.png){kind=icon}
.png){kind=icon}
Return
Efficient human-in-the-loop MPC tuning with multi-task preferential Bayesian optimization
.png){kind=icon}
OA
AI
DOI：10.1016/j.conengprac.2026.106972.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Multi-Task Preferential Bayesian Optimization (MTPBO) is proposed for efficient MPC tuning • MTPBO uses human preference data from previous tasks to accelerate tuning for new task • Multi-task initialization suggests promising comparisons to warm-start optimization • Validated on a real human-in-the-loop MPC tuning problem involving four tasks • MTPBO enables transfer learning of preferences across users and open-loop processes
Keywords:
Multi-Task Preferential Bayesian Optimization
Model Predictive Control
Human-in-the-Loop
Transfer Learning
Efficient Tuning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.6
Papers:
5.6K
Citations:
1.1W