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第33届中国控制与决策会议

作者： Hanru Tang Junguo Lu Dong Yang Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China China Academy of Space Technology

This paper investigates the problems of robust stability and stabilization for fractional-order systems with polytopic *** is assumed that the fractional-order α is a known constant and belongs to 0 <α <***,based on the H bounded real lemma for commensurate fractional-order control systems,a sufficient condition for the above stability problem is established in terms of linear matrix inequalities（LMIs）.Secondly,on the foundation of this condition,sufficient LMI methods for the design of stabilizing controller are obtained for two cases where the polytopic coefficients are known and *** the case of unknown polytopic coefficients,by introducing the additional matrices,the state matrix and the positive-definite Hermitian matrices are *** the case of known polytopic coefficients,by introducing parameter-dependent matrices,a less-conservative robust H stabilization condition is ***,two different numerical examples are provided to compare the conservatism between the existing results and the results in this paper.

关键词： fractional-order systems robust stability robust stabilization robust H_∞ control convex polytopic uncertainties

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A GM-TCN based Hybrid Prediction Method for Ash Fouling Prediction in Coal-fired Power Plant Boiler

A GM-TCN based Hybrid Prediction Method for Ash Fouling Pred...

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Chinese Automation Congress (CAC)

作者： Yuanhao Shi Tianxiang Han Fangshu Cui Jie Wen Gangzhu Qiao School of Electrical and Control Engineering North University of China Taiyuan China Department of Automation Ministry of Education of China Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Shanghai China School of Data Science and Technology North University of China Taiyuan China

In response to the problem that ash deposition in boilers has a great impact on production efficiency as well as production safety, a grey level theory prediction model based on time convolution networks (GM-TCN) is proposed to predict the degree of ash in boiler heated areas by using a cleaning factor to assess the ash deposition condition on the heated surfaces. The accuracy of the prediction model is improved by utilizing the features of grey level theory that can predict the developmental changes in system behavior, combined with the high-precision approximation capability of the time-convolutional network. The modelling prediction of the ash level in the heated area of boilers during the production of coal-fired power stations is investigated. Experiments demonstrate that the model can predict the cleanliness factor with high accuracy, and the predicted values are closest to the true values compared to the other two models, verifying the accuracy and reliability of the model.

关键词： Cogeneration Time series analysis Ash Production Predictive models Boilers Data models

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An Accelerated Gossip-Based Distributed Gradient Method

An Accelerated Gossip-Based Distributed Gradient Method

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第40届中国控制会议

作者： Xiaoxing Ren Dewei Li Yugeng Xi Haibin Shao Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai Engineering Research Center of Intelligent Control and Management

This paper studies distributed optimization over the multi-agent network. We develop and analyze a novel accelerated distributed gradient descent method, termed as G-DGDlm, for gossip communication protocol. G-DGDlm uses gradient tracking technique and local memory. Specifically, each agent stores two past variables, namely, an estimate of the average gradient and an estimate of the optimal solution. G-DGDlm achieves linear convergence for strongly convex and smoot√h functions when the related works, we remove the dependence of θ on global parameters and G-DGDlm is applicable to gossip communication protocol. Numerical experiments of distributed estimation in sensor networks show the faster convergence speed of G-DGDlm in comparison with the state-of-the-art methods.

关键词： Distributed convex optimization gossip communication first-order methods

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Bearing-only Formation control of Nonholonomic Agents with Unknown Dynamic Leaders

Bearing-only Formation Control of Nonholonomic Agents with U...

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Chinese Automation Congress (CAC)

作者： Haifan Su Ziwen Yang Shanying Zhu Cailian Chen Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665426480

In this paper, the problem of bearing-only formation tracking control of multiple nonholonomic agents is studied. A leader-follower structure is adopted, where the leaders are moving at an unknown constant linear velocity and time-varying orientation. To solve the problem using bearing-only measurements, a finite-time estimator is firstly proposed to estimate the leaders’ orientation information. Then, an adaptation law for estimating the leaders’ linear velocity is proposed, based on which control inputs for tracking the leaders in a predefined bearing-based formation are designed, so that formation tracking can be achieved asymptotically. Rigorous theoretical analysis of the closed-loop system is given. It is proved that both the estimation errors and formation errors converge to zero under the connectivity of the communication graph and conditions for bearing rigidity. Numerical simulations are presented to testify the effectiveness of the proposed method.

