NEW AND SCALABLE PARADIGMS FOR DATA-DRIVEN MODEL PREDICTIVE CONTROL

控制和自动化技术有助于确保社会系统(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。传感技术的进步使开发更有效的控制技术成为可能，但此类设备生成的数据格式复杂(例如，视觉和热像)，需要大量额外处理才能与当前的自动控制系统兼容。该项目的目标是开发能够使用复杂格式的数据源进行控制的数学方法。这些数据将被用来构建要控制的系统的数学模型，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。该项目将为有效利用复杂数据(而不是单点测量)的模型预测控制(MPC)开发可扩展的范例。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。具体地说，拓扑学将被用作一个通用框架，它有助于表示附加到复杂空间(点云、场/流形和图形/网络)的数据，并使这些数据能够简化为可用于控制的信息性拓扑描述符。然后，这些描述符将被用于使用ML(例如，递归神经网络)在低维空间中构建数据驱动的动态模型，这些模型随后将被嵌入到MPC公式中。为了指导数据收集，将开发贝叶斯预测公式，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少模型不确定性(探索)和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且可扩展的不确定性量化策略，该策略可以与基于ML/物理的大型模型一起工作，从而使研究计算可处理性和性能之间的相互作用成为可能。这一新的MPC公式的有效性将通过在能源、制造和材料系统中的应用得到证明。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.