Core Concepts
Employing a meta-learning approach called Portfolio to improve the efficiency and stability of the AutoMPC pipeline by warmstarting Bayesian Optimization for system identification tuning.
Abstract
The paper proposes a meta-learning approach called Portfolio to enhance the efficiency and stability of the AutoMPC pipeline for automated tuning of data-driven model predictive control (MPC).
Key highlights:
- AutoMPC is a framework that automates the tuning of data-driven MPC, but it can be computationally expensive and unstable when exploring large search spaces using pure Bayesian Optimization (BO).
- To address these issues, the authors employ a meta-learning approach called Portfolio that optimizes initial designs for BO using a diverse set of configurations from previous tasks and stabilizes the tuning process.
- Experiments on 11 nonlinear control simulation benchmarks and 1 physical underwater soft robot dataset demonstrate that Portfolio outperforms pure BO in finding desirable solutions for AutoMPC within limited computational resources.
- Portfolio can lead to faster convergence of AutoMPC tuning and more stable performance compared to pure BO.
- The impact of portfolio size on the tuning performance is investigated, showing that an appropriate portfolio size is important for achieving the best results.
- The superior model obtained through Portfolio-based tuning can also lead to better control performance.
Stats
The paper does not provide any specific numerical data or metrics to support the key claims. The results are presented in the form of tuning curves and comparative analysis.
Quotes
The paper does not contain any direct quotes from the content.