.png){kind=icon}
Return
Variable Sampling MPC via Differentiable Time-Warping Function
.png){kind=icon}
OA
AI
DOI：10.23919/ACC55779.2023.10155935.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Designing control inputs for a system that involves dynamical responses in multiple timescales is nontrivial. This paper proposes a parameterized time-warping function to enable a non-uniformly sampling along a prediction horizon given some parameters. The horizon should capture the responses under faster dynamics in the near future and preview the impact from slower dynamics in the distant future. Then a variable sampling MPC (VS-MPC) strategy is proposed to jointly determine optimal control and sampling parameters at each timestamp. VS-MPC adapts how it samples along the horizon and determines optimal control accordingly at each timestamp without offline tuning or trial and error. A numerical example of a wind farm battery energy storage system is also provided to demonstrate that VS-MPC outperforms the uniform sampling MPC.
Keywords:
MODEL-PREDICTIVE CONTROL
OPTIMIZATION
SYSTEMS
Journal
A
IF:
0
Papers:
75
Citations:
0
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSaveShareSaveShareSaveShareSave
View more