Fault estimation based on intermediate observer for directed graph multi-mobile robot systems

Purpose Faults in multi-mobile robot systems (MMRS) can lead to task failures or other serious consequences. The purpose of this paper is to accurately estimate faults in cooperative MMRS, which communication topology is directed graphs, with scenarios such as collaborative transportation, and the authors propose a fault estimation method based on the intermediate observer. Design/methodology/approach The method solves the observer gain using the H8 performance criterion. It improves estimation accuracy by using centralized and local estimation errors. For calculating the observer’s parameter matrices, a solution to linear matrix inequality based on Schur decomposition theorem is proposed. Findings Simulation and physical experimental results demonstrate that, in MMRS cooperative tasks, the proposed method can accurately estimate the fault of each robot. Originality/value The proposed method can accurately estimate faults caused by external interference, centralized errors and localized errors transmitted through communication, providing a basis for control decisions and mitigating the impact of faults on MMRS collaborative transportation with the effectiveness of a greater number of robots and more complex scenarios awaits verification through subsequent research.

Authors

Institutions

Publication Details

Journal
Industrial Robot the international journal of robotics research and application
Published
2025-09-19
DOI
https://doi.org/10.1108/ir-01-2025-0012
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fault estimation based on intermediate observer for directed graph multi-mobile robot systems

Xudong Ji, Youdong Chen, Wei‐Ming Li
Industrial Robot the international journal of robotics research and application
Fault Detection and Control Systems
article

Fault estimation based on intermediate observer for directed graph multi-mobile robot systems

Xudong Ji, Youdong Chen, Wei‐Ming Li
article en

Abstract

Purpose Faults in multi-mobile robot systems (MMRS) can lead to task failures or other serious consequences. The purpose of this paper is to accurately estimate faults in cooperative MMRS, which communication topology is directed graphs, with scenarios such as collaborative transportation, and the authors propose a fault estimation method based on the intermediate observer. Design/methodology/approach The method solves the observer gain using the H8 performance criterion. It improves estimation accuracy by using centralized and local estimation errors. For calculating the observer’s parameter matrices, a solution to linear matrix inequality based on Schur decomposition theorem is proposed. Findings Simulation and physical experimental results demonstrate that, in MMRS cooperative tasks, the proposed method can accurately estimate the fault of each robot. Originality/value The proposed method can accurately estimate faults caused by external interference, centralized errors and localized errors transmitted through communication, providing a basis for control decisions and mitigating the impact of faults on MMRS collaborative transportation with the effectiveness of a greater number of robots and more complex scenarios awaits verification through subsequent research.

Industrial Robot the international journal of robotics research and applicationVol. 53(1)
Hefei University (CN), Hefei Institute of Technology Innovation (CN), Beihang University (CN)
Openalex Percentile: Top 68%
Fault Detection and Control Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.