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Dynamics and Optimization in Spatially distributed Electrical Vehicle Charging

IEEE TRANSACTIONS ON control OF NETWORK SYSTEMS 2025年第1期12卷 403-415页

作者： Paganini, Fernando Ferragut, Andres Univ ORT Uruguay Math Appl Telecommun & Energy Res Grp Montevideo 11100 Uruguay

We consider a spatially distributed demand for electrical vehicle recharging, which must be covered by a fixed set of charging stations. Arriving electrical vehicles receive feedback on transport times to each station, and waiting times at congested ones, based on which they make a selfish selection. This selection determines total arrival rates in station queues, which are represented by a fluid state;departure rates are modeled under the assumption that clients have a given sojourn time in the system. The resulting differential equation system is analyzed with tools of optimization. We characterize the equilibrium as the solution to a specific convex program, which has connections to optimal transport problems, and also with road traffic theory. In particular, a price of anarchy appears with respect to a social planner's allocation. From a dynamical perspective, global convergence to equilibrium is established, with tools of Lagrange duality and Lyapunov theory. An extension of the model that makes customer demand elastic to observed delays is also presented, and analyzed with extensions of the optimization machinery. Simulations to illustrate the global behavior are presented, which also help validate the model beyond the fluid approximation.

关键词： Routing Delays Optimization Mathematical models Costs Vehicle dynamics Road traffic Resource management Network systems control systems distributed algorithms/control electrical vehicle (EV) charging optimization transportation networks

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A Switching Performance Index-Driven Escaping-Chasing control of Networked control Systems Under Triggered DoS Attacks

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IEEE TRANSACTIONS ON control OF NETWORK SYSTEMS 2025年第1期12卷 1115-1124页

作者： Xing, Ning Zhang, Hai-Tao Huazhong Univ Sci & Technol MOE Engn Res Ctr Autonomous Intelligent Unmanned S Sch Artificial Intelligence & Automat State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Guangdong HUST Ind Technol Res Inst Dongguan 523808 Peoples R China Guangdong Prov Key Lab Mfg Equipment Digitizat Dongguan 523808 Peoples R China

In this article, an escaping-chasing control problem under triggered denial-of-service (DoS) attacks is addressed. A switching performance index-driven escaping-chasing path planning method is designed, and the tracking error systems are accordingly established. The analytical challenge lies in the tricky coupling between the performance index-driven and the DoS attack-driven switches. To this end, with the assistance of the average dwell time analytical method, the restrictions on both the DoS attack intensity and the switching frequency between "chasing unawareness" and "chasing awareness" are given to guarantee finite-time capturing of the chaser. Finally, simulations are conducted to verify the effectiveness of the proposed escaping-chasing control method.

关键词： Switches control systems Protocols Couplings Vehicle dynamics Switching frequency Robot sensing systems distributed algorithms/control multiagent system control networked control systems (NCSs) networks of autonomous agents

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Adaptive Weighting Push-SUM for Decentralized Optimization with Statistical Diversity

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IEEE Transactions on control of Network Systems 2025年

作者： Zhou, Yiming Cheng, Yifei Xu, Linli Chen, Enhong University of Science and Technology of China (USTC) Anhui Province Key Laboratory of Big Data Analysis and Application Hefei China

Statistical diversity is a property of data distribution and can hinder the optimization of a decentralized network. However, the theoretical limitations of the Push-SUM protocol reduce the performance in handling the statistical diversity of optimization algorithms based on it. In this paper, we theoretically and empirically reduce the negative impact of statistical diversity on decentralized optimization using the Push-SUM protocol. Specifically, we propose the Adaptive Weighting Push-SUM protocol, a theoretical generalization of the original Push-SUM protocol where the latter is a special case of the former. Our theoretical analysis shows that, with sufficient communication, the upper bound on the consensus distance for the new protocol reduces to O(1N), whereas it remains at O(1) for the Push-SUM protocol. We adopt SGD and Momentum SGD on the new protocol and prove that the convergence rate of these two algorithms to statistical diversity is O(NT) on the new protocol, while it is O(NdT) on the Push-SUM protocol, where d is the parameter size of the training model. To address statistical diversity in practical applications of the new protocol, we develop the Moreau weighting method for its generalized weight matrix definition. This method, derived from the Moreau envelope, is an approximate optimization of the distance penalty of the Moreau envelope. We verify that the Adaptive Weighting Push-SUM protocol is practically more efficient than the Push-SUM protocol via deep learning experiments. © 2014 IEEE.

