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Prescribed Performance control of One-DOF Link Manipulator With Uncertainties and Input Saturation Constraint

IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 148-157页

作者： Yang Yang Jie Tan Dong Yue IEEE the College of Automation and the College of Artificial Intelligence Nanjing University of Posts and Telecommunications the Institute of Advanced Technology and Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing University of Posts and Telecommunications

In this paper, we mainly address the position control problem for one-degree of freedom(DOF) link manipulator despite uncertainties and the input saturation via the backstepping technique, active disturbance rejection control(ADRC) as well as predefined tracking performance functions. The extended state observer(ESO) is employed to compensate uncertain dynamics and disturbances, and it does not rely on the accurate model of systems. The tracking differentiator(TD) is utilized to substitute the derivative of the virtual control signals, and the explosion of complexity caused by repeated differentiations of nonlinear functions is removed. The auxiliary system is used to deal with the control input limitation, and the tracking accuracy and speed are improved by predefined tracking performance *** the help of the input-to-state stability(ISS) and Lyapunov stability theories, it is proven that the tracking error can be gradually converged into arbitrarily small neighborhood of the origin, and the tracking error is adjusted by suitable choice of control parameters. The simulation results are presented for the verification of the theoretical claims.

关键词： active disturbance rejection control (ADRC) auxiliary system backstepping technique input saturation predefined tracking performance function

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Distributed Tracking control of a Class of Multi-agent Systems in Non-affine Pure-feedback Form Under a Directed Topology

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 169-180页

作者： Yang Yang Dong Yue College of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China Institute of Advanced Technology and Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing University of Posts and Telecommunications Nanjing 210023 China IEEE

In this paper, we consider a consensus tracking problem of a class of networked multi-agent systems(MASs)in non-affine pure-feedback form under a directed topology. A distributed adaptive tracking consensus control scheme is constructed recursively by the backstepping method, graph theory,neural networks(NNs) and the dynamic surface control(DSC)approach. The key advantage of the proposed control strategy is that, by the DSC technique, it avoids "explosion of complexity"problem along with the increase of the degree of individual agents and thus the computational burden of the scheme can be drastically reduced. Moreover, there is no requirement for prior knowledge about system parameters of individual agents and uncertain dynamics by employing NNs approximation *** then further show that, in theory, the designed control policy guarantees the consensus errors to be cooperatively semi-globally uniformly ultimately bounded(CSUUB). Finally, two examples are presented to validate the effectiveness of the proposed control strategy.

关键词： Backstepping consensus multi-agent systems(MASs) neural networks(NNs)

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Event-trigger-based consensus secure control of linear multi-agent systems under DoS attacks over multiple transmission channels

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Science China(Information Sciences) 2020年第5期63卷 105-118页

作者： Yang YANG Yanfei LI Dong YUE College of Automation & College of Artificial Intelligence Nanjing University of Posts and Telecommunications Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing University of Posts and Telecommunications

This paper proposes a consensus secure control scheme in the presence of denial-of-service(DoS)attacks based on an event-trigger mechanism. In contrast to a scenario in which attacks are the same and simultaneously paralyze all channels, the DoS attack addressed in this paper occurs aperiodically and results in the independent interruption of multiple transmission channels. A sufficient condition for the attack duration is designed and a distributed event-triggered control scheme is proposed, where the updated instants are triggered aperiodically to reduce the required communication resources. It is shown that the overall system is stable with the proposed scheme according to the Lyapunov stability theory and that Zeno behavior is excluded. Finally, a numerical example is presented to verify the effectiveness of the proposed scheme.

关键词： multi-agent system event-triggered control consensus secure control denial-of-service attack multiple transmission channels

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Memory-efficient GPU-based exact and parallel triangle counting in large graphs 21

Memory-efficient GPU-based exact and parallel triangle count...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Ouyang, Zhiyou Wu, Shanni Zhao, Tongtong Yue, Dong Zhang, Tengfei Institute of Advanced Technology School of Automation Nanjing University of Posts and Telecommunications Nanjing210003 China School of Computer Nanjing University of Posts and Telecommunications Nanjing China Institute of Advanced Technology School of Automation Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing University of Posts and Telecommunications Nanjing China School of Automation Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728120584

Triangle counting is one of the basic research topics in many practical problems, such as clustering coefficients, transitivity ratio and traffic network complexity, etc., all of which can be converted into triangle counting issues. In this paper, a memory efficient GPU-based parallel triangle counting algorithm is proposed for large undirected graphs, which based on Polak's parallel forward triangle counting algorithm and improved the algorithm flow, edges sorting and triangle counting kernel function to increase the memory usage efficiency and run time performance. Algorithm comparison with the description of proposed algorithm steps and the implementation details is also introduced in the paper. Performance evaluations show that the proposed algorithm achieves 5 to 10 times speedup over the existing parallel forward algorithm, moreover, the GPU memory usage is reduced to a maximum of 50% compared to the existing algorithm. © 2019 IEEE.