关键词： Estimation error Automation Simulation Numerical simulation Rigidity Velocity measurement Closed loop systems

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Event-Triggered Consensus on Matrix-weighted Networks

Event-Triggered Consensus on Matrix-weighted Networks

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Chinese Automation Congress (CAC)

作者： Lulu Pan Chongzhi Wang Haibin Shao Dewei Li Yugeng Xi 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: (纸本)9781665426480

In this work, we consider the consensus problem of the multi-agent system over matrix-weighted networks using an event-triggered mechanism. An event-triggered coordination strategy is proposed to steer this generalized category of multi-agent systems towards the bipartite consensus state. The triggering condition is periodically checked and distributed in the sense that the event detector only uses neighbors’ information and local computation at discrete sampling instants. Moreover, it is shown that the triggering condition is closely related to the spectrum of matrix-valued weights corresponding to the edges in the matrix-weighted network. In the meantime, the Zeno phenomenon can be avoided automatically under the proposed event trigger approach.

关键词： Automation Image edge detection Detectors Eigenvalues and eigenfunctions Multi-agent systems

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Bearing-based Formation Tracking of Nonholonomic Agents in the Leader’s Local Frame

Bearing-based Formation Tracking of Nonholonomic Agents in t...

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Chinese Automation Congress (CAC)

作者： Ke Lv Ziwen Yang Shanying Zhu Lin Wang Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9781665426480

This paper investigates the formation tracking control problem of nonholonomic agents in the leader’s local frame, based on bearing measurements. Most of existing works on formation tracking are aimed at tracking a given formation in the global frame, which cannot meet the needs of many applications such as a fleet of cars driving on the highway. In this case, if the cars cannot keep a fixed formation in the leader’s local frame, they may drive off the highway. Thus we propose an integrated approach consisting of a bearing-based estimator and a tracking controller, to track a given formation in the leader’s local frame. In order to eliminate the estimation error in formation tracking, a persistently exciting leader is characterized by conditions. The stability of the integrated method is proved rigorously. Simulation results prove the effectiveness of the proposed approach.

关键词： Road transportation Estimation error Automation Simulation Stability analysis Robustness Trajectory

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Query Attack by Multi-Identity Surrogates

arXiv

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arXiv 2021年

作者： Chen, Sizhe Huang, Zhehao Tao, Qinghua Huang, Xiaolin Department of Automation The Institute of Medical Robotics Shanghai Jiao Tong University China MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China ESAT-STADIUS KU Leuven Belgium

Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversarial attacks, while the existing black-box attacks require extensive queries on the victim DNN to achieve high success rates. For query-efficiency, surrogate models of the victim are used to generate transferable Adversarial Examples (AEs) because of their Gradient Similarity (GS), i.e., surrogates’ attack gradients are similar to the victim’s ones. However, it is generally neglected to exploit their similarity on outputs, namely the Prediction Similarity (PS), to filter out inefficient queries by surrogates without querying the victim. To jointly utilize and also optimize surrogates’ GS and PS, we develop QueryNet, a unified attack framework that can significantly reduce queries. QueryNet creatively attacks by multi-identity surrogates, i.e., crafts several AEs for one sample by different surrogates, and also uses surrogates to decide on the most promising AE for the query. After that, the victim’s query feedback is accumulated to optimize not only surrogates’ parameters but also their architectures, enhancing both the GS and the PS. Although QueryNet has no access to pre-trained surrogates’ prior, it reduces queries by averagely about an order of magnitude compared to alternatives within an acceptable time, according to our comprehensive experiments: 11 victims (including two commercial models) on MNIST/CIFAR10/ImageNet, allowing only 8-bit image queries, and no access to the victim’s training data. The code is available at https://***/Sizhe-Chen/QueryNet. Impact Statement—Deep Neural Networks (DNNs) have been validated as vulnerable, while they are much harder to attack in real-world black-box settings. Existing methods require hundreds of queries on the victim DNN to perform an effective attack. By proposing a new perspective that gradient and prediction similarity are fundamentally different, this paper fosters the query-based attackers via stealing the victim model using neural archite

