Statistical Machine Learning for Model Predictive Control of Nonlinear Processes

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

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.