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arXiv 2022年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Topology inference for networked dynamical systems (NDSs) has received considerable attention in recent years. The majority of pioneering works have dealt with inferring the topology from abundant observations of NDSs, so as to approximate the real one asymptotically. Leveraging the characteristic that NDSs will react to various disturbances and the disturbance’s influence will consistently spread, this paper focuses on inferring the topology by a few active excitations. The key challenge is to distinguish different influences of system noises and excitations from the exhibited state deviations, where the influences will decay with time and the exciatation cannot be arbitrarily large. To practice, we propose a one-shot excitation based inference method to infer h-hop neighbors of a node. The excitation conditions for accurate one-hop neighbor inference are first derived with probability guarantees. Then, we extend the results to h-hop neighbor inference and multiple excitations cases, providing the explicit relationships between the inference accuracy and excitation magnitude. Specifically, the excitation based inference method is not only suitable for scenarios where abundant observations are unavailable, but also can be leveraged as auxiliary means to improve the accuracy of existing methods. Simulations are conducted to verify the analytical results. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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Enhancing Power system Stability: A Machine Learning Approach with SVM, Random Forest and Deep Learning Utilizing Wide Area Measurements 7

Enhancing Power System Stability: A Machine Learning Approac...

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7th IEEE Student Conference on Electric Machines and systems, SCEMS 2024

作者： Song, Chao Tan, Mengcan Chu, Mingzhao Zhou, Chihan Tian, Guojie Mu, Longfei Shandong Future Intelligent Technology Co. Ltd Shandong Electrical Engineering & Equipment Group Co. Ltd Jinan China Fengtai Supply Company Distribution Operation and Maintenance Center State Grid Beijing Electric Power Company Beijing China State Grid Shandong Electric Power Company Changyi Supply Company Marketing Department Changyi China State Grid Kunming Power Supply Company Kunming China Shandong Tianyu Changtong Energy Group Co. Ltd. Qingdao China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

ISBN: (纸本)9798350366945

As the scale and complexity of modern power systems continue to expand, the importance of system stability has become increasingly critical. To ensure the stable operation of the power system, this paper proposes a power system stability analysis and optimal control strategy based on the Wide Area Measurement system (WAMS). Initially, a novel stability evaluation method is established using WAMS data and machine learning techniques, enabling effective handling of complex power system data and providing accurate stability assessments. Following this, an optimal control strategy is designed based on the evaluation method, aimed at real-time adjustments of power resource allocation to enhance system stability and responsiveness. Simulation analyses confirm the effectiveness and reliability of the proposed approach, indicating that the optimized control strategy significantly improves the system's performance under disturbances. This paper presents an innovative method for the stable operation of integrated power systems, contributing to the enhancement of power supply quality within the grid and ensuring the safe and efficient operation of the overall power system. © 2024 IEEE.

关键词： control system stability

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Fragility-Aware Stealthy Attack Strategy for Multi-Robot systems against Multi-Hop Wireless Networks

Fragility-Aware Stealthy Attack Strategy for Multi-Robot Sys...

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IEEE Conference on Decision and control

作者： Yun Gao Kai Luo Chongrong Fang Jianping He Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper proposes a fragility-aware stealthy attack for multiple mobile robots to secretly learn the accurate model of a multi-hop wireless network (MHWN). The stealthy attack scenario consists of three steps: active detection, node hijacking, and network eavesdropping. Our goal is to design distributed motion control strategies for naive attackers, for obtaining the connectivity information to evaluate the fragility degree of each node in MHWN. We designed a switching control strategy, which includes (i) move-to-fragile-neighbors-barycenter (MFNB) law and (ii) move-to-undetected-region-centroid (MURC) law. This strategy can drive mobile attackers to detect, hijack and eavesdrop on the MHWN’s nodes as quickly as possible. As a result, the fragility degree map of MHWN can be secretly learned to paralyze the whole network. Finally, the simulation results show that the proposed strategy is effective.

