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Nonconvex, Fully distributed Optimization Based CAV Platooning Control Under Nonlinear Vehicle Dynamics

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第11期23卷 20506-20521页

作者： Shen, Jinglai Kammara, Eswar Kumar Hathibelagal Du, Lili Univ Maryland Baltimore Cty Dept Math & Stat Baltimore MD 21250 USA Walmart Global Tech Hoboken NJ 07030 USA Univ Florida Dept Civil & Coastal Engn Gainesville FL 32608 USA

CAV platooning technology has received considerable attention, driven by the next generation smart transportation systems. This paper considers nonlinear vehicle dynamics and develops fully distributed optimization based CAV platooning control schemes via the platoon centered MPC approach for a possibly heterogeneous CAV platoon. The nonlinear vehicle dynamics leads to major difficulties in distributed algorithm development and control analysis. Specifically, the underlying MPC optimization problem is nonconvex and densely coupled. Further, the closed loop dynamics becomes a time-varying nonlinear system with non-vanishing external perturbations, making stability analysis rather complicated. To overcome these difficulties, we formulate the underlying MPC optimization problem as a locally coupled, albeit nonconvex, optimization problem and develop a sequential convex programming based fully distributed scheme for a general MPC horizon. Such a scheme can be effectively implemented for real-time computing using operator splitting methods. To analyze the closed loop stability, we apply various tools from global implicit function theorems, stability of linear time-varying systems, and Lyapunov theory for input-to-state stability to show that the closed loop system is locally input-to-state stable uniformly in all small coefficients pertaining to the nonlinear dynamic effects. Numerical tests on a heterogeneous CAV platoon in a real traffic condition illustrate the effectiveness of the proposed method.

关键词： Vehicle dynamics Optimization Numerical stability Stability criteria Aerodynamics Nonlinear dynamical systems Time-varying systems Connected and autonomous vehicle car following control distributed algorithm nonconvex optimization input-to-state stability Lyapunov stability theory

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distributed Nonlinear Placement for Multicluster Systems: A Time-Varying Nash Equilibrium-Seeking Approach

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第11期52卷 11614-11623页

作者： Huang, Bomin Yang, Chengwang Meng, Ziyang Chen, Fei Ren, Wei Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China Tsinghua Univ Dept Precis Instrument Beijing 100084 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

In this article, a class of distributed nonlinear placement problems is considered for a multicluster system. The task is to determine the positions of the agents in each cluster subject to the constraints on agent positions and the network topology. In particular, the agents in each cluster are placed to form the desired shape and minimize the sum of squares of the Euclidean lengths of the links amongst the center of each cluster and its corresponding cluster members. The problem is converted into a time-varying noncooperative game and then a distributed Nash equilibrium-seeking algorithm is designed based on a distributed observer method. A new iterative approach is employed to prove the convergence with the aid of the Lyapunov stability theorem. The effectiveness of the distributed algorithm is validated by numerical examples.

关键词： Shape Clustering algorithms Wires Transportation Trajectory Network topology Multi-agent systems distributed algorithm Nash equilibrium (NE) seeking noncooperative game nonlinear placement

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Real-Time Asynchronous Information Processing in distributed Power Systems Control

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IEEE TRANSACTIONS ON SMART GRID 2022年第1期13卷 773-782页

作者： Cintuglu, Mehmet H. Ishchenko, Dmitry ABB Inc Raleigh NC 27610 USA Hitachi ABB Power Grids AG Power Grids Res Raleigh NC 27606 USA

Centralized power system algorithms are gradually augmented and substituted with distributed counterparts, where timely updated asynchronous information processing is critical for real-time implementation. This paper presents two methods to achieve the desired real-time performance. distributed sequencing method simulates a synchronous system on an asynchronous network using a distributed logical vector clock. The second method, adaptive asynchronous processing, handles asynchrony at each iteration step through adaptive dynamics of the network agents. Necessity of the developed methods is discussed based on the specific requirements of the communication network in terms of algorithm accuracy, network size and time-varying message delays. A case study on the distributed power system state estimation for networked microgrids is presented to validate the developed methods implementing industrial publish/subscribe protocols. The results demonstrate that the developed information processing methods can be efficiently applied for real-world industrial implementation.

关键词： Clocks distributed algorithms Servers Real-time systems Synchronization Information processing Smart grids distributed algorithm asynchronous information processing publish subscribe power systems

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Decentralized energy management of a hybrid building cluster via peer-to-peer transactive energy trading

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APPLIED ENERGY 2024年 372卷

作者： Ying, Chenhao Zou, Yunyang Xu, Yan Nanyang Technol Univ Ctr Power Engn CPE Sch Elect & Elect Engn Singapore 639798 Singapore

