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

TL;DR

This paper introduces a cloud-assisted nonlinear model predictive control framework that combines cloud and local MPCs to improve control performance for finite-duration tasks, accounting for communication delays and model mismatches.

Contribution

It proposes a novel fusion scheme integrating cloud and local MPCs, formalizes the fusion problem considering delays and mismatches, and analyzes stability and robustness for practical control applications.

Findings

Enhanced control performance demonstrated through simulations

Framework effectively handles communication delays and model mismatches

Potential for industrial automotive control applications

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.