用元学习构建自适应模型，让控制算法在未知参数下仍能稳定高效运行。
MAKO: Meta-Adaptive Koopman Operators for Learning-based Model Predictive Control of Parametrically Uncertain Nonlinear Systems
- 基于元学习的自适应建模，从多模态数据中学习通用元模型
- 仅需少量在线数据即可适配新系统，控制闭环稳定性有保障
- 适合处理参数不确定的非线性系统控制问题，如机器人、自动驾驶
本文提出一种基于元学习的柯尔莫哥洛夫（Koopman）建模与预测控制方法，用于处理具有参数不确定性的非线性系统。提出一种名为MAKO的自适应深度元学习建模方法，无需预先知晓参数不确定性，即可从多模态数据集中学习元模型，并通过在线数据快速适应此前未见的参数设置。基于学习到的元柯尔莫哥洛夫模型，设计了预测控制方案，在面对未见过的参数配置时仍能保证闭环系统的稳定性。大量仿真结果表明，该方法在建模精度和控制效果上均优于现有基线方法。
原文摘要 · Abstract (English)
In this work, we propose a meta-learning-based Koopman modeling and predictive control approach for nonlinear systems with parametric uncertainties. An adaptive deep meta-learning-based modeling approach, called Meta Adaptive Koopman Operator (MAKO), is proposed. Without knowledge of the parametric uncertainty, the proposed MAKO approach can learn a meta-model from a multi-modal dataset and efficiently adapt to new systems with previously unseen parameter settings by using online data. Based on the learned meta Koopman model, a predictive control scheme is developed, and the stability of the closed-loop system is ensured even in the presence of previously unseen parameter settings. Through extensive simulations, our proposed approach demonstrates superior performance in both modeling accuracy and control efficacy as compared to competitive baselines.
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