Distributed containment control for active leaders with time-varying inputs: PI-based and reset-based approaches

This paper investigates the containment control of double-integrator multi-agent systems with active leaders subject to time-varying inputs. A proportional–integral (PI) containment controller is developed to ensure asymptotic convergence under the disturbances, and a reset-based mechanism is incorporated into the integral term to enhance transient performance without the disturbances. Rigorous stability analyses are provided for both two controllers. It is further proved that the reset-based controller achieves faster convergence than the case without integral term, while each follower possesses regularity in every dimension. Finally, numerical simulations demonstrate the effectiveness and performance advantages of the proposed approaches.

Authors

Institutions

Publication Details

Journal
International Journal of Systems Science
Published
2026-08-03
DOI
https://doi.org/10.1080/00207721.2026.2711359
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Distributed containment control for active leaders with time-varying inputs: PI-based and reset-based approaches

Yinglun Mo, Wenfeng Hu, Tao Chen
International Journal of Systems Science
Reinforcement Learning in Robotics
article

Distributed containment control for active leaders with time-varying inputs: PI-based and reset-based approaches

Yinglun Mo, Wenfeng Hu, Tao Chen
article en

Abstract

This paper investigates the containment control of double-integrator multi-agent systems with active leaders subject to time-varying inputs. A proportional–integral (PI) containment controller is developed to ensure asymptotic convergence under the disturbances, and a reset-based mechanism is incorporated into the integral term to enhance transient performance without the disturbances. Rigorous stability analyses are provided for both two controllers. It is further proved that the reset-based controller achieves faster convergence than the case without integral term, while each follower possesses regularity in every dimension. Finally, numerical simulations demonstrate the effectiveness and performance advantages of the proposed approaches.

International Journal of Systems Science
Central South University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 7%
Reinforcement Learning in Robotics
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.