With a rising number of buildings being equipped with private distributed energy resources (DERs) such as rooftop PV panels and energy storage devices, an effective energy management method for a building cluster becomes increasingly imperative. This paper proposes a novel decentralized transactive energy management (TEM) method for a hybrid cluster of residential and commercial buildings, which enables peer-to-peer (P2P) energy trading among the DER owners and consumers. The strategic interactions among the DER owners and consumers are modeled as a multi-leader-multi-follower (MLMF) Stackelberg game and formulated as a bi-level model. The DER owners, the commercial and residential consumers are all autonomous entities, optimizing their individual welfare functions and sharing necessary trading-related information, which are expressed as the upper-level leaders' models and the lower-level followers' models, respectively. To preserve the privacy and autonomy of each entity within the building cluster, a distributed algorithm incorporated with an efficient P2P pricing mechanism is designed for the formulated MLMF Stackelberg game model. Simulation results demonstrate the effectiveness of the proposed method on mitigating the reliance of the building cluster on the power grid, motivating the DERs to actively participate in P2P trading, and reducing the consumers' energy consumption costs

关键词： Peer -to -peer energy trading Transactive energy management Building cluster Stackelberg game distributed algorithm distributed energy resources

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HACCR: Hierarchical Agglomerative Clustering-Based Connectivity Restoration Approach in Mobile Wireless Sensor and Robot Networks

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WIRELESS PERSONAL COMMUNICATIONS 2024年第3期138卷 1773-1798页

作者： Mogharabi, Alireza Taghizadeh, Alireza Manochehri, Kooroush Islamic Azad Univ Dept Comp Engn Parand Branch Parand Iran Amirkabir Univ Technol Dept Comp Engn Garmsar Campus Garmsar Iran

Wireless sensor networks consist of numerous sensor nodes that monitor and send environmental data, such as temperature measurements and motion detection information, to a central station. Due to the nature of hop-by-hop data transmission in such networks, the failure of several nodes can adversely affect the functionality of the remaining healthy nodes, making the connection to the central station impossible. This study introduces the Hierarchical Agglomerative Clustering-Based Connectivity Restoration (HACCR) approach to address the problem of network reconnection after node failures. HACCR operates in two phases: backbone formation and recovery process. In the backbone formation phase, nodes exchange data packets to determine their locations and run the Hierarchical Agglomerative Clustering algorithm to form clusters. The clusters' geometric centers are then used to construct an Equivalent Graph, from which a Minimum Spanning Tree (MST) is derived using the Kruskal algorithm. In the recovery process, mobile nodes are deployed to restore connectivity by moving towards the cluster centers and establishing communication links along the MST. HACCR's performance is validated through comprehensive simulation studies, considering metrics such as the number of deployed mobile nodes, total traveled distance, and maximum distance traveled. The results demonstrate that HACCR outperforms baseline approaches in terms of efficiency and effectiveness.

关键词： Mobile wireless sensor networks (MWSN) Connectivity restoration Hierarchical agglomerative clustering Network recovery Machine learning distributed algorithm

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Decentralized Federated Learning With Asynchronous Parameter Sharing for Large-Scale IoT Networks

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IEEE INTERNET OF THINGS JOURNAL 2024年第21期11卷 34123-34139页

作者： Xie, Haihui Xia, Minghua Wu, Peiran Wang, Shuai Huang, Kaibin Sun Yat sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China Fujian Agr & Forestry Univ Sch Comp & Informat Sci Fuzhou 350002 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Ctr Cloud Comp Shenzhen 518055 Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

Federated learning (FL) enables wireless terminals to collaboratively learn a shared parameter model while keeping all the training data on devices per se. Parameter sharing consists of synchronous and asynchronous ways: the former transmits parameters as blocks or frames and waits until all transmissions finish, whereas the latter provides messages about the status of pending and failed parameter transmission requests. Whatever synchronous or asynchronous parameter sharing is applied, the learning model shall adapt to distinct network architectures as an improper learning model will deteriorate learning performance and, even worse, lead to model divergence for the asynchronous transmission in resource-limited large-scale Internet of Things (IoT) networks. This article proposes a decentralized learning model and develops an asynchronous parameter-sharing algorithm for resource-limited distributed IoT networks. This decentralized learning model approaches a convex function as the number of nodes increases, and its learning process converges to a global stationary point with a higher probability than the centralized FL model. Moreover, by jointly accounting for the convergence bound of FL and the transmission delay of wireless communications, we develop a node scheduling and bandwidth allocation algorithm to minimize the transmission delay. Extensive simulation results corroborate the effectiveness of the distributed algorithm in terms of fast learning model convergence and low-transmission delay.

关键词： Delays Convergence Data models Computational modeling Adaptation models Peer-to-peer computing Wireless communication Asynchronous communications distributed algorithm federated learning (FL) large-scale Internet of Things (IoT) networks transmission delay

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Performance optimization for continuous network localization

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AUTOMATICA 2025年 171卷

作者： Chen, Liangming Liang, Chenyang Li, Yibei Mei, Jie Xie, Lihua Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab Control Theory & Intelligent Syst Shenzhen Peoples R China Harbin Inst Technol Sch Mech Engn & Automat Shenzhen Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

