An integrated fuzzy and learning approach to performance improvement of model-based multi-agent robotic control systems

Erfu Yang; Dongbing Gu

doi:10.1109/ICMA.2007.4303757

An integrated fuzzy and learning approach to performance improvement of model-based multi-agent robotic control systems

Erfu Yang^*, Dongbing Gu

^*Corresponding author for this work

Design, Manufacturing And Engineering Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

Abstract

This paper presents an integrated approach to improving the performance of model-based control for multi-agent robotic systems (MARS). The fuzzy logic and learning techniques are compactly and efficiently integrated into the proposed approach to yield an improved formation controller for MARS while ensuring the stability obtained from model-based control systems. As a case study the proposed approach is applied to a leader-follower MARS where the robotic leader agent has its own target and the robotic follower agent is constrained by formation tasks. Simulation results are presented to demonstrate the effectiveness of the integrated fuzzy and learning approach.

Original language	English
Title of host publication	International Conference on Mechatronics and Automation, 2007. ICMA 2007
Place of Publication	Piscataway, NJ.
Publisher	IEEE
Pages	1417-1422
Number of pages	6
ISBN (Print)	9781424408283
DOIs	https://doi.org/10.1109/ICMA.2007.4303757
Publication status	Published - 2007
Event	2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007 - Harbin, United Kingdom Duration: 5 Aug 2007 → 8 Aug 2007

Conference

Conference	2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007
Country/Territory	United Kingdom
City	Harbin
Period	5/08/07 → 8/08/07

Keywords

fuzzy logic and learning
model-based control
multi-agent systems
performance improvement
robotics

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10.1109/ICMA.2007.4303757

Cite this

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title = "An integrated fuzzy and learning approach to performance improvement of model-based multi-agent robotic control systems",

abstract = "This paper presents an integrated approach to improving the performance of model-based control for multi-agent robotic systems (MARS). The fuzzy logic and learning techniques are compactly and efficiently integrated into the proposed approach to yield an improved formation controller for MARS while ensuring the stability obtained from model-based control systems. As a case study the proposed approach is applied to a leader-follower MARS where the robotic leader agent has its own target and the robotic follower agent is constrained by formation tasks. Simulation results are presented to demonstrate the effectiveness of the integrated fuzzy and learning approach.",

keywords = "fuzzy logic and learning, model-based control, multi-agent systems, performance improvement, robotics",

author = "Erfu Yang and Dongbing Gu",

year = "2007",

doi = "10.1109/ICMA.2007.4303757",

language = "English",

isbn = "9781424408283",

pages = "1417--1422",

booktitle = "International Conference on Mechatronics and Automation, 2007. ICMA 2007",

publisher = "IEEE",

note = "2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007 ; Conference date: 05-08-2007 Through 08-08-2007",

}

Yang, E & Gu, D 2007, An integrated fuzzy and learning approach to performance improvement of model-based multi-agent robotic control systems. in International Conference on Mechatronics and Automation, 2007. ICMA 2007., 4303757, IEEE, Piscataway, NJ., pp. 1417-1422, 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007, Harbin, United Kingdom, 5/08/07. https://doi.org/10.1109/ICMA.2007.4303757

TY - GEN

T1 - An integrated fuzzy and learning approach to performance improvement of model-based multi-agent robotic control systems

AU - Yang, Erfu

AU - Gu, Dongbing

PY - 2007

Y1 - 2007

N2 - This paper presents an integrated approach to improving the performance of model-based control for multi-agent robotic systems (MARS). The fuzzy logic and learning techniques are compactly and efficiently integrated into the proposed approach to yield an improved formation controller for MARS while ensuring the stability obtained from model-based control systems. As a case study the proposed approach is applied to a leader-follower MARS where the robotic leader agent has its own target and the robotic follower agent is constrained by formation tasks. Simulation results are presented to demonstrate the effectiveness of the integrated fuzzy and learning approach.

AB - This paper presents an integrated approach to improving the performance of model-based control for multi-agent robotic systems (MARS). The fuzzy logic and learning techniques are compactly and efficiently integrated into the proposed approach to yield an improved formation controller for MARS while ensuring the stability obtained from model-based control systems. As a case study the proposed approach is applied to a leader-follower MARS where the robotic leader agent has its own target and the robotic follower agent is constrained by formation tasks. Simulation results are presented to demonstrate the effectiveness of the integrated fuzzy and learning approach.

KW - fuzzy logic and learning

KW - model-based control

KW - multi-agent systems

KW - performance improvement

KW - robotics

UR - http://www.scopus.com/inward/record.url?scp=37049037712&partnerID=8YFLogxK

UR - http://2007.ieee-icma.org/

UR - http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4303487

U2 - 10.1109/ICMA.2007.4303757

DO - 10.1109/ICMA.2007.4303757

M3 - Conference contribution book

AN - SCOPUS:37049037712

SN - 9781424408283

SP - 1417

EP - 1422

BT - International Conference on Mechatronics and Automation, 2007. ICMA 2007

PB - IEEE

CY - Piscataway, NJ.

T2 - 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007

Y2 - 5 August 2007 through 8 August 2007

ER -

An integrated fuzzy and learning approach to performance improvement of model-based multi-agent robotic control systems

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