.png){kind=icon}
.png){kind=icon}
Return
Distributed Iterative Learning Model Predictive Control for Unmanned Surface Vehicles
.png){kind=icon}
PRE
AI
DOI：10.1109/JAS.2025.125972.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Dear Editor, This letter proposes a distributed iterative learning model predictive control (LMPC) strategy for coordinated trajectory tracking of multiple unmanned surface vehicles (USVs). By learning from previously feasible control and state trajectories, each USV iteratively refines its input sequence to improve the accuracy of trajectory tracking and formation control. To tackle challenges such as system coupling, limited onboard computational resources, and communication constraints, the method integrates the alternating direction method of multipliers (ADMM) with iterative learning. The effectiveness and advantages of the proposed approach are demonstrated through comparison results.
Journal
I
IF:
0
Papers:
116
Citations:
0