关键词： communication networks Directed decentralized optimization distributed algorithms/control learning optimization

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A Cooperative distributed MPC Algorithm With Event-Based Communication and Parallel Optimization

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IEEE TRANSACTIONS ON control OF NETWORK SYSTEMS 2016年第3期3卷 275-285页

作者： Gross, Dominic Stursberg, Olaf Univ Kassel Inst Control & Syst Theory Dept Elect Engn & Comp Sci D-34121 Kassel Germany

This paper investigates the use of event-based communication in a distributed model predictive control (DMPC) scheme for linear subsystems interconnected by dynamics and costs. In the proposed DMPC scheme, all subsystems optimize their local input sequences in parallel, and local iterations are performed to update the global input sequence. To reduce the load on the communication network, we propose an event-based communication protocol, in which local information is only communicated if doing so results in a sufficient improvement of the overall control performance. Based on the event generator and a distributed stopping criterion, we first establish that the scheme terminates after a finite number of iterations, and we provide bounds on the suboptimality of the solution. It is shown that the suboptimality of the scheme can be made arbitrarily small by choosing an appropriate threshold. Subsequently, a bound on the convergence rate is established. Based on this bound, parameters used in the scheme are optimized for fast convergence. Finally, the stability properties of the proposed DMPC scheme are analyzed for the case, with and without terminal constraint. We illustrate our analysis by numerical examples and compare the load on the communication network and the suboptimality for event-based communication and full communication in every iteration.

关键词： distributed algorithms/control networked control systems optimal control optimization

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Event-Triggered Quantized Communication-Based distributed Convex Optimization

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IEEE TRANSACTIONS ON control OF NETWORK SYSTEMS 2018年第1期5卷 167-178页

作者： Liu, Shuai Xie, Lihua Quevedo, Daniel E. Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Paderborn Univ Dept Elect Engn EIM E D-33098 Paderborn Germany

A NOVEL distributed algorithm based on multiple agents with continuous-time dynamics is proposed for a convex optimization problem where the objective function is the summation of local objective functions and the state of each agent is subject to a convex constraint set. Considering the limited bandwidth of the communication channels, we introduce a dynamic quantizer for each agent. To further save on communication costs, we develop an event-based broadcasting scheme for each agent. In comparison with algorithms that rely on continuous communication, the proposed algorithm serves to save communication expenditure by exploiting temporal and spatial aspects. Though a joint design of dynamic quantizers and event-trigger functions are under mild conditions, the states of the agents asymptotically approach the global optimal point with an adjustable error bound without incurring Zeno behavior.

关键词： Communication networks distributed algorithms/control event trigger optimization quantization

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distributed Submodular Minimization and Motion Planning Over Discrete State Space

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IEEE TRANSACTIONS ON control OF NETWORK SYSTEMS 2020年第2期7卷 932-943页

作者： Jaleel, Hassan Shamma, Jeff S. Lahore Univ Management Sci Syed Babar Ali Sch Sci & Engn Dept Elect Engn Lahore 54792 Pakistan King Abdullah Univ Sci & Technol Comp Elect & Math Sci & Engn Div Robot Intelligent Syst & Control Lab Thuwal 239556900 Saudi Arabia

We develop a framework for the distributed minimization of submodular functions. Submodular functions are a discrete analog of convex functions and are extensively used in large-scale combinatorial optimization problems. While there has been a significant interest in the distributed formulations of convex optimization problems, distributed minimization of submodular functions has received relatively little research attention. Our framework relies on an equivalent convex reformulation of a submodular minimization problem, which is efficiently computable. We then use this relaxation to exploit methods for the distributed optimization of convex functions. The proposed framework is applicable to submodular set functions as well as to a wider class of submodular functions defined over certain lattices. We also propose an approach for solving distributed motion-planning problems in discrete state space based on submodular function minimization. We establish through a challenging setup of the capture the flag game that submodular functions over lattices can be used to design artificial potential fields for multiagent systems with discrete inputs. These potential fields are designed such that their minima correspond to desired behaviors, that is, agents are attracted toward their goals and are repulsed from obstacles and from each other for collision avoidance. Finally, we demonstrate that the proposed distributed framework can be employed effectively for generating feasible trajectories in such motion coordination problems.

关键词： distributed algorithms/control multi-robot systems optimization submodular optimization

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distributed Differential Games for control of Multi-Agent Systems

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IEEE TRANSACTIONS ON control OF NETWORK SYSTEMS 2022年第2期9卷 635-646页

作者： Cappello, Domenico Mylvaganam, Thulasi Imperial Coll London Dept Aeronaut London SW7 2AZ England

Motivated by the challenges arising in the field of multi-agent system (MAS) control, we consider linear heterogeneous MAS subject to local communication and investigate the problem of designing distributed controllers for such systems. We provide a game theoretic framework for systematically designing distributed controllers, taking into account individual objectives of the agents and their possibly incomplete knowledge of the MAS. Linear state-feedback control laws are obtained via the introduction of a distributed differential game, namely, the combination of local non-cooperative differential games, which are solved in a decentralized fashion. Conditions for stability of the MAS are provided for the special cases of acyclic and strongly connected communication graph topologies. These results are then exploited to provide stability conditions for general graph topologies. The proposed framework is demonstrated on a tracking synchronization problem associated with the design of a distributed secondary voltage controller for microgrids and on a numerical example.

