Componentwise Hölder Inference for Robust Learning-Based MPC
Componentwise Hölder Inference for Robust Learning-Based MPC
复制标题
用于基于鲁棒学习的 MPC 的组件式 Hölder 推理
DOI：
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发表时间：
2021
影响因子：
6.8
通讯作者：
D. Limón
中科院分区：
文献类型：
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作者：
J. M. Manzano;D. M. D. L. Peña;Jan;D. Limón
本文提出了一种基于分量Hölder连续性的新的学习方法，它允许人们独立地考虑每个输入对要学习的函数的每个输出的贡献。该方法提供了一个有界的预测误差，并证明了其学习性能。它可以用来获得一个预测器的非线性鲁棒学习的预测控制器的约束系统。所得到的控制器实现了更好的闭环性能和更大的域的吸引力比学习方法，只考虑非线性集成员，如案例研究所示。
This article presents a novel learning method based on componentwise Hölder continuity, which allows one to consider independently the contribution of each input to each output of the function to be learned. The method provides a bounded prediction error, and its learning property is proven. It can be used to obtain a predictor for a nonlinear robust learning-based predictive controller for constrained systems. The resulting controller achieves better closed loop performance and larger domains of attraction than learning methods that only consider nonlinear set membership, as illustrated by a case study.