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distributed Learning and Coordination in Cognitive Infrastructureless Networks of Unknown Size

IEEE SYSTEMS JOURNAL 2020年第2期14卷 2085-2096页

作者： Kumar, Rohit Darak, Sumit J. Hanawal, Manjesh K. Yadav, Ankit Natl Inst Technol Delhi Dept Elect & Commun Engn New Delhi 110040 India Indian Inst Technol Delhi Dept Elect & Commun Engn New Delhi 110020 India Indian Inst Technol Ind Engn & Operat Res Bombay 400076 Maharashtra India Texas A&M Univ Dept Elect Engn College Stn TX 77843 USA

The cognitive infrastructureless network has received significant attention to reduce signaling load in next-generation networks, which are expected to be ultradense with very high peak rate but relatively lower expected traffic per user. Research in this area has evolved from the distributed algorithms requiring prior knowledge of the number of secondary users (SUs) U to the existing algorithms, which can estimate U independently by counting the number of collisions. The major drawback of these algorithms is the large number of collisions leading to wastage of power and bring down the effective life of battery operated SUs. In this paper, we develop algorithms that learn faster and incurs fewer collisions. We assume unknown U with two types of networks: fixed U (i.e., static networks) and time-varying U (i.e., dynamic networks). Proposed algorithms are based on the multiplayer multi-armed bandit (MAB) approach. We show that the proposed algorithms offer constant regret (i.e., throughput loss) with high probability and sub-linear regret in static and dynamic networks, respectively. Theoretical analysis, simulation, and experimental results demonstrate the efficacy of the proposed algorithms in terms of vacant spectrum utilization, regret, and the number of collisions. Fewer collisions significantly increase the operational life of SUs.

关键词： distributed algorithms infrastructureless network multiplayer bandit opportunistic spectrum access

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Double-Mode Energy Management for Multi-Energy System via distributed Dynamic Event-Triggered Newton-Raphson Algorithm

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IEEE TRANSACTIONS ON SMART GRID 2020年第6期11卷 5339-5356页

作者： Li, Yushuai Gao, David Wenzhong Gao, Wei Zhang, Huaguang Zhou, Jianguo Univ Denver Dept Elect & Comp Engn Denver CO 80208 USA Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst Shenzhen 518055 Peoples R China

The islanded and network-connected modes are expected to be modeled into a unified form as well as in a distributed fashion for multi-energy system. In this way, the adaptability and flexibility of multi-energy system can be enhanced. To this aim, this paper establishes a double-mode energy management model for the multi-energy system. It is formed by many energy bodies. With such a model, each participant is able to adaptively respond to the change of mode switching. Furthermore, a novel distributed dynamic event-triggered Newton-Raphson algorithm is proposed to solve the double-mode energy management problem in a fully distributed fashion. In this method, the idea of Newton descent along with the dynamic event-triggered communication strategy are introduced and embedded in the execution of the proposed algorithm. With this effort, each participant can adequately utilize the second-order information to speed up convergence. The optimality is not affected. Meanwhile, the proposed algorithm can be implemented with asynchronous communication and without needing special initialization conditions. It exhibits better flexibility and adaptability especially when the system modes are changed. In addition, the continuous-time algorithm is executed with discrete-time communication driven by the proposed dynamic event-triggered *** results in reduced communication interaction and avoids needing continuous-time information transmission. It is also proved that each participant can asymptotically converge to the global optimal point. Finally, simulation results show the effectiveness of the proposed model and illustrate the faster convergence feature of the proposed algorithm.

关键词： Heuristic algorithms Energy management Convergence Resistance heating Indexes Adaptation models distributed algorithms Newton-Raphson energy Internet distributed algorithm multi-energy systems

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distributed Inference of the Multiplex Network Topology of Complex Systems

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2020年第1期7卷 278-287页

作者： Lombana, Daniel Alberto Burbano Freeman, Randy A. Lynch, Kevin NYU Dept Mech & Aerosp Engn Brooklyn NY 11201 USA Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA Northwestern Inst Complex Syst Evanston IL 60201 USA Northwestern Univ Dept Mech Engn Evanston IL 60208 USA

Many natural and engineered systems can be modeled as a set of nonlinear units interacting with each other over a network of interconnections. Often, such interactions occur through different types of functions giving rise to so-called multiplex networks. As an example, two masses can interact through both a spring and a damper. In many practical applications, the multiplex network topology is unknown, and global information is not available. In this paper, we propose a novel distributed approach to infer the network topology for a class of networks with both nonlinear node dynamics and multiplex couplings. In our strategy, the estimators measure only local network states but cooperate with their neighbors to fully infer the network topology. Sufficient conditions for stability and convergence are derived using appropriate Lyapunov functions. Applications to networks of chaotic oscillators and multirobot manipulation are presented to validate our theoretical findings and illustrate the effectiveness of our approach.

