Resilient Constrained Optimization in Multi-Agent Systems With Improved Guarantee on Approximation Bounds

Kaheni, Mojtaba; Usai, Elio; Franceschelli, Mauro

doi:10.1109/lcsys.2022.3173495

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…Furthermore, the authors in [20] enhance the accuracy of the solution by imposing additional restrictions on the communication network. Additionally, an algorithm based on gradient tracking is presented in [21] to tackle the issue, while the authors in [22] introduce an algorithm specifically designed for constrained problems in the shared memory architecture.…”

Section: Introductionmentioning

confidence: 99%

Resilient and Privacy-Preserving Multi-Agent Optimization and Control of a Network of Battery Energy Storage Systems Under Attack

Kaheni,

Usai,

Franceschelli

2024

IEEE Trans. Automat. Sci. Eng.

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This paper deals with resilient and privacypreserving control to optimize the daily operation costs of networked Battery Energy Storage Systems (BESS) in a multiagent network vulnerable to various types of cyber-attacks. First, we formulate the optimization problem by defining the objective function and the local and coupling constraints. Next, we introduce a novel resilient decentralized control and optimization algorithm that can mitigate the effects of cyber-attacks, specifically false data injection attacks and hijacking, to enhance the network's resilience. The proposed method is based on filtering out outlier Lagrange multipliers in a suitable dual problem. Our proposed algorithm has two main advantages compared to the existing literature. Firstly, it can solve problems where the coupling constraint is not restricted to the average or a function of the average of decision variables. Secondly, our algorithm extends the well-known dual decomposition and Lagrange multiplier method to the decentralized control problem of BESSs. In the proposed algorithm presented in this paper, only the data relevant to the dual problem is exchanged among the agents. Noticing that the data of the dual problem does not contain any private information, mitigating privacy concerns associated with our proposed algorithm. We formally prove the convergence of our algorithm to a feasible and sub-optimal solution. Additionally, simulations demonstrate the effectiveness of our results.Note to Practitioners-Optimal coordinated control of BESSs increases the power system's reliability and reduces costs. With the expansion of the use of small-scale BESSs in household customers, it is possible to considerably increase the free capacity of power networks by optimally controlling these small BESSs.

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Section: Introductionmentioning

confidence: 99%

Resilient and Privacy-Preserving Multi-Agent Optimization and Control of a Network of Battery Energy Storage Systems Under Attack

Kaheni,

Usai,

Franceschelli

2024

IEEE Trans. Automat. Sci. Eng.

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“…Distributed optimization using multi-agent systems has found a wide range of applications in science and engineering, such as resource allocation using networks [2], energy and thermal comfort optimization in smart buildings [3], and formation control [4,5]. Many works have been devoted to discrete-time formulations of distributed optimization [6][7][8]. Those interested in the topic can refer to [9] for a comprehensive examination of models and algorithms with various constraints in relation to the model structure, algorithm type, and communication topology.…”

Section: Introductionmentioning

confidence: 99%

Estimating tolerable communication delays for distributed optimization problems in control of heterogeneous multi‐agent systems

Rahimi,

Mahboobi Esfanjani

2023

IET Control Theory & Appl

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This study addresses the problem of distributed optimization in control of heterogeneous linear multi‐agent systems. Instead of assuming a perfect communication medium as in many existing approaches, two important issues arising from communication networks are considered. First, the existence of communication delays is assumed when each agent receives information from its neighbors. Second, since communication networks are generally unreliable, it is assumed that each agent interacts with other agents through random digraphs. Finally, delay‐dependent sufficient conditions are derived in the form of linear matrix inequalities (LMIs) to prove convergence to optimal solutions. Numerical simulation results are analyzed to demonstrate the effectiveness of the proposed approach.

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“…The consensus problem for multi-agent systems has been widely investigated over the past few decades due to its applications in aircraft formation control [1], autonomous unmanned systems [2], wireless sensor networks [3], and other fields. Many remarkable research findings, such as the adaptive cooperative control of nonlinear multi-agent systems [4], adaptive distributed control of non-affine multi-agent systems [5], iterative learning control of nonlinear multi-agent systems [6], distributed optimization control of linear multi-agent systems [7,8], adaptive event-triggered control of multi-agent systems [9,10], and so on, have been extensively reported. It is not difficult to find that in many existing achievements on multi-agent systems, the fuzzy logic system and neural network approach have been successfully applied to approximate the unknown nonlinear dynamics by many researchers; see [11][12][13][14] and references therein.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Command Filtered Finite-Time Tracking Control for Uncertain Nonlinear Multi-Agent Systems with Unknown Input Saturation and Unknown Control Directions

2022

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This paper investigates the finite-time consensus tracking control problem of uncertain nonlinear multi-agent systems with unknown input saturation and unknown control directions. An adaptive fuzzy finite-time consensus control law is proposed by combining the fuzzy logic system, command filter, and finite-time control theory. Using the fuzzy logic systems, the uncertain nonlinear dynamics are approximated. Considering the command filter and backstepping control technique, the problem of the so-called “explosion of complexity” in the design of virtual control laws and adaptive updating laws is avoided. Meanwhile, the Nussbaum gain function method is applied to handle the unknown control directions and unknown input saturation problems. Based on the finite-time control theory and Lyapunov stability theory, it was found that all signals in the closed-loop system remained semi-global practical finite-time stable, and the tracking error could converge to a sufficiently small neighborhood of the origin in the finite time. In the end, simulation results were provided to verify the validity of the designed control law.

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Resilient Constrained Optimization in Multi-Agent Systems With Improved Guarantee on Approximation Bounds

Cited by 8 publications

References 17 publications

Resilient and Privacy-Preserving Multi-Agent Optimization and Control of a Network of Battery Energy Storage Systems Under Attack

Resilient and Privacy-Preserving Multi-Agent Optimization and Control of a Network of Battery Energy Storage Systems Under Attack

Estimating tolerable communication delays for distributed optimization problems in control of heterogeneous multi‐agent systems

Adaptive Fuzzy Command Filtered Finite-Time Tracking Control for Uncertain Nonlinear Multi-Agent Systems with Unknown Input Saturation and Unknown Control Directions

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