关键词： Deep neural networks

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Global Update Guided Federated Learning

arXiv

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arXiv 2022年

作者： Wu, Qilong Liu, Lin Xue, Shibei Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Electronic Information Engineering Shanghai Dianji University Shanghai201306 China

Federated learning protects data privacy and security by exchanging models instead of data. However, unbalanced data distributions among participating clients compromise the accuracy and convergence speed of federated learning algorithms. To alleviate this problem, unlike previous studies that limit the distance of updates for local models, we propose global-update-guided federated learning (FedGG), which introduces a model-cosine loss into local objective functions, so that local models can fit local data distributions under the guidance of update directions of global models. Furthermore, considering that the update direction of a global model is informative in the early stage of training, we propose adaptive loss weights based on the update distances of local models. Numerical simulations show that, compared with other advanced algorithms, FedGG has a significant improvement on model convergence accuracies and speeds. Additionally, compared with traditional fixed loss weights, adaptive loss weights enable our algorithm to be more stable and easier to implement in practice. Copyright © 2022, The Authors. All rights reserved.

关键词： Data privacy

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Distributed MPC Algorithm for Linear Discrete systems with Communication Noise

Distributed MPC Algorithm for Linear Discrete Systems with C...

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第40届中国控制会议

作者： Duqiao Zhao Ding Liu Linxiong Liu Xia Zhang National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xian University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

In this paper, a distributed MPC algorithm is proposed for linear discrete systems with bounded communication noise and random communication failure. Based on the characteristics of time-varying directed graphs, the convergence of the algorithm is analyzed in the case of communication noise and random communication failure. This algorithm uses the distributed projection dual subgradient algorithm to solve the dual problem and replaces the double stochastic condition of the traditional algorithm with row stochastic condition. The exponential stability of the system is guaranteed. Finally, an example is given to illustrate the performance of the algorithm.

关键词： Distributed MPC Bounded Communication Noise Random Communication Faults Distributed Projection Dual Subgradient Row Stochastic

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Model-free False Data Injection Attack in Networked control systems: A Feedback Optimization Approach

arXiv

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arXiv 2022年

作者： Luo, Xiaoyu Fang, Chongrong He, Jianping Zhao, Chengcheng Paccagnan, Dario Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China Department of Computing Imperial College London LondonSW7 2AZ United Kingdom

Security issues have gathered growing interest within the control systems community, as physical components and communication networks are increasingly vulnerable to cyber attacks. In this context, recent literature has studied increasingly sophisticated false data injection attacks, with the aim to design mitigative measures that improve the systems’ security. Notably, data-driven attack strategies – whereby the system dynamics is oblivious to the adversary – have received increasing attention. However, many of the existing works on the topic rely on the implicit assumption of linear system dynamics, significantly limiting their scope. Contrary to that, in this work we design and analyze truly model-free false data injection attack that applies to general linear and nonlinear systems. More specifically, we aim at designing an injected signal that steers the output of the system toward a (maliciously chosen) trajectory. We do so by designing a zeroth-order feedback optimization policy and jointly use probing signals for real-time measurements. We then characterize the quality of the proposed model-free attack through its optimality gap, which is affected by the dimensions of the attack signal, the number of iterations performed, and the convergence rate of the system. Finally, we extend the proposed attack scheme to the systems with internal noise. Extensive simulations show the effectiveness of the proposed attack scheme. Copyright © 2022, The Authors. All rights reserved.

关键词： Networked control systems

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