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Differentially Private Dynamic Average Consensus-Based Newton Method for distributed optimization Over General Networks

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第2期55卷 1348-1361页

作者： Xing, Mingqi Ma, Dazhong Zhao, Jing Wong, Pak Kin Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Univ Macau Dept Electromech Engn Macau Peoples R China

This article investigates the issue of privacy preservation in distributed optimization, where each node possesses a local private objective function and collaborates to minimize the sum of those functions. A novel dynamic average consensus-based distributed Newton algorithm is introduced to achieve consensus, optimality, and differential privacy. Each node utilizes its local gradient and Hessian as time-varying reference signals, facilitating information exchange with neighbors for tracking the average. To safeguard privacy, persistent Laplace noise is introduced into the exchanged data, affecting the estimated optimal solution, gradient, and Hessian averages. To counteract the noise's impact, the internode coupling strength is adaptively reduced over time through decay factors, allowing for noise attenuation as the algorithm progresses. The algorithm's convergence to the optimal solution, assuming global function smoothness and strong convexity, is theoretically proven. The algorithm's accurate convergence to the optimal solution, assuming global function smoothness and strong convexity, is theoretically proven. Furthermore, the efficiency and reliability of the algorithm are empirically validated through simulations of an IEEE 14-bus test system.

关键词： Newton method Differential privacy distributed optimization distributed optimization dynamic average consensus dynamic average consensus unbalanced directed graph unbalanced directed graph unbalanced directed graph

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distributed optimization of High-Order Nonlinear Systems: Saving Computation and Communication via Prefiltering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第3期69卷 1144-1148页

作者： Kang, Jian Guo, Ge Yang, Guanghong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China

This brief investigates a distributed optimization problem for high-order strict-feedback nonlinear multiagent systems. A virtual system is built for each agent using a distributed proportional-integral (PI) optimization algorithm to estimate the global optimal solution online. The estimate is then input into a prefilter to generate an alternative estimate and its high-order derivatives. Based on these signals, a backstepping controller is used to make the actual system track the alternative estimate asymptotically, which thereby realizes exact global consensus optimization. Compared to the existing result, the proposed algorithm is fully distributed under undirected topologies with much weaker assumptions on local and global objective functions, lower computation and communication cost. Exact global optimal consensus can also be reached in the cases with directed topologies and intermittent communications. The effectiveness of the method is verified by simulations.

关键词： optimization Topology Nonlinear systems Linear programming Multi-agent systems Heuristic algorithms Directed graphs distributed optimization multiagent systems high-order nonlinear systems prefiltering intermittent communications

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A resilient control algorithm to distributed optimization under DoS attacks

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2024年第15期34卷 10346-10364页

作者： Liu, Dan Li, Liwei Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China

This article focuses on a distributed optimization problem under an undirectd and connected network affected by DoS attacks. First, new event-triggered mechanisms are developed to schedule the communication between agents. Then, an event-based resilient distributed optimization algorithm that removes the requirement of continuous communication in the existing results is designed to achieve consensus optimization. Assisted by the design of regulator equations and the algebraic graph theory, it proves that the proposed algorithm is exponentially convergent. Moreover, Zeno behavior can be excluded by reductio ad absurdum. The simulation results validate that the proposed algorithm is effective.

关键词： distributed optimization DoS attacks event-triggered control

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A distributed optimization Algorithm for Fixed-Time Flocking of Second-Order Multiagent Systems

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 152-162页

作者： Chen, Jianing Yang, Yihua Qin, Sitian Harbin Inst Technol Dept Math Weihai 264209 Peoples R China

In this article, a distributed optimization algorithm with fixed-time flocking is proposed for continuous-time multi-agent system with double-integrator dynamics. This algorithm has the ability to jointly drive the agents toward a common velocity while optimizing the global objective function. Moreover, based only on local information and communication, the fixed-time flocking and convergence are reported, and the upper bounds of the settling time are given, respectively. This significantly improves the consensus and convergence speed of the algorithm, which only needs the adjustment of parameters. In addition, the event-triggered mechanism is introduced to reduce the energy required for agent communication. It is verified that, under certain conditions, such an event-triggered algorithm can still ensure fixed-time flocking and convergence, and Zeno behavior is strictly prohibited. Besides, the fixed-time distributed algorithm is further adapted to accommodate time-varying communication conditions, where the communication topologies are switching within a series of undirected and connected graphs. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical analysis.

