NEW AND SCALABLE PARADIGMS FOR DATA-DRIVEN MODEL PREDICTIVE CONTROL

数据驱动模型预测控制的新的、可扩展的范式

• 批准号: 2315963
• 负责人: Victor Zavala Tejeda
• 金额: $ 34.41万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2315963&HistoricalAwards=false
• 关键词: NEW; SCALABLE; PARADIGMS; DATA; DRIVEN

Control and automation technologies have been instrumental in ensuring that societal systems (e.g., buildings, power networks, manufacturing facilities, autonomous vehicles, materials/fuels production) are operated in a safe, reliable, and sustainable manner. Advances in sensing technologies make possible the development of more efficient control technologies, but such devices generate data in complex formats (e.g., visual and thermal images) that need significant additional processing to be compatible with current automatic control systems. The goal of this project is to develop mathematical methods that enable the use of complex-format data sources for control. These data will be used to construct mathematical models of the system to be controlled to predict the behavior of the system in a reliable manner and to quantify risks associated with inaccurate predictions. This project will also support the development of new educational materials and computational tools that will help K-12, undergraduate, and graduate students better visualize and make sense of complex data; such skills are essential for enabling data-driven science and engineering careers.This project will develop a scalable paradigm for model predictive control (MPC) that make effective use of complex data (as opposed to single-point measurements). This will be done by integrating concepts of control, topology, machine learning (ML), and Bayesian analysis. Specifically, topology will be used as a general framework that facilitates representation of data that is attached to complex spaces (point clouds, fields/manifolds, and graphs/networks) and that enables the reduction of such data into informative topological descriptors that can be used for control. These descriptors will then be used to construct data-driven, dynamical models in a low-dimensional space using ML (e.g., recurrent neural networks), models that then will be embedded in MPC formulations. To guide data collection, Bayesian MPC formulations will be developed which interpret the controller as a real-time experimental design oracle that aims to simultaneously gather information to mitigate model uncertainty (exploration) and to maximize control performance (exploitation). A key research objective is the development of fast and scalable uncertainty quantification strategies that can work with large ML/physics-based models, making it possible to study the interplay between computational tractability and performance. The effectiveness of this new MPC formulation will be demonstrated with applications in energy, manufacturing, and materials systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

控制和自动化技术在确保社会系统（例如，建筑物、电力网络、制造设施、自动驾驶车辆、材料/燃料生产）以安全、可靠和可持续的方式运行。传感技术的进步使得更有效的控制技术的开发成为可能，但是这样的设备以复杂的格式生成数据（例如，视觉和热图像），其需要显著的附加处理以与当前的自动控制系统兼容。这个项目的目标是开发数学方法，使复杂格式的数据源的控制使用。这些数据将用于构建待控制系统的数学模型，以可靠的方式预测系统的行为，并量化与不准确预测相关的风险。该项目还将支持开发新的教育材料和计算工具，帮助K-12、本科生和研究生更好地可视化和理解复杂数据;这些技能对于实现数据驱动的科学和工程职业至关重要。该项目将开发一个可扩展的模型预测控制（MPC）范例，有效利用复杂数据（相对于单点测量）。这将通过集成控制，拓扑，机器学习（ML）和贝叶斯分析的概念来完成。具体而言，拓扑将被用作一个通用框架，便于表示连接到复杂空间（点云，场/流形和图形/网络）的数据，并将这些数据简化为可用于控制的信息拓扑描述符。然后，这些描述符将用于使用ML在低维空间中构建数据驱动的动态模型（例如，递归神经网络），然后将嵌入MPC公式的模型。为了指导数据收集，将开发贝叶斯MPC公式，将控制器解释为实时实验设计预言，旨在同时收集信息以减轻模型不确定性（探索）并最大限度地提高控制性能（开发）。一个关键的研究目标是开发快速和可扩展的不确定性量化策略，这些策略可以与基于ML/物理的大型模型一起使用，从而可以研究计算易处理性和性能之间的相互作用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。