关键词： distributed algorithms/control game theory multi-agent systems (MAS) networks of autonomous agents

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Differential Privacy in Linear distributed control Systems: Entropy Minimizing Mechanisms and Performance Tradeoffs

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IEEE TRANSACTIONS ON control OF NETWORK SYSTEMS 2017年第1期4卷 118-130页

作者： Wang, Yu Huang, Zhenqi Mitra, Sayan Dullerud, Geir E. Univ Illinois Coordinate Sci Lab Urbana IL 61801 USA

In distributed control systems with shared resources, participating agents can improve the overall performance of the system by sharing data about their personal preferences. In this paper, we formulate and study a natural tradeoff arising in these problems between the privacy of the agent's data and the performance of the control system. We formalize privacy in terms of differential privacy of agents' preference vectors. The overall control system consists of N agents with linear discrete-time coupled dynamics, each controlled to track its preference vector. Performance of the system is measured by the mean squared tracking error. We present a mechanism that achieves differential privacy by adding Laplace noise to the shared information in a way that depends on the sensitivity of the control system to the private data. We show that for stable systems the performance cost of using this type of privacy preserving mechanism grows as O(T-3/N epsilon(2)), where T is the time horizon and epsilon is the privacy parameter. For unstable systems, the cost grows exponentially with time. From an estimation point of view, we establish a lower-bound for the entropy of any unbiased estimator of the private data from any noise-adding mechanism that gives epsilon-differential privacy. We show that the mechanism achieving this lower-bound is a randomized mechanism that also uses Laplace noise.

关键词： Communication networks decision/estimation theory differential privacy distributed algorithms/control

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Differentially Private distributed Convex Optimization via Functional Perturbation

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IEEE TRANSACTIONS ON control OF NETWORK SYSTEMS 2018年第1期5卷 395-408页

作者： Nozari, Erfan Tallapragada, Pavankumar Cortes, Jorge Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA

We study a class of distributed convex constrained optimization problems where a group of agents aim to minimize the sum of individual objective functions while each desires that any information about its objective function is kept private. We prove the impossibility of achieving differential privacy using strategies based on perturbing the inter-agent messages with noise when the underlying noise-free dynamics are asymptotically stable. This justifies our algorithmic solution based on the perturbation of individual functions with Laplace noise. To this end, we establish a general framework for differentially private handling of functional data. We further design post-processing steps that ensure the perturbed functions regain the smoothness and convexity properties of the original functions while preserving the differentially private guarantees of the functional perturbation step. This methodology allows us to use any distributed coordination algorithm to solve the optimization problem on the noisy functions. Finally, we explicitly bound the magnitude of the expected distance between the perturbed and true optimizers which leads to an upper bound on the privacy-accuracy tradeoff curve. Simulations illustrate our results.

关键词： Cyber-physical systems differential privacy distributed algorithms/control networks of autonomous agents optimization

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Simulation-Based distributed Coordination Maximization Over Networks

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IEEE TRANSACTIONS ON control OF NETWORK SYSTEMS 2019年第2期6卷 713-726页

作者： Jang, Hyeryung Shin, Jinwoo Yi, Yung Kings Coll London Dept Informat London WC2R2LS England Korea Adv Inst Sci & Technol Dept Elect Engn Daejeon 34141 South Korea

In various online/offline multiagent networked environments, it is very popular that the system can benefit from coordinating actions of two interacting agents at some cost of coordination. In this paper, we first formulate an optimization problem that captures the amount of coordination gain at the cost of node activation over networks. This problem is challenging to solve in a distributed manner, since the target gain is a function of the long-term time portion of the intercoupled activations of two adjacent nodes and, thus, a standard Lagrange duality theory is hard to apply to obtain a distributed decomposition as in the standard network utility maximization. In this paper, we propose three simulation-based distributed algorithms, each having different update rules, all of which require only one-hop message passing and locally observed information. The key idea for being distributedness is due to a stochastic approximation method that runs a Markov chain simulation incompletely over time, but provably guarantees its convergence to the optimal solution. Next, we provide a game-theoretic framework to interpret our proposed algorithms from a different perspective. We artificially select the payoff function, where the game's Nash equilibrium is asymptotically equal to the socially optimal point, which leads to no price of anarchy. We show that two stochastically approximated variants of standard game-learning dynamics overlap with two algorithms developed from the optimization perspective. Finally, we demonstrate our theoretical findings on convergence, optimality, and further features such as a tradeoff between efficiency and convergence speed through extensive simulations.

关键词： distributed algorithms/control game theory optimization stochastic approximation

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