Event-trigger Optimal Consensus for Multi-agent System Subject to Differential Privacy

Dong, Tao; Zhu, Huiyun; Hu, Wenjie

doi:10.1007/s12555-020-0343-7

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…Traditional time-triggered control adopts the way of transmitting communication resources all the time, which causes some unnecessary information to be transmitted and wastes a lot of communication resources (Zou et al, 2019). However, event-triggered mechanisms can effectively solve the problem of communication resource waste (Dong et al, 2021; Fan et al, 2020; Ling, 2020). Therefore, the event-triggered mechanism has been widely concerned and researched.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered sliding mode control for networked control systems with actuator faults: Application to gas turbine system

Wang,

Hu,

et al. 2024

Transactions of the Institute of Measurement and Control

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This paper presents an event-triggered sliding mode fault-tolerant control method for networked control systems with actuator faults and external disturbance. First, an event-triggered scheme in the networked control systems is proposed to reduce the number of data transmission. In addition, considering the event-triggering, a sliding mode surface based on the system structure is constructed. Besides, to obtain the controller parameters that meet the [Formula: see text] performance, the bounded real lemma in the form of linear matrix inequality is obtained using the Lyapunov functional. In addition, a sliding mode fault-tolerant control is designed to guarantee that the system can still run stably under the condition of faults and disturbances. Finally, the simulation results of gas turbine system are given to verify the feasibility of the theoretical method.

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Section: Introductionmentioning

confidence: 99%

Event-triggered sliding mode control for networked control systems with actuator faults: Application to gas turbine system

Wang,

Hu,

et al. 2024

Transactions of the Institute of Measurement and Control

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show abstract

“…Conventional consensus algorithms achieve their objective through direct transmission of state information between adjacent nodes, potentially jeopardizing the privacy of participating nodes seeking to maintain the confidentiality of their state information. In recent years, a wealth of researchers has ventured into the realm of preserving the privacy of individual nodes within MASs [32,33]. Regarding the privacy-preserving issue in MASs, certain scholars have advocated for the utilization of differential privacy techniques.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric bipartite consensus for multi‐agent systems with strong‐privacy‐preserving

Fang,

Yang,

Liu

2024

IET Control Theory & Appl

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In multi‐agent systems (MASs), not only do agents engage in cooperative relationships, but they also compete with one another. The topic of collaborative group in MASs has garnered significant attention. Due to the possibility of explicit interaction among agents, the complete sharing of known information among nodes presents a risk of privacy leakage. Therefore, this article explores the problem of asymmetric bipartite consensus control for MASs with strong‐privacy‐preserving. A novel state decomposition privacy‐preserving algorithm is proposed to achieve asymmetric bipartite consensus of MASs. This algorithm can ensure that the initial and final states of each agent will not be compromised, via a preservation coefficient designed in proposed algorithm. By employing algebraic graph theory and automatic control theory, a thorough analysis of the algorithmic performance is conducted. It is demonstrated that our algorithm can preserve the privacy of all agents in MASs while achieving asymmetric bipartite consensus, i.e. the consensus values are different in sign and modulus. Finally, the effectiveness of the main results is validated through simulation examples.

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Event‐Triggered Based Differential Privacy Distributed Optimization

Wang,

Lü

2024

Intl J Robust & Nonlinear

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Distributed optimization has emerged as a pivotal research area with widespread applications in machine learning and networked systems. While optimization algorithms efficiently collaborate to minimize a global objective function through information exchange among nodes, they are susceptible to privacy breaches, potentially leading to severe and unpredictable consequences. As differential privacy becomes the de facto standard for privacy preservation, recent studies have integrated differential privacy with distributed optimization. However, existing differential privacy distributed optimization algorithms typically require continuous communication and noise injection, which results in substantial communication overhead and degraded optimization performance. In this paper, we introduce a novel event‐triggered based differential privacy distributed optimization algorithm that leverages intermittent Laplace noise injection. Recognizing that event‐triggered timings may inadvertently reveal gradient information about the objective function, we introduce an enhanced ‐Differential Privacy definition specifically tailored for event‐triggered communication, accounting for both the transmitted state information and the event‐triggered timings. By intermittently injecting Laplace noise, our mechanism significantly reduces the total amount of noise required, thereby minimizing its negative impact on the algorithm's performance. We rigorously prove that, even with reduced noise, our algorithm ensures rigorous privacy preservation. Furthermore, our algorithm achieves accurate convergence and improved optimization performance in constrained optimization problems, while reducing communication overhead. Numerical simulations confirm the effectiveness of our proposed algorithm.

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Event-trigger Optimal Consensus for Multi-agent System Subject to Differential Privacy

Cited by 7 publications

References 32 publications

Event-triggered sliding mode control for networked control systems with actuator faults: Application to gas turbine system

Event-triggered sliding mode control for networked control systems with actuator faults: Application to gas turbine system

Asymmetric bipartite consensus for multi‐agent systems with strong‐privacy‐preserving

Event‐Triggered Based Differential Privacy Distributed Optimization

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