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.

中文翻译：

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有限持续时间任务的云辅助非线性模型预测控制

云计算通过提供强大的计算和存储能力为控制应用创造了新的可能性。在本文中，我们提出了一种新的云辅助模型预测控制 (MPC) 框架，在该框架中，我们系统地融合了使用高保真非线性模型的云 MPC，但会受到与利用简化动态的本地 MPC 的通信延迟（由于有限的计算）但有及时的反馈。与在网络系统控制设置中将云视为强大、远程和唯一控制器的传统基于云的控制不同，所提出的框架旨在无缝集成两个控制器以提高性能。特别是，我们通过明确考虑模型不匹配和由于请求-响应通信延迟引起的错误来形式化有限持续时间任务的融合问题。我们分析了所提出的云辅助 MPC 框架的类似稳定性的特性，并建立了在该框架内稳健处理约束的方法，尽管存在工厂模型不匹配和干扰。然后开发融合方案以提高控制性能，同时满足类似稳定性的条件，其功效通过多个仿真示例得到证明，包括展示其工业应用潜力的汽车控制示例。