Robust consensus for linear multi‐agent systems with noises

Wang, Zhongmei; Wang, Wei; Zhang, Huanshui

doi:10.1049/iet-cta.2016.0191

Cited by 21 publications

(5 citation statements)

References 28 publications

(35 reference statements)

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“…(4) T is a group of agents that work in collaboration with other agents, which is determined by the liaison officer according to the requirements of the Ag ∈ T collaboration target. (5) S stands for coordination strategy, that is, the coordination strategy of the relationship between agents participating in S ∈ P cooperation [8,9]. e agent cooperation process is that once a new job is received, it first checks whether it is active and sends the work to the coordinating agent; otherwise, it matches its own capabilities, if it can be done independently; if the work cannot be done independently, then it is forwarded to the coordinating agent for cooperation.…”

Section: Theoretical Analysis and Methodsmentioning

confidence: 99%

Study about Law Multi-Issue Automatic Negotiation Method Based on Artificial Intelligence and Multiagent Evolutionary Algorithm

Hsu

Liu

2021

Mobile Information Systems

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Multiagent System (MAS) is a self-learning intelligent system formed by many single agents. Each agent in the MAS works independently of the other, and they have all the characteristics of an agent system. It can respond to changes and countermeasures based on its own external environmental conditions. When solving a complex problem, multiple agents can form a group to solve the problem together. In this paper, the agent’s evolutionary algorithm is integrated into the actual problem—multi-issue autonegotiation of law. According to this problem, the autonegotiation solution process and corresponding model are designed. In addition, a new type of solution is proposed for multiple legal issues. Compared with traditional solutions, the applicability has great advantages. Among them, the autonegotiation result of all the agent’s total utility can be quickly found. In the changing environment, this article focuses on the multiagent system negotiation problem. According to the distributed information sharing of multiple agents, even if the case reveals incomplete information, the multiagent can be generated while ignoring the incomplete information. Optimal solution is proposed. The experimental results show that the success rate of the system in analyzing multiple legal issues and autonegotiations reached 67.56% under the condition of incomplete information from the outside world.

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Section: Theoretical Analysis and Methodsmentioning

confidence: 99%

Study about Law Multi-Issue Automatic Negotiation Method Based on Artificial Intelligence and Multiagent Evolutionary Algorithm

Hsu

Liu

2021

Mobile Information Systems

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“…However, most of the existing control protocols only adopts the present information to generate the distributed control laws, some researchers propose to use predictive control to predict the future actions of neighboring agents [70]. In [71][72][73][74], a model predictive control is presented to accelerate the convergence speed to realize dynamic consensus of the MAS.…”

Section: K P T P T C Ve T Ve T B P T P T Ve T Ve Tmentioning

confidence: 99%

“…The realization of consensus in MAS is one of the most important direction to achieve the coordinated control. In this paper, the MAS-based consensus control schemes are systematically reviewed [62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77].…”

Section: Introductionmentioning

confidence: 99%

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

345

182

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Abstract-The increasing integration of the distributed renewable energy sources highlights the requirement to design various control strategies for microgrids (MGs) and microgrid clusters (MGCs). The multi-agent system (MAS)-based distributed coordinated control strategies shows the benefits to balance the power and energy, stabilize voltage and frequency, achieve economic and coordinated operation among the MGs and MGCs. However, the complex and diverse combinations of distributed generations in multi-agent system increase the complexity of system control and operation. In order to design the optimized configuration and control strategy using MAS, the topology models and mathematic models such as the graph topology model, non-cooperative game model, the genetic algorithm and particle swarm optimization algorithm are summarized. The merits and drawbacks of these control methods are compared. Moreover, since the consensus is a vital problem in the complex dynamical systems, the distributed MAS-based consensus protocols are systematically reviewed. NOMENCLATURE

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“…Based on the findings in [41], the dynamics of minimum linear mean square equation is estimated by using the following Kalmanfilter:…”

Section: Distributed Robust Stochastic Voltage Restoration Controlmentioning

confidence: 99%

Robust distributed stochastic secondary control of microgrids with system and communication noises

Dehkordi

Nekoukar

2020

IET Generation, Transmission & Distribution

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Robust consensus for linear multi‐agent systems with noises

Cited by 21 publications

References 28 publications

Study about Law Multi-Issue Automatic Negotiation Method Based on Artificial Intelligence and Multiagent Evolutionary Algorithm

Study about Law Multi-Issue Automatic Negotiation Method Based on Artificial Intelligence and Multiagent Evolutionary Algorithm

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Robust distributed stochastic secondary control of microgrids with system and communication noises

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