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

• 成果类型: Article
• 署名作者: Li, Nan; Zhang, Kaixiang; Li, Zhaojian; Srivastava, Vaibhav; Yin, Xiang
• 署名单位: Auburn University System; Auburn University; Michigan State University; Michigan State University; Shanghai Jiao Tong University; Shanghai Jiao Tong University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3219293
• 发表日期: 2023
• 页码: 5287-5300
• 关键词: Cloud Computing; control fusion; model predictive control (MPC)

摘要

Cloud computing creates new possibilities for control applications by offering powerful computation and storage capabilities. In this article, 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.