Statistical Machine Learning for Model Predictive Control of Nonlinear Processes

用于非线性过程模型预测控制的统计机器学习

• 批准号: 2140506
• 负责人: Panagiotis Christofides
• 金额: $ 35.11万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140506&HistoricalAwards=false
• 关键词: Statistical; Machine; Learning; Model; Predictive

Machine learning (ML) has attracted increased attention in recent years due to its ability to uncover patterns in large sets of data (“big data") and its widening application in classical engineering fields. Traditionally, process control systems rely on a linear data-driven models, and in certain cases on first-principles models. However, modeling large-scale, complex nonlinear processes continues to be a major challenge in process systems engineering. Since process models are key elements of advanced model-based control systems, such as model predictive control (MPC) and economic MPC, building, training, and characterizing the accuracy of ML models is a new frontier in control system design that will impact the next generation of industrial control systems. Motivated by this, the goal of the proposed research program is to employ and further advance the methodological framework of generalized error bounds from machine learning theory for the development and verification of machine learning models and to integrate these models into predictive control system design for broad classes of nonlinear chemical processes. The research results and software tools will be incorporated within the undergraduate process control and senior design/process economics course curricula at UCLA to introduce students to the applications of machine learning techniques in accordance with departmental and campus goals. The project will also involve a diverse group of undergraduate and graduate students in the research through participation in the Center for Engineering Education and Diversity at UCLA, outreach to high school students and teachers, and outreach to the California State Polytechnic University in Pomona and the predominantly Hispanic El-Camino College. The goal of the proposed research program is to employ and advance the methodological framework of generalized error bounds from machine learning theory for the development and verification of machine learning models with specific theoretical accuracy guarantees and integrate these models into model predictive control (MPC) and economic MPC system design for nonlinear chemical processes. Specifically, this research will focus on the following broad objectives: a) the development of generalized probabilistic error bounds for machine learning models accounting for the impact of the number of neurons and layers on accuracy and guiding network structure selection and training, b) the design of model predictive control schemes that incorporate machine learning models in the form of process-structure aware recursive neural networks that are computationally efficient and ensure desired closed-loop stability, performance, robustness and operational safety properties, c) the development of a methodology for on-line adaptation of machine learning models in model predictive control to capture changing process dynamics and model uncertainty using real-time noisy data, and d) applications of the machine learning modeling and control methods to high-fidelity, large-scale process simulators incorporating process-data informed parameters and noise from data sets provided by industrial collaborators as well as from an experimental electrochemical reactor system developed within the research team for the reduction of CO and CO2. While the research will be carried out in the context of chemical process control systems synthesis, the resulting design framework will broadly impact manufacturing processes across a wide range of industrial sectors as well as smart devices that currently make use of model predictive control.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.

近年来，机器学习(ML)因其能够发现大数据(大数据)中的模式以及在经典工程领域的广泛应用而受到越来越多的关注。传统上，过程控制系统依赖于线性数据驱动模型，在某些情况下依赖于第一原理模型。然而，大规模、复杂的非线性过程的建模仍然是过程系统工程中的一大挑战。由于过程模型是先进的基于模型的控制系统的关键要素，如模型预测控制(MPC)和经济预测控制(MPC)，因此建立、训练和表征ML模型的精度是控制系统设计的新前沿，它将影响下一代工业控制系统。在此基础上，提出的研究计划的目标是采用并进一步发展机器学习理论中的广义误差界的方法框架，以开发和验证机器学习模型，并将这些模型集成到广泛类别的非线性化工过程的预测控制系统设计中。研究成果和软件工具将被纳入加州大学洛杉矶分校的本科过程控制和高级设计/过程经济学课程，向学生介绍符合部门和校园目标的机器学习技术的应用。该项目还将通过参与加州大学洛杉矶分校的工程教育和多样性中心，接触高中学生和教师，以及接触波莫纳的加州州立理工大学和以西班牙裔为主的El-Camino学院，让不同的本科生和研究生参与研究。该研究计划的目标是将机器学习理论中的广义误差界方法框架用于开发和验证具有特定理论精度保证的机器学习模型，并将这些模型集成到非线性化工过程的模型预测控制(MPC)和经济型MPC系统设计中。具体地说，这项研究将集中于以下广泛的目标：a)开发机器学习模型的广义概率误差界，考虑神经元和层的数量对精度的影响并指导网络结构选择和训练，b)设计模型预测控制方案，该模型预测控制方案将机器学习模型以过程结构感知递归神经网络的形式结合在一起，其计算效率高，并确保期望的闭环系统稳定性、性能、稳健性和操作安全特性，c)开发用于模型预测控制中的机器学习模型的在线适配的方法，以使用实时噪声数据捕捉变化的过程动态和模型不确定性，以及d)将机器学习建模和控制方法应用于高保真的大规模过程模拟器，该模拟器结合了来自工业合作者提供的数据集的过程数据通知的参数和噪声，以及来自研究团队内为减少CO和CO2而开发的实验电化学反应器系统。虽然这项研究将在化学过程控制系统综合的背景下进行，但由此产生的设计框架将广泛影响广泛的工业部门以及目前使用模型预测控制的智能设备的制造流程。该奖项反映了NSF的法定使命，并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。