Session: 20-17-01: Rising Stars of Mechanical Engineering

Paper Number: 172156

Data-Enabled Neural Multi-Step Predictive Control (Demuspc): A Learning-Based Predictive and Adaptive Control Approach for Complex Nonlinear Systems 

This Faculty Early Career Development (CAREER) grant will fund research that enables new knowledge related to a data-enabled automatic control approach for complex processes that are hard to describe from first principles and are changing over time, promoting the progress of science and advancing national health and prosperity. Among all the processes with the properties mentioned above, one compelling example is the regulation of blood glucose in people with type 1 diabetes using exogenous insulin. Insulin therapy is affected by a considerable number of unknown and hidden physiological variables that change with patient's lifestyle and growth/aging, requiring frequent interventions by patients and their caregivers. Despite intensive and burdensome treatment, the majority of patients still fail to meet their prescribed glycemic targets, leading to complications that are costly to both the individual and the healthcare system. This project will support fundamental research to provide needed knowledge for developing data-driven and learning-based predictive and adaptive automatic control. The success of this project will enable a framework for optimal regulation and adaptation to changes with application in healthcare, biomedical, advanced manufacturing, chemical, or automotive industries. By combining advances in both ML and control theory in a tight loop, the proposed work will advance knowledge on how to solve nonlinear predictive control problems with light computational load by exploiting data-driven control-oriented neural multi-step output predictors that satisfy prescribed behavioral constraints. This project will provide foundational theory and algorithms for Data-Enabled neural Multi-Step Predictive control (DeMuSPc). The research is integrated with educational and outreach activities to broaden participation of groups traditionally underrepresented in control research and contribute positively to engineering education.

This research aims to make fundamental contributions to data-enabled predictive and adaptive control to overcome several limitations affecting existing predictive control approaches, including large errors in the model predictions for long prediction horizons due to large plant-model mismatch and unmodeled dynamics, as well as policy parameters that are static and do not adapt to varying operating conditions. The project will (1) exploit the use of multi-step ahead output predictors with a structure nonlinear in the state and affine in the future control moves, (2) identify the unknown mappings in the predictor parameterizations from input-output data by means of neural networks embedding prescribed behavioral guarantees in their structure, (3) integrate the predictors into a linear time-varying model predictive control framework, and (4) use Bayesian Optimization to tune and adapt the parameters of the controller to changes in the dynamics. The algorithms will be validated on the motivating examples of automated glucose regulation in people with type 1 diabetes by performing extensive in-silico trials with a metabolic simulator.

 

Presenting Author: Marzia Cescon University of Houston

Presenting Author Biography: Marzia Cescon is the David C. Zimmerman Assistant Professor of Mechanical and Aerospace Engineering at the University of Houston (UH). At UH she is also founder and director of the Advanced Learning, Artificial Intelligence and Control laboratory, a multidisciplinary effort for learning-based decision making and control of complex and potentially unknown dynamical systems. Dr. Cescon earned a bachelor’s degree in information engineering and a master’s degree in control systems engineering from the University of Padua, Italy, and received a Ph.D. in automatic control from Lund University, Sweden. She has held several research positions, including at the University of California at Santa Barbara, the University of Melbourne, and Harvard University. Dr. Cescon is the recipient of the Cornelis Drebbel Faculty Fellowship from TU Delft (2023), the NSF Career Award (2024) and the Cullen College of Engineering Early Inventor Award (2025).

Authors:

Marzia Cescon University of Houston