关键词： optimization Convergence Heuristic algorithms Topology Task analysis Multi-agent systems Linear programming Fixed-time flocking distributed optimization multi-agent system double-integrator dynamics

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Adaptive Fuzzy distributed optimization for Uncertain Nonlinear Multiagent Systems

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第4期32卷 1862-1872页

作者： Zheng, Yukan Li, Yuan-Xin Ahn, Choon Ki South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China Liaoning Univ Technol Coll Sci Jinzhou 121001 Peoples R China Korea Univ Sch Elect Engn Seoul 02841 South Korea

The adaptive fuzzy distributed optimization problem of nonlinear multiagent systems with unknown nonlinearities and uncertain disturbances is explored in this article. The unknown nonlinearities in the system model are determined by incorporating fuzzy logic systems. Rather than using the existing known gradient values of local objective functions, this article further takes into account the measured gradient values that are based on the output information. The bounded estimation method and well-defined smooth functions are used to account for the effects of uncertainties. To ensure that the output can asymptotically converge to the optimal solution, a new adaptive fuzzy distributed optimization controller is proposed using the Lyapunov function method and the adaptive backstepping technique. To demonstrate the effectiveness of the developed distributed optimization technique, two practical examples are used.

关键词： Adaptive backstepping design distributed optimization fuzzy logic system (FLS) uncertain multiagent systems (MASs)

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Prescribed-time distributed optimization problem with constraints

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ISA TRANSACTIONS 2024年 148卷 255-263页

作者： Li, Hailong Zhang, Miaomiao Yin, Zhongjie Zhao, Qi Xi, Jianxiang Zheng, Yuanshi Rocket Force Univ Engn Xian 710025 Peoples R China Xidian Univ Sch Mechanoelect Engn Shaanxi Key Lab Space Solar Power Stn Syst Xian 710071 Peoples R China Qingdao Univ Sci & Technol Sch Data Sci Qingdao 266061 Peoples R China

In recent years, distributed optimization problem have a wide range of applications in various fields. This paper considers the prescribed-time distributed optimization problem with/without constraints. Firstly, we assume the state of each agent is constrained, and the prescribed-time distributed optimization algorithm with constraints is designed on the basis of gradient projection algorithm and consensus algorithm. Secondly, the constrained distributed optimization problem is transformed into the unconstrained distributed optimization problem, and according to the gradient descent algorithm and consensus algorithm, we also propose the prescribed-time distributed optimization algorithm without constraints. By designing the appropriate objective functions, we prove the multi-agent system can converge to the optimal solution within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. Finally, three simulation examples are provided to verify the validity of the designed algorithms.

关键词： Prescribed-time distributed optimization Convex constraint Convex set

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Event-triggered predefined-time distributed optimization algorithm based on zero-gradient-sum

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SYSTEMS & CONTROL LETTERS 2025年 196卷

作者： Guo, Ge Zhang, Xinxin Zhang, Renyongkang Kang, Jian Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Qinhuangdao Sch Control & Engn Qinhuangdao 066004 Peoples R China Dalian Maritime Univ Coll Marine Elect Engn Dalian 116026 Peoples R China

This paper presents an event-triggered distributed optimization (DO) algorithm for consensus constraints and strongly convex local cost functions. The algorithm is derived by means of the zero-gradient-sum mechanism and sliding mode control, which is free from any initial conditions or local minimization. In order to avoid continuous communication and measurement, our algorithm enables the interaction between agents solely at event-triggered instants, resulting in conserving communication resources. Importantly, the proposed method drives system to the optimal consensus state within a predefined time. Simulation results demonstrate the effectiveness of our approach.

