Multi-agent distributed model predictive control with fuzzy negotiation

被引：35

作者：

Francisco, M. ^{[1
]}

Mezquita, Y. ^{[2
]}

Revollar, S. ^{[1
]}

Vega, P. ^{[3
]}

De Paz, Juan F. ^{[3
]}

机构：

[1] Univ Salamanca, Comp & Automat Dept, Higher Tech Sch Ind Engn, Av Fernando Ballesteros, Salamanca 37700, Spain

[2] Univ Salamanca, BISITE Res Grp, Edificio I D i C Espejo S-N, Salamanca 37007, Spain

[3] Univ Salamanca, Comp & Automat Dept, Fac Sci, Pza Merced S-N, E-37008 Salamanca, Spain

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2019年 / 129卷

关键词：

Multi-agent system (MAS); Distributed model predictive control (DMPC); Fuzzy logic; Four coupled tanks system; IDENTIFICATION; SYSTEMS;

D O I：

10.1016/j.eswa.2019.03.056

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this work, a multi-agent distributed model predictive control (DMPC) including fuzzy negotiation has been developed. A novel fuzzy inference system is introduced as a negotiation technique between agents in a cooperative game algorithm, allowing for the consideration of economic criteria and process constraints within the negotiation process, providing an easier interpretation of the available knowledge. The fuzzy negotiation produces smoother control actions than where the negotiation is based only on costs evaluation, because both agents provide their best to generate the final control action. The results show good tracking and disturbance rejection in the case study proposed. The methodology has been implemented in a JAVA based platform with a friendly user interface to deploy the multi-agent system (MAS), and it has been validated in the water level control in a four coupled tanks system. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页码：68 / 83

页数：16

共 34 条

[1] A comparative analysis of distributed MPC techniques applied to the HD-MPC four-tank benchmark
Alvarado, I.
Limon, D.
Munoz de la Pena, D.
Maestre, J. M.
Ridao, M. A.
Scheu, H.
Marquardt, W.
Negenborn, R. R.
De Schutter, B.
Valencia, F.
Espinosa, J.
[J]. JOURNAL OF PROCESS CONTROL, 2011, 21 (05) : 800 - 815
[2] [Anonymous], 2009, MULTIAGENT MODEL PRE
[3] [Anonymous], 2000, Multiagent Systems: AModern Approach to DistributedArtificial Intelligence
[4] Shape-independent model predictive control for Takagi-Sugeno fuzzy systems
Arino, Carlos
Querol, Andres
Sala, Antonio
[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2017, 65 : 493 - 505
[5] Bai Y., 2006, Advanced Fuzzy Logic Technologies in Industrial Applications, P17, DOI DOI 10.1007/978-1-84628-469-4_2
[6] A new T-S fuzzy model predictive control for nonlinear processes
Boulkaibet, Ilyes
Belarbi, Khaled
Bououden, Sofiane
Marwala, Tshilidzi
Chadli, Mohammed
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2017, 88 : 132 - 151
[7] Camacho E.F., 2007, ADV TK CONT SIGN PRO, DOI 10.1007/978-0-85729-398-5
[8] Distributed MPC based consensus for single-integrator multi-agent systems
Cheng, Zhaomeng
Fan, Ming-Can
Zhang, Hai-Tao
[J]. ISA TRANSACTIONS, 2015, 58 : 112 - 120
[9] Fargallo L, 2017, ACT 38 JORN AUT ACT, P46
[10] An agent-based fuzzy constraint-directed negotiation model for solving supply chain planning and scheduling problems
Hsu, Chia-Yu
Kao, Bo-Ruei
Ho, Van Lam
Li, Lin
Lai, K. Robert
[J]. APPLIED SOFT COMPUTING, 2016, 48 : 703 - 715

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