Componentwise Holder Inference for Robust Learning-Based MPC

José María Manzano

doi:10.1109/TAC.2021.3056356

Componentwise Holder Inference for Robust Learning-Based MPC

2021-11-01

11

OA

AI

J

José María Manzano *

D

David Muñoz de la Peña

DOI：10.1109/TAC.2021.3056356

Original

Save

View PDF

Abstract

En 中文

This article presents a novel learning method based on componentwise Holder 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.

Keywords:

Learning systems

Predictive models

Estimation

Uncertainty

Standards

Prediction algorithms

Interpolation

Inference algorithms

machine learning

nonlinear systems

predictive control

robust stability

Componentwise Holder Inference for Robust Learning-Based MPC

Abstract

AI Summary

Journal

Organization