especially for dynamically changing scenes. © 2020, CC-BY-NC-ND.

关键词： Forecasting

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Learning-based human segmentation and velocity estimation using automatic labeled lidar sequence for training

arXiv

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arXiv 2020年

作者： KIM, WONJIK TANAKA, MASAYUKI OKUTOMI, MASATOSHI SASAKI, YOKO Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Meguro-ku Tokyo152-8550 Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Koto-ku Tokyo135-0064 Japan

In this paper, we propose an automatic labeled sequential data generation pipeline for human segmentation and velocity estimation with point clouds. Considering the impact of deep neural networks, state-of-the-art network architectures have been proposed for human recognition using point clouds captured by Light Detection and Ranging (LiDAR). However, one disadvantage is that legacy datasets may only cover the image domain without providing important label information and this limitation has disturbed the progress of research to date. Therefore, we develop an automatic labeled sequential data generation pipeline, in which we can control any parameter or data generation environment with pixel-wise and perframe ground truth segmentation and pixel-wise velocity information for human recognition. Our approach uses a precise human model and reproduces a precise motion to generate realistic artificial data. We present more than 7K video sequences which consist of 32 frames generated by the proposed pipeline. With the proposed sequence generator, we confirm that human segmentation performance is improved when using the video domain compared to when using the image domain. We also evaluate our data by comparing with data generated under different conditions. In addition, we estimate pedestrian velocity with LiDAR by only utilizing data generated by the proposed pipeline. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical radar

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Increasing Part Geometric Accuracy in High Speed Machining using Cascade Iterative Learning control 9

Increasing Part Geometric Accuracy in High Speed Machining u...

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9th CIRP Conference on High Performance Cutting, HPC 2020

作者： Ward, Rob Ozkirimli, Omer Jones, Bryn Industrial Doctorate Centre in Machining Science Advanced Manufacturing Research Centre with Boeing University of Sheffield Rotherham S60 5TZ United Kingdom Machining Dynamics Research Group Advanced Manufacturing Research Centre with Boeing University of Sheffield Rotherham S60 5TZ United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom

ISBN: (纸本)9781713835431

High speed machining provides high productivity and low machining cycle times. Post machining, there can exist differences between desired and measured part geometry due to tool deflection induced from higher feedrates. Reducing the feedrate leads to an increase in machining time. Using predicted drive responses on a virtual CNC with an integrated surface location error model, this research is the first time Iterative Learning control (ILC) has been applied to reduce part geometry errors from tool deflection. Validation machining trials demonstrated that the ILC scheme improved machining performance whilst maintaining machining times when compared to a baseline part program. © 2020 The Authors. Published by Elsevier B.V.

关键词： Learning algorithms

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Research on path planning of robot based on deep reinforcement learning

Research on path planning of robot based on deep reinforceme...

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第三十九届中国控制会议

作者： Feng Liu Chang Chen Zhihua Li Zhi-Hong Guan Hua O Wang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems College of Automation Huazhong University of Science and Technology Department of Mechanical Engineering Boston University

In this paper, to avoid the problem of local optimization and slow convergence in complex environment, a reinforcement learning algorithm is proposed to solve the problem. A robot path planning model is built and its feasibility is verified by simulation. In addition, this paper proposes a deep environment to neural network for robot camera to establish a deep reinforcement learning path planning model, and establishes a deep recursive Q-network（DRQN） and Deep Dueling Q-network（DDQN）respectively. In the comparison of the final simulation results, DRQN needs to consume more computation time, but can achieve better results with higher accuracy.

关键词： Reinforcement learning path planning robots deep reinforcement learning

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