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Event-triggered Consensus control of Heterogeneous Multi-agent systems: Model- and Data-based Analysis

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arXiv 2022年

作者： Wang, Xin Sun, Jian Wang, Gang Chen, Jie The School of Automation The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

This article deals with model- and data-based consensus control of heterogenous leader-following multi-agent systems (MASs) under an event-triggering transmission scheme. A dynamic periodic transmission protocol is developed to significantly alleviate the transmission frequency and computational burden, where the followers can interact locally with each other approaching the dynamics of the leader. Capitalizing on a discrete-time looped-functional, a model-based consensus condition for the closed-loop MASs is derived in form of linear matrix inequalities (LMIs), as well as a design method for obtaining the distributed controllers and event-triggering parameters. Upon collecting noise-corrupted state-input measurements during open-loop operation, a data-driven leader-following MAS representation is presented, and employed to solve the data-driven consensus control problem without requiring any knowledge of the agents' models. This result is then extended to the case of guaranteeing an H∞ performance. A simulation example is finally given to corroborate the efficacy of the proposed distributed event-triggering scheme in cutting off data transmissions and the data-driven design method. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations

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arXiv 2022年

作者： Liu, Qi Yang, Bo Wang, Zhaojian Zhu, Dafeng Wang, Xinyi Ma, Kai Guan, Xinping The 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 Electrical Engineering Yanshan University Qinhuangdao066004 China

Due to the different losses caused by various photovoltaic (PV) array faults, accurate diagnosis of fault types is becoming increasingly important. Compared with a single one, multiple PV stations collect sufficient fault samples, but their data is not allowed to be shared directly due to potential conflicts of interest. Therefore, federated learning can be exploited to train a collaborative fault diagnosis model. However, the modeling efficiency is seriously affected by the model update mechanism since each PV station has a different computing capability and amount of data. Moreover, for the safe and stable operation of the PV system, the robustness of collaborative modeling must be guaranteed rather than simply being processed on a central server. To address these challenges, a novel asynchronous decentralized federated learning (ADFL) framework is proposed. Each PV station not only trains its local model but also participates in collaborative fault diagnosis by exchanging model parameters to improve the generalization without losing accuracy. The global model is aggregated distributedly to avoid central node failure. By designing the asynchronous update scheme, the communication overhead and training time are greatly reduced. Both the experiments and numerical simulations are carried out to verify the effectiveness of the proposed method. © 2022, CC BY.

关键词： Failure analysis

Submodularity-based False Data Injection Attack Scheme in Multi-agent Dynamical systems

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arXiv 2022年

作者： Luo, Xiaoyu Zhao, Chengcheng Fang, Chongrong He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China The Department of Electrical and Computer Engineering University of Victoria BC Canada

Consensus in multi-agent dynamical systems is prone to be sabotaged by the adversary, which has attracted much attention due to its key role in broad applications. In this paper, we study a new false data injection (FDI) attack design problem, where the adversary with limited capability aims to select a subset of agents and manipulate their local multi-dimensional states to maximize the consensus convergence error. We first formulate the FDI attack design problem as a combinatorial optimization problem and prove it is NP-hard. Then, based on the submodularity optimization theory, we show the convergence error is a submodular function of the set of the compromised agents, which satisfies the property of diminishing marginal returns. In other words, the benefit of adding an extra agent to the compromised set decreases as that set becomes larger. With this property, we exploit the greedy scheme to find the optimal compromised agent set that can produce the maximum convergence error when adding one extra agent to that set each time. Thus, the FDI attack set selection algorithms are developed to obtain the near-optimal subset of the compromised agents. Furthermore, we derive the analytical suboptimality bounds and the worst-case running time under the proposed algorithms. Extensive simulation results are conducted to show the effectiveness of the proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

Abstraction-Based Verification of Approximate Pre-Opacity for control systems

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arXiv 2022年

作者： Hou, Junyao Liu, Siyuan Yin, Xiang Zamani, Majid Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University China Department of Electrical and Computer Engineering Technical University of Munich Germany Department of Computer Science LMU Munich Germany Department of Computer Science University of Colorado Boulder United States

In this paper, we consider the problem of verifying pre-opacity for discrete-time control systems. Preopacity is an important information-flow security property that secures the intention of a system to execute some secret behaviors in the future. Existing works on pre-opacity only consider non-metric discrete systems, where it is assumed that intruders can distinguish different output behaviors precisely. However, for continuous-space control systems whose output sets are equipped with metrics (which is the case for most real-world applications), it is too restrictive to assume precise measurements from outside observers. In this paper, we first introduce a concept of approximate pre-opacity by capturing the security level of control systems with respect to the measurement precision of the intruder. Based on this new notion of pre-opacity, we propose a verification approach for continuous-space control systems by leveraging abstraction-based techniques. In particular, a new concept of approximate pre-opacity preserving simulation relation is introduced to characterize the distance between two systems in terms of preserving pre-opacity. This new system relation allows us to verify pre-opacity of complex continuous-space control systems using their finite abstractions. We also present a method to construct pre-opacity preserving finite abstractions for a class of discrete-time control systems under certain stability assumptions. Copyright © 2022, The Authors. All rights reserved.