Recent advances in linear localization of sensor networks allow sensors to localize themselves by using inter-sensor measurements, such as distances, bearings and interior angles. According to earlier works, linear localization algorithms' performance is relatively poor, which, however, has not been adequately addressed in the existing literature. The aim of this paper is to improve the performance of linear and continuous localization algorithms. More specifically, we focus on improving three key aspects of linear localization algorithms' performance, i.e., the stability margin, convergence rate and robustness against measurement noises. Firstly, we propose a unified description for networks' linear localization algorithms, given different types of measurements, and show that the stability margin, convergence rate and robustness of linear localization algorithms are commonly determined by one parameter, , namely, the minimum eigenvalue of the network's localization matrix. . Secondly, by carefully choosing the decision variable, we formulate the performance optimization problem as an eigenvalue optimization problem, and show the non-differentiability of the eigenvalue optimization problem. Thirdly, we propose a distributed optimization algorithm, which guarantees the convergence to an optimal solution of the eigenvalue optimization problem. Finally, simulation examples validate the effectiveness of the proposed distributed optimization algorithm. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： distributed network localization Angle rigidity Performance optimization Sensor networks Angle measurements distributed algorithm

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Demand response with incomplete information: A systematic review

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 246卷

作者： Huang, Lidong Liu, Hui Liu, Bin Ma, Siyu Wang, Ni Xie, Haimin He, Zihan Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tech Nanning 530004 Peoples R China

Demand response (DR), as an effective approach to maintaining supply-demand balance, has attracted widespread attentions. However, due to the data acquisition limitation, the communication failure and the privacy protection, the incomplete information problem in DR widely exists, which will reduce the response efficiency and profit. To cope with this issue, many approaches are proposed, including the regression method, reinforcement learning (RL) and distributed algorithm. This paper presents an overview of DR with incomplete information issues according to a systematic review of over 150 papers and 11 countries. First, the DR is characterized into price based demand response (PBDR), incentive based demand response (IBDR), integrated demand response (IDR) and multi-region DR. At the same time, the source and type of the incomplete information in different DRs are analyzed. Furthermore, the commonly used methods for the incomplete information are categorized, in which their application, advantage and disadvantage are discussed. Finally, key challenges are summarized and future research directions are presented including the operational parameters estimation of household devices, the environment effect on customer response willingness and the robust regression method.

关键词： Demand response distributed algorithm Incomplete information Regression method Reinforcement Learning (RL)

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Supply Resilience Constrained Scheduling of MEGs for Distribution System Restoration: A Stochastic Model and FW-PH algorithm

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IEEE TRANSACTIONS ON SMART GRID 2025年第1期16卷 194-208页

作者： Zhang, Menglin Cai, Sheng Xie, Yunyun Zhou, Bo Zheng, Weiye Wu, Qiuwei Wen, Jinyu China Univ Geosci Sch Automat Hubei Key Lab Adv Control & Intelligent Automat Co Minist Educ Wuhan 430074 Peoples R China China Univ Geosci Engn Res Ctr Intelligent Technol Geoexplorat Minist Educ Wuhan 430074 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA South China Univ Technol Sch Elect Engn Guangzhou 510641 Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT8 5BN North Ireland Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China

Mobile emergency generators (MEGs) are critical resources for distribution system restoration after disaster- induced outages. However, insufficient fuel storage can cause MEGs to cease operation, leading to secondary interruptions of restored services. To enhance supply resilience, we include fuel tank (FT) dispatch as part of the MEG-aid restoration strategy. An integrated sequential service restoration model is developed to optimize the spatiotemporal travel behavior of MEGs and FTs, restoration paths, and load pickup sequences in the distribution network. To account for uncertain distribution line faults and traffic congestion, a stochastic programming approach is used. The proposed method optimally prepositions MEGs and routes them to target locations to restore critical loads by gradually forming microgrids. Moreover, the FTs in the transportation network are scheduled to ensure adequate fuel for the uninterrupted and reliable operation of MEGs. The presence of both uncertainty and discreteness presents computational challenges to the stochastic mixed-integer programming model. Thus, a distributed algorithm that combines the progressive hedging and Frank-Wolfe methods is applied to enhance computational efficiency while ensuring global convergence. Simulation results showed the effectiveness of the proposed method in improving the reliability of restoration schemes and computational efficiency.

关键词： Fuels Resilience Stochastic processes Generators Convergence Fuel storage Disasters Mobile emergency generator sequential service restoration supply resilience distributed algorithm stochastic mixed-integer programming

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A distributed quantile estimation algorithm of heavy-tailed distribution with massive datasets

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2020年第1期18卷 214-230页

作者： Xie, Xiaoyue Shi, Jian Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

Quantile estimation with big data is still a challenging problem in statistics. In this paper we introduce a distributed algorithm for estimating high quantiles of heavy-tailed distributions with massive datasets. The key idea of the algorithm is to apply the alternating direction method of multipliers in parameter estimation of the generalized pareto distribution in a distributed structure and compute high quantiles based on parameter estimation by the Peak Over Threshold method. This paper proves that the proposed algorithm converges to a stationary solution when the step size is properly chosen. The numerical study and real data analysis also shows that the algorithm is feasible and efficient for estimating high quantiles of heavy-tailed distribution with massive datasets and there is a clear-cut winner for the extreme quantiles.

关键词： distributed algorithm big data high quantile estimation heavy-tailed distribution Peak Over Threshold method

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