Data-driven nonlinear predictive control for feedback linearizable systems

被引：8

作者：

Alsalti, Mohammad ^{[1
]}

Lopez, Victor G. ^{[1
]}

Berberich, Julian ^{[2
]}

Allgoewer, Frank ^{[2
]}

Mueller, Matthias A. ^{[1
]}

机构：

[1] Leibniz Univ Hannover, Inst Automat Control, D-30167 Hannover, Germany

[2] Univ Stuttgart, Inst Syst Theory & Automat Control, D-70550 Stuttgart, Germany

来源：

IFAC PAPERSONLINE | 2023年 / 56卷 / 02期

基金：

欧洲研究理事会;

关键词：

Data-based control; Nonlinear predictive control; Data-driven predictive control; Feedback linearization;

D O I：

10.1016/j.ifacol.2023.10.1636

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present a data- driven nonlinear predictive control approach for the class of discrete-time multi-input multi-output feedback linearizable nonlinear systems. The scheme uses a non-parametric predictive model based only on input and noisy output data along with a set of basis functions that approximate the unknown nonlinearities. Despite the noisy output data as well as the mismatch caused by the use of basis functions, we show that the proposed multistep robust data-driven nonlinear predictive control scheme is recursively feasible and renders the closed-loop system practically exponentially stable. We illustrate our results on a model of a fully-actuated double inverted pendulum. Copyright (c) 2023 The Authors.

引用

页码：617 / 624

页数：8

共 22 条

[1]

Alsalti M., 2023, IEEE Transactions on Automatic Control, P1

[2]

Alsalti M, 2023, Arxiv, DOI arXiv:2303.08707

[3]

Alsalti M, 2022, Arxiv, DOI arXiv:2204.01150

[4] Linear Tracking MPC for Nonlinear Systems-Part II: The Data-Driven Case [J].