关键词： Opacity

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

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

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

A Two-layer Adaptive control Strategy for Photovoltaic Power in Different Scenarios

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A Two-layer Adaptive Control Strategy for Photovoltaic Power...

Energy engineering and Power systems (EEPS), International Conference on

作者： Yanghui Wan Jiahao Yan Tianfeng Chu Yaping Li Kun Yuan Kaifeng Zhang Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Southeast University Nanjing China Power Automation Department China Electric Power Research Institute(Nanjing) Nanjing China Electric Power Research Institute State Grid Liaoning Electric Power Co. Ltd. Shenyang China

Currently, control strategies for distributed photovoltaic (PV) power generation are relatively single and rigid, and cannot flexibly adapt to the diverse demands of power grids in different scenarios. Firstly, this paper analyzes the adaptive relationship between different dispatching demands, the regulation ability of distributed PV, and the characteristics of the local voltage. Secondly, for different scenarios, the coordinated controller determines the reference values of active power and reactive power given to the inverter. Thus, distributed PV power control modes which can adapt to different scenarios have been established. Then, these control modes can be classified into single control modes and compound control modes according to the complexity of scenario demands. On this basis, a specific two-layer adaptive control strategy and process of distributed PV power generation in a low-voltage distribution network are further proposed, which takes into account both external demands of the distribution network and the regulation ability of distributed PV. Finally, a case study is given to verify the rationality and effectiveness of the two-layer adaptive scenarios control strategy. This strategy can effectively improve the comprehensive support ability of distributed PV power generation towards the power grid in multiple scenarios, and improve the stable operation level of the regional power grid.

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Bidirectional Pricing and Demand Response for Nanogrids with HVAC systems

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arXiv 2022年

作者： Cao, Jiaxin Yang, Bo Zhu, Shanying Ma, Kai Guan, Xinping The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The School of Electrical Engineering Yanshan University Qinhuangdao066004 China

Owing to the fluctuant renewable generation and power demand, the energy surplus or deficit in each nanogrid is embodied differently across time. To stimulate local renewable energy consumption and minimize the long-term energy cost, some issues still remain to be explored: when and how the energy demand and bidirectional trading prices are scheduled considering personal comfort preferences and environmental factors. For this purpose, the demand response and two-way pricing problems concurrently for nanogrids and a public monitoring entity (PME) are studied with exploiting the large potential thermal elastic ability of heating, ventilation and air-conditioning (HVAC) units. Different from nanogrids, in terms of minimizing time-average costs, PME aims to set reasonable prices and optimize profits by trading with nanogrids and the main grid bi-directionally. In particular, such bilevel energy management problem is formulated as a stochastic form in a long-term horizon. Since there are uncertain system parameters, time-coupled queue constraints and the interplay of bilevel decision-making, it is challenging to solve the formulated problems. To this end, we derive a form of relaxation based on Lyapunov optimization technique to make the energy management problem tractable without forecasting the related system parameters. The transaction between nanogrids and PME is captured by a one-leader and multi-follower Stackelberg game framework. Then, theoretical analysis of the existence and uniqueness of Stackelberg equilibrium (SE) is developed based on the proposed game property. Following that, we devise an optimization algorithm to reach the SE with less information exchange. Numerical experiments validate the effectiveness of the proposed approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Costs

Intelligent intersection scheduling based on vehicle traffic priority

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Intelligent intersection scheduling based on vehicle traffic...

第40届中国控制会议

作者： Zhengxin Ruan Sicong Chen Yuanlong Li Liangren Shi Department 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

This paper proposes an intelligent intersection management method based on the vehicle dynamic priority. By the information interaction between the autonomous vehicles（AVs） and the infrastructure equipment, the vehicle position and posture information is obtained in real time, and the priority of the vehicles passing through the conflict points in the scene is determined according to the order of the vehicles driving through the crossroads. Subsequently, to track the planned trajectory, we use the model predictive control（MPC） algorithm minimize the intersection delay with collision-free constraints. This guarantees the vehicles to drive through the intersection safely in the scene. Simulation experiments demonstrate the reliability and efficiency of the proposed algorithm.

关键词： Autonomous vehicle Intersection management system Vehicle-to-infrastructure communication Dynamic priority Model predictive control

An Accelerated Gossip-Based Distributed Gradient Method

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An Accelerated Gossip-Based Distributed Gradient Method

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