AI 中文总结
研究如何提升模型预测控制（MPC）的适应性，提出结合策略优化与元学习的框架，能快速适应未见任务并保持高性能，还集成系统识别优化超参数和预测模型，在球板系统验证了方法的卓越适应性。
AI 中文摘要
将策略优化应用于模型预测控制（MPC）可产生高性能且可靠的控制器。然而，所得控制器常过度拟合其训练条件，在未见任务中性能显著下降。我们提出一种将策略优化与元学习相结合的新框架来训练高度适应性的MPC控制器。该方法能快速适应未见任务，以全量重新训练所需计算成本的一小部分保持高性能。此外，我们将系统识别集成到流程中以不断优化MPC超参数和基础预测模型。我们在球板系统上验证了所提方法，在各种参数化轨迹跟踪任务中展示出卓越适应性。
英文摘要
Applying policy optimization to Model Predictive Control (MPC) yields high-performance and reliable controllers. However, the resulting controllers often overfit their training conditions and suffer significant performance degradation in unseen tasks. We propose a novel framework combining policy optimization with meta-learning to train highly adaptable MPC controllers. Our approach enables rapid adaptation to unseen tasks, maintaining high performance at a fraction of the computational cost required for full retraining. Furthermore, we integrate system identification into the pipeline to continuously refine both the MPC hyperparameters and the underlying predictive models. We validate our proposed methodology on a Ball-on-Plate system, demonstrating superior adaptability across various parameterized trajectory-tracking tasks.
CommentsThis work has been submitted to the IEEE for possible publication