关键词： Adaptive control distributed algorithms network control network reconstruction (NR)

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distributed Model Predictive Control for More Electric Aircraft Subsystems Operating at Multiple Time Scales

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2020年第6期28卷 2177-2190页

作者： Dunham, William Hencey, Brandon Girard, Anouck R. Kolmanovsky, Ilya Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA US Air Force State Awareness & Real Time Response Grp SARTR Res Lab Riverside OH 45433 USA

This article considers predictive control of the jet engine and electrical power distribution system of a more electric aircraft (MEA) with asynchronous distributed controllers. The shafts of the engine spools are mechanically coupled to the electrical generators of the microgrid, linking the dynamics of the two subsystems. As the subsystems and their controllers are designed by different entities, the preservation of subsystem privacy must be an integral part of any coordination mechanism and, due to the underlying subsystems having differing time scales in their dynamics, the subsystem control updates are not guaranteed to be synchronized. To address these requirements, a distributed model predictive control (D-MPC) algorithm based on the alternating direction method of multipliers (ADMM) is proposed, which accounts for and exploits the differing control update rates of the engine and power subsystem controllers while preserving privacy. An extension to the Algorithm that seeks to minimize the required communication volume by downsampling the interactions to the rate of the engine time scale is also presented. Simulation results on a high-fidelity nonlinear system model demonstrate that the distributed controllers can outperform a decentralized controller and their performance can match that of a fully centralized controller.

关键词： Engines Aircraft Aircraft propulsion Predictive control Shafts Privacy Aerospace control Decentralized control distributed algorithms energy management power control predictive control turbomachinery

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distributed Robust Algorithm for Economic Dispatch in Smart Grids Over General Unbalanced Directed Networks

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第7期16卷 4322-4332页

作者： Li, Huaqing Wang, Zheng Chen, Guo Dong, Zhao Yang Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

The increased complexity of modern energy network raises the necessity of flexible and reliable methods for smart grid operation. To this end, this article is centered on the economic dispatch problem (EDP) in smart grids, which aims at scheduling generators to meet the total demand at the minimized cost. This article proposes a fully distributed algorithm to address the EDP over directed networks and takes into account communication delays and noisy gradient observations. In particular, the rescaling gradient technique is introduced in the algorithm design and the implementation of the distributed algorithm only resorts to row-stochastic weight matrices, which allows each generator to locally allocate the weights on the messages received from its in-neighbors. It is proved that the optimal dispatch can be achieved under the assumptions that the nonidentical constant communication delays inflicting on each link are uniformly bounded and the noises embroiled in gradient observation of every generator are bounded variance zero mean. Simulations are provided to validate and testify the effectiveness of the presented algorithm.

关键词： Generators distributed algorithms Smart grids Delays Cost function Economics Communication delay distributed algorithm directed networks economic dispatch noisy gradient

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Interaction-Based distributed Learning in Cyber-Physical and Social Networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第1期65卷 223-236页

作者： Sasso, Francesco Coluccia, Angelo Notarstefano, Giuseppe Univ Salento Dept Engn I-73100 Lecce Italy Univ Bologna Dept Elect Elect & Informat Engn I-40136 Bologna Italy

In this paper, we consider a network scenario in which agents can evaluate each other according to a score graph that models some physical or social interaction. The goal is to design a distributed protocol, run by the agents, allowing them to learn their unknown state among a finite set of possible values. We propose a Bayesian framework in which scores and states are associated to probabilistic events with unknown parameters and hyperparameters, respectively. We prove that each agent can learn its state by combining a local Bayesian classifier with a (centralized) Maximum Likelihood (ML) estimator of the parameter-hyperparameter. To overcome the intractability of the ML problem, we provide two relaxed probabilistic models that lead to distributed estimation schemes with affordable complexity. In order to highlight the appropriateness of the proposed relaxations, we demonstrate the distributed estimators on a machine-to-machine testing setup for anomaly detection and on a social interaction setup for user profiling.

关键词： Bayes methods Probabilistic logic Maximum likelihood estimation Social networking (online) distributed algorithms Random variables Classification consensus distributed estimation distributed learning distributed optimization empirical Bayes

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distributed non-linear robust consensus-based sensor calibration for networked control systems

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IET CONTROL THEORY AND APPLICATIONS 2020年第9期14卷 1200-1208页

作者： Stankovic, Maja S. Antic, Dragan S. Univ Nis Fac Elect Nish Serbia Telekom Srbija Belgrade Serbia

In this study a new algorithm is proposed for distributed blind sensor macro-calibration in networked control systems robust to noise. The proposed distributed algorithm for estimation of gain and offset correction parameters is of stochastic approximation type, with local non-linear transformations of residuals. Convergence of the algorithm in mean-square and with probability one to consensus is proved for a large class of non-linear transformations, network properties and communication and measurement noise characteristics. The choice of the introduced non-linear transformations in accordance with the theory of robust statistics leads to the proposal of new calibration algorithms robustified w.r.t. noise. It is demonstrated by Monte Carlo simulation that the proposed algorithms are very efficient in the presence of large outliers from the point of view of both achievement of high convergence rate and adequate values of convergence points, outperforming the existing linear algorithms.

