Cloud-Assisted Nonlinear Model Predictive Control for Finite-Duration Tasks
Cloud-Assisted Nonlinear Model Predictive Control for Finite-Duration Tasks
复制标题
有限持续时间任务的云辅助非线性模型预测控制
DOI：
10.1109/tac.2022.3219293
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发表时间：
2021-06
影响因子：
6.8
通讯作者：
Nan Li;Kaixiang Zhang;Zhaojian Li;Vaibhav Srivastava;Xiang Yin
中科院分区：
文献类型：
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作者：
Nan Li;Kaixiang Zhang;Zhaojian Li;Vaibhav Srivastava;Xiang Yin
云计算通过提供强大的计算和存储能力，为控制应用程序创造了新的可能性。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，我们系统地融合了一个云MPC，该云MPC利用云的计算能力来计算基于高保真非线性模型的最优控制（因此，更准确），但由于局部计算能力有限（因此，准确性较低），因此与依赖简化线性动力学的本地MPC存在通信延迟，而本地MPC具有及时反馈。与传统的基于云的控制不同，传统的基于云的控制将云视为网络控制系统设置中强大的、远程的、唯一的控制器，所提出的框架旨在无缝集成两个控制器以增强性能。特别是，我们形式化了有限持续时间任务的融合问题，明确考虑了由于请求-响应通信延迟导致的模型不匹配和错误。我们分析了所提出的云辅助MPC框架的稳定性类型属性，并建立了在该框架内稳健处理约束的方法，尽管存在植物模型不匹配和干扰。为了在满足稳定型条件的同时提高控制性能，提出了一种融合方案，并通过多个仿真实例验证了该方案的有效性，其中包括一个汽车控制实例，以展示其工业应用潜力。
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.