Adaptive Distributed Observer-based Model Predictive Control for Multi-Agent Formation with Resilience to Communication Link Faults

To address the nonlinear multi-agent formation tracking problem with input constraints and unknown communication faults, this paper develops a novel adaptive distributed observer-based model predictive control (MPC) method. The design integrates adaptive distributed observers into local control systems to estimate the leader's state, dynamics, and desired displacement. By utilizing the observed information to construct local references, the original distributed formation tracking problem is decomposed into fully localized tracking control tasks, efficiently handled by local MPC controllers. This design enhances resilience against communication faults while simplifying the distributed MPC formulation.

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Publication Details

Published
2025-06-30
DOI
https://doi.org/10.1109/icca65672.2025.11129755
Citations
1
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
1.21

Funders

Controls
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article

Adaptive Distributed Observer-based Model Predictive Control for Multi-Agent Formation with Resilience to Communication Link Faults

Yang Shi, Binyan Xu, Yufan Dai, Afzal Suleman
1 citations
Advanced Control Systems Optimization
1.21
article

Adaptive Distributed Observer-based Model Predictive Control for Multi-Agent Formation with Resilience to Communication Link Faults

Yang Shi, Binyan Xu, Yufan Dai, Afzal Suleman
article en
1 citations

Abstract

To address the nonlinear multi-agent formation tracking problem with input constraints and unknown communication faults, this paper develops a novel adaptive distributed observer-based model predictive control (MPC) method. The design integrates adaptive distributed observers into local control systems to estimate the leader's state, dynamics, and desired displacement. By utilizing the observed information to construct local references, the original distributed formation tracking problem is decomposed into fully localized tracking control tasks, efficiently handled by local MPC controllers. This design enhances resilience against communication faults while simplifying the distributed MPC formulation.

University of Victoria (CA), University of Guelph (CA)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 17%
Advanced Control Systems Optimization
1.21
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