Time-Varying Optimal Sliding-Mode Lag Formation Control for High-Order Nonlinear Multiagent Systems Based on Reinforcement Learning

This paper considers a type of high-order nonlinear multiagent systems (MASs), and the time-varying optimal lag formation control problem for such MAS is addressed. To eliminate the influences of the unknown system dynamics and external disturbance on single agent, a neural network (NN) identifier and disturbance observer based compensation controller is developed. Then, an optimal control law to ensure the practical time-varying lag formation for the MAS is introduced by exploiting the sliding-mode control (SMC) method and reinforcement learning (RL) technique. Finally, a numerical example is given to validate the effectiveness of the proposed optimal control law.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Automation Science and Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tase.2025.3579744
Citations
2
Primary Topic
Advanced Algorithms and Applications
Type
article
Field-Weighted Citation Impact
2.43
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Time-Varying Optimal Sliding-Mode Lag Formation Control for High-Order Nonlinear Multiagent Systems Based on Reinforcement Learning

Qinhong Fu, Tingwen Huang, Jin-Liang Wang
2 citations
IEEE Transactions on Automation Science and Engineering
Advanced Algorithms and Applications
2.43
article

Time-Varying Optimal Sliding-Mode Lag Formation Control for High-Order Nonlinear Multiagent Systems Based on Reinforcement Learning

Qinhong Fu, Tingwen Huang, Jin-Liang Wang
article en
2 citations

Abstract

This paper considers a type of high-order nonlinear multiagent systems (MASs), and the time-varying optimal lag formation control problem for such MAS is addressed. To eliminate the influences of the unknown system dynamics and external disturbance on single agent, a neural network (NN) identifier and disturbance observer based compensation controller is developed. Then, an optimal control law to ensure the practical time-varying lag formation for the MAS is introduced by exploiting the sliding-mode control (SMC) method and reinforcement learning (RL) technique. Finally, a numerical example is given to validate the effectiveness of the proposed optimal control law.

IEEE Transactions on Automation Science and EngineeringVol. 22
Shenzhen University (CN), Tiangong University (CN), Shenzhen Technology University (CN)
Openalex Percentile: Top 11%
Advanced Algorithms and Applications
2.43
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.