Robust offset-free constrained Model Predictive Control with Long Short-Term Memory Networks -- Extended version

Autor:	Schimperna, Irene, Magni, Lalo
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
DOI:	10.1109/TAC.2024.3398494
Popis:	This paper develops a control scheme, based on the use of Long Short-Term Memory neural network models and Nonlinear Model Predictive Control, which guarantees recursive feasibility with slow time variant set-points and disturbances, input and output constraints and unmeasurable state. Moreover, if the set-point and the disturbance are asymptotically constant, offset-free tracking is guaranteed. Offset-free tracking is obtained by augmenting the model with a disturbance, to be estimated together with the states of the Long Short-Term Memory network model by a properly designed observer. Satisfaction of the output constraints in presence of observer estimation error, time variant set-points and disturbances is obtained using a constraint tightening approach. Comment: This work has been submitted to the IEEE for possible publication
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.17304 Zobrazit plný text záznamu View this record from Arxiv