关键词： Wireless networks Simulation Spread spectrum communication Switches control systems Stability analysis Multi-robot systems

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Predictability of Stochastic Dynamical system: A Probabilistic Perspective

Predictability of Stochastic Dynamical System: A Probabilist...

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IEEE Conference on Decision and control

作者： Tao Xu Jianping He Dept. of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper, to characterize the predictability of discrete-time continuous-state stochastic dynamical system (SDS) and use predictability to explain the probability of making accurate predictions, we have proposed a new performance metric, i.e., predictability exponent. This metric quantifies the asymptotic exponential decaying rate of the probability that prediction errors never exceed a fixed ϵ. The novelties of this predictability metric lie in that it is proposed from an asymptotic probabilistic perspective, which i) minimizes the exponential decaying rate of prediction probability, ii) holds an approximated expression deeply related with differential entropy within an error of O(ϵ), and iii) possesses a fast converging speed from finite decaying rate of O(e −K ) in the sense of probability. Numerical examples are given to illustrate the efficiency of the obtained results.

关键词： Measurement Stochastic processes Predictive models Probabilistic logic Entropy Dynamical systems

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Inferring Topology of Networked Dynamical systems by Active Excitations

Inferring Topology of Networked Dynamical Systems by Active ...

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IEEE Conference on Decision and control

作者： Yushan Li Jianping He Cailian Chen Xinping Guan Dept. of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Topology inference for networked dynamical systems (NDSs) has received considerable attention in recent years. The majority of pioneering works have dealt with inferring the topology from abundant observations of NDSs, so as to approximate the real one asymptotically. Leveraging the characteristic that NDSs will react to various disturbances and the disturbance’s influence will consistently spread, this paper focuses on inferring the topology by a few active excitations. The key challenge is to distinguish different influences of system noises and excitations from the exhibited state deviations, where the influences will decay with time and the excitation cannot be arbitrarily large. To practice, we propose a one-shot excitation based inference method to infer h-hop neighbors of a node. The excitation conditions for accurate one-hop neighbor inference are first derived with probability guarantees. Then, we extend the results to h-hop neighbor inference and multiple excitations cases, providing the explicit relationships between the inference accuracy and excitation magnitude. Specifically, the excitation based inference method is not only suitable for scenarios where abundant observations are unavailable, but also can be leveraged as auxiliary means to improve the accuracy of existing methods. Simulations are conducted to verify the analytical results.

关键词： Analytical models Network topology Topology Performance analysis Dynamical systems Testing

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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controller Synthesis of Signal Temporal Logical Tasks for Cyber-Physical Production systems via Acyclic Decomposition

Controller Synthesis of Signal Temporal Logical Tasks for Cy...

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IEEE Conference on Decision and control

作者： Shuling Wang Shanying Zhu Cailian Chen Liang Xu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Institute of Artificial Intelligence Shanghai University Shanghai China

Modularization facilitates the adaptability of cyber-physical production systems (CPPSs) with a variety of collaborative tasks. Various production rules can be captured by signal temporal logic (STL) specifications imposed on interconnected multi-agent systems (MASs). In this paper, we focus on the controller synthesis of STL tasks for interconnected MASs to accomplish collaborative tasks in modular CPPSs. Firstly, a class of STL specifications characterizing tasks by the combination of fixed-time reachability and finite-time persistence tasks is proposed, which encompasses a large class of production specifications for the MAS. Secondly, the acyclic decomposition of the global STL formula is constructed to enable conflict-free collaborative tasks and unidirectional couplings between subsystems. By establishing the equivalence between the proposition and the state set of the MAS, necessary and sufficient conditions are respectively proposed for the satisfaction of reachability and persistence tasks. In addition, an algorithm is presented to synthesize controllers for the MAS with the global STL specification based on local controllers of subsystems. An illustrative example is given to show the effectiveness of the proposed method.

