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

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 leverages the computing power of the cloud to compute optimal control based on a high-fidelity nonlinear model (thus, more accurate) but is subject to communication delays with a local MPC that relies on simplified linear dynamics due to limited local computation capability (thus, less accurate) while has timely feedback. Unlike traditional cloud-based control that treats the cloud as a powerful, remote, and sole controller in a networked control system 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 with explicit consideration for model mismatches and errors due to request-response communication delays. We analyze stability-type 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-type conditions, the efficacy of which is demonstrated with multiple simulation examples, including an automotive control example to show its industrial application potentials.