关键词： Graphics processing unit

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A Very-Short-Term Online PV Power Prediction Model Based on RAN With Secondary Dynamic Adjustment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1214-1224页

作者： Zhang, Tengfei Ma, Fumin Peng, Chen Yu, Yang Yue, Dong Dou, Chunxia O'Hare, Gregory M. P. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence The Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing210023 China Nanjing University of Finance and Economics College of Information Engineering Nanjing210023 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200072 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210023 China Trinity College Dublin School of Computer Science and Statistics Dublin 2 Ireland

Photovoltaic (PV) power is progressively being subsumed into power grids. As a consequence, reliable PV power forecasting has become essential in order to ensure the optimal functioning of the power grid. Neural networks remain the dominant prediction model utilized. Conventional neural network forecasting models are wholly dependent upon offline data. Subsequent to offline training, no further structural adjustments can be made during the forecasting process, which therefore they fail to cater for PV power supply fluctuations that are fundamentally dynamic. To address this failing, this article proposes a very-short-term online prediction model based on a resource-allocating network (RAN) incorporating a secondary dynamic adjustment. The RAN is initially trained offline to obtain a basic forecasting model. Thereafter, in an online prediction process, those samples with large prediction errors that exceed a preset value are recorded in a specific buffer. When the set conditions are triggered, the secondary dynamic adjustment strategy is employed, which enables the online prediction model to effectively relearn previously unmodeled samples while shielding external interference. Experimental results obtained from actual testing demonstrate the validity of the secondary dynamic adjustment strategy for online learning while also providing higher accuracy levels from the prediction model. © 2020 IEEE.

关键词： Forecasting

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Ubiquitous Sensing Intelligent Gateway for Power IoT based on IEC61850 Standard

Ubiquitous Sensing Intelligent Gateway for Power IoT based o...

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第33届中国控制与决策会议

作者： Zhikang Ji Tengfei Zhang Kuihan Chen Jiahong Chen Yi Wang Yirui Zhang College of Automation & Artifical Intelligence Nanjing University of Posts and Telecommunications Jiangsu Engineering Lab.of Big Data Analysis & Control for Active Distribution Network

In view of the fact that existing equipment in the distribution network generally does not support IEC61850,a conversion gateway supporting IEC61850 is *** gateway can simultaneously access the traditional protocols used in the field of multiple substations and convert them to the IEC61850 international *** gateway system uses a protocol conversion mechanism based on shared *** gateway supports a variety of different interfaces and protocols,enabling the equipment of the traditional substation system to be compatible with the IEC61850 *** can output standard interfaces to realize the collection of the operating status and parameters of heterogeneous devices and different protocols,and can be used as a core gateway device for constructing the perception layer of a general power Internet of Things system.

关键词： IEC61850 Power Internet of things Intelligent Gateway Protocol conversion

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Adaptive neural output consensus control of stochastic nonlinear strict-feedback multi-agent systems

Adaptive neural output consensus control of stochastic nonli...

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2018 Australian and New Zealand control Conference, ANZCC 2018

作者： Yang, Yang Miao, Songtao Xu, Chuang Yue, Dong Tan, Jie Tian, Yu-Chu College of Automation College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing University of Posts and Telecommunications Nanjing China School of Electrical Engineering and Computer Science Queensland University of Technology BrisbaneQLD4001 Australia

ISBN: (纸本)9781538666173

An adaptive neural output consensus control issue is considered for stochastic nonlinear strict-feedback multi-agent systems (MASs). The traditional backstepping framework is employed combing with the graph theory, as well as neural networks (NNs) technology. NNs are utilized for the approximation of unknown functions, and the Itô's lemma is used to deal with stochastic dynamics of the system. It is proved that all signals remain bounded in probability and that the tracking errors of all followers converge to a small neighborhood of the origin in the sense of mean quartic value by suitable choice of parameters. A simulation example is provided. © 2018 IEEE.

关键词： Multi agent systems

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Learning robust and discriminative low-rank representations for face recognition with occlusion

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Pattern Recognition 2017年 66卷 129-143页

作者： Guangwei Gao Jian Yang Xiao-Yuan Jing Fumin Shen Wankou Yang Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Laboratory of Big Data Analysis and Control for active distribution network Nanjing University of Posts and Telecommunications Nanjing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Automation Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Southeast University Nanjing China

For robust face recognition tasks, we particularly focus on the ubiquitous scenarios where both training and testing images are corrupted due to occlusions. Previous low-rank based methods stacked each error image into a vector and then used L 1 or L 2 norm to measure the error matrix. However, in the stacking step, the structure information of the error image can be lost. Depart from the previous methods, in this paper, we propose a novel method by exploiting the low-rankness of both the data representation and each occlusion-induced error image simultaneously, by which the global structure of data together with the error images can be well captured. In order to learn more discriminative low-rank representations, we formulate our objective such that the learned representations are optimal for classification with the available supervised information and close to an ideal-code regularization term. With strong structure information preserving and discrimination capabilities, the learned robust and discriminative low-rank representation (RDLRR) works very well on face recognition problems, especially with face images corrupted by continuous occlusions. Together with a simple linear classifier, the proposed approach is shown to outperform several other state-of-the-art face recognition methods on databases with a variety of face variations.

关键词： Face recognition Low-rank matrix recovery Nuclear norm

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