关键词： calibration networked control systems robust control measurement errors Monte Carlo methods estimation theory stochastic processes distributed algorithms probability mean square error methods distributed control nonlinear control systems distributed nonlinear robust consensus-based sensor calibration networked control systems distributed blind sensor macro-calibration stochastic approximation type measurement noise characteristics robust statistics linear algorithms distributed algorithm local nonlinear transformations Monte Carlo simulation

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distributed Adaptive Robust Voltage/VAR Control With Network Partition in Active Distribution Networks

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IEEE TRANSACTIONS ON SMART GRID 2020年第3期11卷 2245-2256页

作者： Li, Peishuai Zhang, Cuo Wu, Zaijun Xu, Yan Hu, Minqiang Dong, Zhaoyang Southeast Univ Sch Elect Engn Nanjing 210000 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

This paper proposes a distributed adaptive robust voltage/var control (DAR-VVC) method in active distribution networks to minimize power loss while keeping operating constraints under uncertainties. The DAR-VVC aims to coordinate on-load tap changers, capacitor banks and PV inverters in multiple operation stages through a distributed algorithm. To improve efficiency of the distributed algorithm, an affinity propagation clustering algorithm is employed to divide the distribution network by aggregating "the close nodes" together and setting "the far nodes" apart, leading to the network partition where the information exchange between adjacent sub-networks is reduced. Moreover, the virtual load which describes load characteristics of the sub-networks is applied to enhance the boundary conditions. To fully deal with the uncertainties, the proposed DAR-VVC is formulated in a robust optimization model which considers the worst case to guarantee solution robustness against uncertainty realization. Besides, this paper develops an alternating optimization procedure integrating a column-and-constraint generation algorithm and an alternating direction method of multipliers to solve the DAR-VVC problem. The proposed approach is tested on IEEE 33 and IEEE 123 bus distribution test system and numerical simulations verify high efficiency and full solution robustness of the DAR-VVC.

关键词： Uncertainty Optimization Voltage control Convex functions Inverters distributed algorithms Load modeling Affinity propagation clustering algorithm alternating optimization procedure distributed algorithm network partition robust optimization voltage VAR control

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Increment-correction method for online distributed optimisation in distribution networks with extra-high DER integration

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IET RENEWABLE POWER GENERATION 2020年第11期14卷 1933-1945页

作者： Xie, Kaijun Liu, Mingbo Lu, Wentian South China Univ Technol Sch Elect Power Engn Guangzhou Guangdong Peoples R China

This study focuses on future distribution networks featuring the integration of extra-high distributed energy resources (DERs). In these distribution networks, the extra-high DER penetration and the randomness and volatility of DER outputs increase the likelihood of voltage problems. Hence, it is necessary to design efficient solutions for DER output setpoints to regulate the voltages of distribution networks. However, the existing online distributed algorithms were designed based on different linearised technologies for power flow equations, which will cause imprecise results under extra-high DER integration. In this study, the authors propose an increment-correction method to improve the linearised power flow model. To obtain an efficient distributed manner, they introduced an indirect function into the end-customer (the DER owner)'s objective function, stimulating them to provide voltage-regulating service. In particular, two price incentive signals contained in the indirect function were transmitted from the distribution system operator to drive the DER owner to regulate output increments, which is suitable for cases where the DER belongs to different end-customers. The proposed method can not only protect the private information of end-customers but also enhance the robustness of the distribution network for the DER integration level. Numerical test results demonstrate the merits of the proposed method.

关键词： distribution networks load flow distributed power generation distributed algorithms voltage control power distribution control optimisation increment-correction method distribution network extra-high DER integration future distribution networks extra-high distributed energy resources extra-high DER penetration DER output setpoints existing online distributed algorithms efficient distributed manner the DER owner distribution system operator DER integration level

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distributed Privacy-Preserving Iterative Summation Protocols

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IEEE INTELLIGENT SYSTEMS 2020年第4期35卷 25-36页

作者： Liu, Yang Liu, Qingchen Zhang, Xiong Qin, Shuqi Lei, Xiaoping Tencent Shenzhen Peoples R China Tech Univ Munich Chair Informat Oriented Control Munich Germany

In this article, we study the problem of summation evaluation of secrets. The secrets are distributed over a network of nodes that form a ring graph. Privacy-preserving iterative protocols for computing the sum of the secrets are proposed, which are resilient against dynamic node join and leave situations. Theoretic bounds are derived regarding the utility and accuracy, and the proposed protocols are shown to comply with differential privacy requirements. Based on utility, accuracy, and privacy, we also provide guidance on appropriate selections of random noise parameters. Additionally, a few numerical examples that demonstrate their effectiveness and superiority are provided.

关键词： Protocols Privacy Data mining Cryptography Machine learning distributed algorithms

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