Influence of fuzzy layer in distributed control structure applied to four coupled tanks

Morales-Rodelo, Keidy; Vega, Pastora; Francisco, Mario; Revollar, Silvana

doi:10.1109/ccac.2019.8920993

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The work in [33] uses a fuzzy Q-learning approach for controlling a chemical production line. Another approach of fuzzy logic in a chemical plant is proposed in [34].…”

Section: B Related Workmentioning

confidence: 99%

Coordinated Cooperative Distributed Decision-Making Using Synchronization of Local Plans

Kloock

Alrifaee

2023

IEEE Trans. Intell. Veh.

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In cooperative decision-making, agents locally plan for a subset of all agents. Due to only local system knowledge of the agents, these local plans are inconsistent to local plans of other agents. This inconsistency leads to infeasibility of the plans. This article introduces an algorithm for synchronizing local plans for cooperative distributed decision-making. The algorithm enables parallel decision-making and achieves feasible decisions. The algorithm consists of two iterative steps: local planning and global synchronization. In the local planning step, the agents parallely compute local decisions, referred to as plans. Subsequently, consistency of the local plans across agents is achieved using global synchronization. The globally synchronized plans act as reference decisions to the local planning step in the next iteration. In each iteration, the local planning guarantees locally feasible plans, while the synchronization guarantees globally consistent plans in that iteration. The parallel algorithm converges to globally feasible decisions if the coupling topology is feasible. We introduce requirements for the coupling topology to achieve convergence to globally feasible decisions and present the algorithm using a model predictive control example. Our evaluations with car-like robots show that globally feasible decisions are achieved.

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“…The work in [33] uses a fuzzy Q-learning approach for controlling a chemical production line. Another approach of fuzzy logic in a chemical plant is proposed in [34].…”

Section: B Related Workmentioning

confidence: 99%

Coordinated Cooperative Distributed Decision-Making Using Synchronization of Local Plans

Kloock

Alrifaee

2023

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

show abstract

“…The work in [21] uses a fuzzy Q-learning approach for controlling a chemical production line. Another approach of fuzzy logic in a chemical plant is proposed in [22].…”

Section: A Motivationmentioning

confidence: 99%

Coordinated Cooperative Distributed Decision-Making using Synchronization of Local Plans

Kloock¹,

Alrifaee²

2023

Preprint

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<p>Centralized decision-making for a Networked Control System (NCS) suffers from a high computational burden on the planning agent. Distributed agents, which compute cooperative decision-making, increase computational performance. In cooperative decision-making, agents locally plan for a subset of all agents. Due to only local system knowledge of the agents, these local plans are inconsistent with the local plans of other agents. This inconsistency leads to the infeasibility of plans. This article introduces an algorithm for synchronizing local plans for cooperative distributed decision-making. The algorithm enables parallel decision-making and achieves feasible decisions. The algorithm consists of two iterative steps: local planning and global synchronization. In the local planning step, the agents parallelly compute local decisions, referred to as plans. Subsequently, consistency of the local plans across agents is achieved using global synchronization. The globally synchronized plans act as reference decisions for the local planning step in the next iteration. In each iteration, the local planning guarantees locally feasible plans, while the synchronization guarantees globally consistent plans in that iteration. The parallel algorithm converges to globally feasible decisions if the coupling topology is feasible. We introduce requirements for the coupling topology to achieve convergence to globally feasible decisions and present the algorithm using a model predictive control example. Our evaluations with car-like robots show that globally feasible decisions are achieved.</p>

show abstract

Coordinated Cooperative Distributed Decision-Making using Synchronization of Local Plans

Kloock¹,

Alrifaee²

2022

Preprint

View full text Add to dashboard Cite

<p>In cooperative decision-making, agents locally plan for a subset of all agents. Due to only local system knowledge of the agents, these local plans may be inconsistent to local plans of other agents. This inconsistency leads to infeasibility of the plans. This article introduces an algorithm for synchronizing local plans for cooperative distributed decision-making of multi-agent systems. The algorithm consists of two iterative steps: planning and synchronization. In the local planning step, the agents compute local decisions, referred to as plans. Subsequently, consistency of the local plans across agents is achieved using synchronization. The synchronized plans act as reference decisions to the local planning step in the next iteration. In each iteration, the local planning guarantees locally feasible plans, while the synchronization guarantees globally consistent plans in that iteration. The algorithm converges to globally feasible decisions if the coupling topology is feasible. We introduce requirements for the coupling topology to achieve convergence to globally feasible decisions and present the algorithm using a model predictive control example. Our evaluations with car-like robots show that feasible decisions are achieved.</p>

show abstract

Influence of fuzzy layer in distributed control structure applied to four coupled tanks

Cited by 3 publications

References 11 publications

Coordinated Cooperative Distributed Decision-Making Using Synchronization of Local Plans

Coordinated Cooperative Distributed Decision-Making Using Synchronization of Local Plans

Coordinated Cooperative Distributed Decision-Making using Synchronization of Local Plans

Coordinated Cooperative Distributed Decision-Making using Synchronization of Local Plans

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