Cloud-Assisted Nonlinear Model Predictive Control for Finite-Duration Tasks

DOI10.1109/TAC.2022.3219293arXiv2106.10604MaRDI QIDQ6047028

Author name not available (Why is that?)

Publication date: 9 October 2023

Published in: IEEE Transactions on Automatic Control (Search for Journal in Brave)

Abstract: Cloud computing creates new possibilities for control applications by offering powerful computation and storage capabilities. In this paper, we propose a novel cloud-assisted model predictive control (MPC) framework in which we systematically fuse a cloud MPC that uses a high-fidelity nonlinear model but is subject to communication delays with a local MPC that exploits simplified dynamics (due to limited computation) but has timely feedback. Unlike traditional cloud-based control that treats the cloud as powerful, remote, and sole controller in a networked-system control setting, the proposed framework aims at seamlessly integrating the two controllers for enhanced performance. In particular, we formalize the fusion problem for finite-duration tasks by explicitly considering model mismatches and errors due to request-response communication delays. We analyze stability-like properties of the proposed cloud-assisted MPC framework and establish approaches to robustly handling constraints within this framework in spite of plant-model mismatch and disturbances. A fusion scheme is then developed to enhance control performance while satisfying stability-like conditions, the efficacy of which is demonstrated with multiple simulation examples, including an automotive control example to show its industrial application potentials.

Full work available at URL: https://arxiv.org/abs/2106.10604

zbMATH Keywords

model predictive control (MPC)control fusion cloud computing

Mathematics Subject Classification ID

Systems theory; control (93-XX)