关键词： distributed optimization Event-trigger Predefined-time convergence Multi-agent systems

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A Tutorial on distributed optimization for Cooperative Robotics: From Setups and Algorithms to Toolboxes and Research Directions

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PROCEEDINGS OF THE IEEE 2025年第1期113卷 40-65页

作者： Testa, Andrea Carnevale, Guido Notarstefano, Giuseppe Univ Bologna Dept Elect Elect & Informat Engn I-40136 Bologna Italy

Several interesting problems in multirobot systems can be cast in the framework of distributed optimization. Examples include multirobot task allocation, vehicle routing, target protection, and surveillance. While the theoretical analysis of distributed optimization algorithms has received significant attention, its application to cooperative robotics has not been investigated in detail. In this article, we show how notable scenarios in cooperative robotics can be addressed by suitable distributed optimization setups. Specifically, after a brief introduction on the widely investigated consensus optimization (most suited for data analytics) and on the partition-based setup (matching the graph structure in the optimization), we focus on two distributed settings modeling several scenarios in cooperative robotics, i.e., the so-called constraint-coupled and aggregative optimization frameworks. For each one, we consider use-case applications, and we discuss tailored distributed algorithms with their convergence properties. Then, we revise state-of-the-art toolboxes allowing for the implementation of distributed schemes on real networks of robots without central coordinators. For each use case, we discuss its implementation in these toolboxes and provide simulations and real experiments on networks of heterogeneous robots.

关键词： optimization Robots Multi-robot systems Robot kinematics Surveillance Resource management distributed algorithms Tutorials Formation control Vehicle routing Cooperating robots distributed optimization distributed robot systems optimization and optimal control

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A Row-Stochastic Event-Based Quantized Algorithm for distributed optimization With Linear Convergence

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第6期55卷 2937-2948页

作者： Xing, Mingqi Ma, Dazhong Zhang, Huaguang Zhao, Jing Wong, Pak Kin Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Univ Macau Dept Electromech Engn Macau Peoples R China

This article proposes the row-stochastic event-based quantized (RSEQ) algorithm to address the distributed optimization problem with multiple communication constraints, including limited communication costs and bandwidth. In RSEQ, a novel event-based dynamic quantizer is designed to resist the negative effects of communication constraints on the algorithm. The quantizer encompasses the event generator and the dynamic encoder/decoder, which collectively adapt the frequency and size of information sharing based on real-time state. The RSEQ only requires the construction of a row-stochastic weight matrix, which leads to lower conservatism compared to algorithms based on column-stochastic matrices. Additionally, the introduction of an acceleration term enables RSEQ to linearly converge to the globally optimal solution without the deployment of the average gradient estimator. Instead, a Perron vector estimator needs to be employed to counteract the unbalancedness of the directed network. With the effect of the event generator, the Perron vector estimator can also be left inactive after a certain number of iterations, which means that the transmission of only state information between agents can linearly converge to the global optimal solution under directed networks. Finally, the effectiveness of the algorithm is demonstrated through an economic dispatch problem in smart grids.

关键词： Event detection Vectors Heuristic algorithms Convergence Bandwidth Costs Quantization (signal) Linear programming Directed graphs Cost function distributed optimization linear convergence quantized communication row-stochastic weight

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Differential privacy distributed optimization algorithm against adversarial attacks for efficiency optimization of complex industrial processes

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Yue, Changyang Du, Wenli Li, Zhongmei Liu, Bing Nie, Rong Qian, Feng East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China East China Univ Sci & Technol State Key Lab Ind Control Technol Shanghai 200237 Peoples R China

In order to improve the overall performance of large-scale industrial processes with complex constraints, vast amounts of production data are collected for distributed process optimization. Usually, this data contains a lot of enterprise sensitive information. Considering the conventional distributed algorithm has the issue of data privacy leakage, which weakens the safety of the entire manufacturing process and poses a serious threat to economic benefit, this paper proposes a privacy-preserving based distributed algorithm for a class of optimization problem of large-scale industrial processes. Additionally, the proposed method can be applied to the privacy of industrial process optimization problems between different enterprises, such as industrial value chain optimization problems. In specific, the differential privacy mechanism is adopted to protect the data privacy of the local node. Meanwhile, the mutual information technique is adopted to analyze the information loss in the communication data. Moreover, Lagrange primal-dual method is used to deal with the coupling inequality constraint. Subsequently, rigorous theoretical proof shows the convergence of the proposed algorithm. Then, the algorithm privacy metrics fully demonstrate the rationality and superiority of the mutual information technology used in this article for designing privacy parameters. Finally, experimental results of numerical cases and ethylene process optimization show the effectiveness of the proposed algorithm.

关键词： Differential privacy Mutual information distributed optimization Inequality constraints Industrial information security

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