Data-driven nonlinear predictive control for feedback linearizable systems

作     者：Mohammad Alsalti Victor G. Lopez Julian Berberich Frank Allgöwer Matthias A. Müller 

作者机构：Leibniz University Hannover Institute of Automatic Control 30167 Hannover Germany University of Stuttgart Institute for Systems Theory and Automatic Control 70550 Stuttgart Germany 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2023年第56卷第2期

页      面：617-624页

主　　题：Data-based control Nonlinear predictive control Data-driven predictive control Feedback linearization 

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 multi-step 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.