Hierarchical distributed model predictive control based on fuzzy negotiation

Masero, Eva; Francisco, Mario; Maestre, José María Maestre; Revollar, Silvana; Vega, Pastora

doi:10.1016/j.eswa.2021.114836

Cited by 14 publications

(5 citation statements)

References 41 publications

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“…Their work adds a new class of lease called Dynamic lease which can be renegotiated according to its current utilization. Masero et al [16] propose a fuzzy-based two-layer control architecture. In the lower control layer, there are pairwise negotiations between agents according to the couplings and the communication network.…”

Section: Related Workmentioning

confidence: 99%

Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities

Chu,

Singh,

Toor

2023

2023 IEEE 16th International Conference on Cloud Computing (CLOUD)

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In the past few decades, the rapid development of information and internet technologies has spawned massive amounts of data and information. The information explosion drives many enterprises or individuals to seek to rent cloud computing infrastructure to put their applications in the cloud. However, the agreements reached between cloud computing providers and clients are often not efficient. Many factors affect the efficiency, such as the idleness of the providers' cloud computing infrastructure, and the additional cost to the clients. One possible solution is to introduce a comprehensive, bargaining game (a type of negotiation), and schedule resources according to the negotiation results. We propose an agent-based auto-negotiation system for resource scheduling based on fuzzy logic. The proposed method can complete a one-to-one auto-negotiation process and generate optimal offers for the provider and client. We compare the impact of different member functions, fuzzy rule sets, and negotiation scenario cases on the offers to optimize the system. It can be concluded that our proposed method can utilize resources more efficiently and is interpretable, highly flexible, and customizable. We successfully train machine learning models to replace the fuzzy negotiation system to improve processing speed. The article also highlights possible future improvements to the proposed system and machine learning models. All the codes and data are available in the open-source repository.

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Section: Related Workmentioning

confidence: 99%

Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities

Chu,

Singh,

Toor

2023

2023 IEEE 16th International Conference on Cloud Computing (CLOUD)

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“…A multi-demand negotiation model implemented fuzzy rules obtained by psychological experiments were described by [65]. In two papers [7,66], models predictive control based on fuzzy negotiation were proposed.…”

Section: Fuzzy Approaches To Negotiation Supportmentioning

confidence: 99%

Reducing Cognitive Effort in Scoring Negotiation Space Using the Fuzzy Clustering Model

Filipowicz-Chomko

Mierzwiak

Nowak

et al. 2021

Entropy

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Negotiation scoring systems are fundamental tools used in negotiation support to facilitate parties searching for negotiation agreement and analyzing its efficiency and fairness. Such a scoring system is obtained in prenegotiation by implementing selected multiple criteria decision-aiding methods to elicit the negotiator’s preferences precisely and ensure that the support is reliable. However, the methods classically used in the preference elicitation require much cognitive effort from the negotiators, and hence, do not prevent them from using heuristics and making simple errors that result in inaccurate scoring systems. This paper aims to develop an alternative tool that allows scoring the negotiation offers by implementing a sorting approach and the reference set of limiting profiles defined individually by the negotiators in the form of complete packages. These limiting profiles are evaluated holistically and verbally by the negotiator. Then the fuzzy decision model is built that uses the notion of increasing the preference granularity by introducing a series of limiting sub-profiles for corresponding sub-categories of offers. This process is performed automatically by the support algorithm and does not require any additional preferential information from the negotiator. A new method of generating reference fuzzy scores to allow a detailed assignment of any negotiation offer from feasible negotiation space to clusters and sub-clusters is proposed. Finally, the efficient frontier and Nash’s fair division are used to identify the recommended packages for negotiation in the bargaining phase. This new approach allows negotiators to obtain economically efficient, fair, balanced, and reciprocated agreements while minimizing information needs and effort.

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“…Fuzzy logic (FL) is a potential solution to regulate the weight coefficient for model-predictive yaw control. FL is an abstraction of the approximate reasoning characteristics of human decision-making, which has been applied in many fields [14]. Yet, the excessive dependence of FL on expert experience leads to artificially set membership functions (MFs) and fuzzy rules that could limit the control performance.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Intelligent Predictive Control for the Yaw System of Large-Scale Wind Turbines

Song¹,

Li²,

Yang³

et al. 2023

Nonlinear Systems - Recent Developments and Advances

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This chapter presents a nonlinear intelligent predictive control using multi-step prediction model for the electrical motor-based yaw system of an industrial wind turbine. The proposed method introduces a finite control set under constraints for the demanded yaw rate, predicts the multi-step yaw error using the control set element and the prediction wind directions, and employs an exhaustive search method to search the control output candidate giving the minimal value of the objective function. As the objective function is designed for a joint power and actuator usage optimization, the weighting factor in the objective function is optimally determined by the fuzzy regulator that is optimized by an intelligent algorithm. Finally, the proposed method is demonstrated by simulation tests using real wind direction data.

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Hierarchical distributed model predictive control based on fuzzy negotiation

Cited by 14 publications

References 41 publications

Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities

Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities

Reducing Cognitive Effort in Scoring Negotiation Space Using the Fuzzy Clustering Model

Nonlinear Intelligent Predictive Control for the Yaw System of Large-Scale Wind Turbines

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