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Impulsive Formation control of Nonlinear Leader-Following Multi-agent systems with Input Saturation*

Impulsive Formation Control of Nonlinear Leader-Following Mu...

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Industrial Cyber-Physical systems (ICPS)

作者： Ni Zhang Xiaowei Jiang Xianhe Zhang Le You School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

Leader-following formation analysis problem for a second-order nonlinear multi-agent system(MAS) with input saturation is investigated in this paper. And the impulsive formation control algorithm is introduced in the designed protocol which only works at the impulse times. Owing to the real-world limited communication channels, input saturation is considered in the impulsive controller. Furthermore, based on Lyapunov stability theories, Kronecker properties, eigenvalue and so on, some sufficient conditions that guarantee the leader-following consensus of MAS are obtained. Lastly, several simulations are worked out to verify the correctness and effectiveness of the theoretical results.

关键词： Sufficient conditions Protocols Simulation Communication channels Cyber-physical systems control systems Formation control

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Evaluating Image Quality of DIBR-synthesized Views in Texture and Depth Domains

Evaluating Image Quality of DIBR-synthesized Views in Textur...

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2021 China Automation Congress, CAC 2021

作者： Wang, Guangcheng Guo, Nan Liu, Hongyan Zhang, Yonghui Zhou, Yazhen Gu, Ke Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China

ISBN: (纸本)9781665426473

Accurately predicting the quality of depth-image-based-rendering (DIBR) synthesized images is of great significance in promoting DIBR techniques. Recently, Many DIBR-synthesized image quality assessment (IQA) algorithms based on quantifying the distortion existed in texture images have been proposed. However, these methods ignore the damage of DIBR algorithms on the depth structure of DIBR-synthesized images and thus fail to evaluate the visual quality of DIBR-synthesized images accurately. To this end, we present a DIBR-synthesized image quality assessment metric with multi-modal information (i.e. integrating texture and depth information), dubbed as MMI. MMI predicts the quality of DIBR-synthesized images by jointly measuring the texture structure and depth structure of the synthesized image. The design principle of our MMI is that the local geometric distortion, introduced by DIBR techniques in the hole-filling process, destroys the texture structure and the depth structure of DIBR-synthesized images. Thus, we can accurately evaluate DIBR-synthesized image quality by a joint representation of texture structure and depth structure. Experiments show that our MMI is better than the competing state-of-the-art IQA algorithms in predicting DIBR-synthesized image quality. © 2021 IEEE

关键词： Textures

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Remote Sensing Inversion for River Turbidity Estimation Based on Noise Injection and Ensemble Learning

Remote Sensing Inversion for River Turbidity Estimation Base...

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2021 China Automation Congress, CAC 2021

作者： Zhou, Yazhen Shi, Shuang Liu, Hongyan Zhang, Yonghui Gu, Ke Qiao, Junfei Faculty of Information Technology Beijing University of Technology Beijing 100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute

ISBN: (纸本)9781665426473

Turbidity is one main indicator of quality typically used for monitoring water pollution and water eutrophication. The monitoring technology of river water quality can be divided into contact monitoring technology based on physical sensors and non-contact monitoring technology based on remote sensing satellites. The traditional contact monitoring technology has limited monitoring spatial resolution. As a new technology, the remote sensing monitoring technology of the river can conveniently solve the above-mentioned shortcomings and realize the coverage of the whole river basin. However, due to the scarcity of remote sensing data and the measurement errors of remote sensing satellites themselves, learning-based turbidity measurement methods have weak generalization ability and thus are easily prone to produce large deviations. To effectively address such difficulty, a noise injection method is introduced into a river turbidity measurement model based on ensemble learning. To be specific, we randomly generate many noise sets and add them to the remote sensing data to make it closer to the real application scene, and then adopt the ensemble learning model to estimate river turbidity. Experiments validate that the random noise injection strategy can effectively improve the generalization performance of river turbidity measurement. © 2021 IEEE

关键词： Rivers

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