CAREER: Hypersampled Model Predictive Control: Reconciling Analog Systems with Digital Controllers

This Faculty Early Career Development Program (CAREER) grant will support research related to constrained control theory, promoting its dissemination in engineering science, practice, and education. Constrained control theory is a branch of mathematics aimed at steering a dynamical system to its desired target, while also enforcing a set of safety/quality restrictions.

This enabling technology allows engineering systems to live up to their full potential by operating at the limit of their specifications. Model Predictive Control (MPC) achieves this objective by solving an optimal control problem at every sampling instant. However, doing so requires a large number of computations in a relatively short amount of time, making the approach challenging to implement on high frequency engineering applications.

This award supports fundamental research for the development of a new constrained control paradigm that combines systems theory, numerical methods, and algorithmic optimization to reduce the computational effort and hardware requirements of MPC. Results from this research will enable the widespread adoption of MPC in the aerospace, automotive, energy, and robotic industries, thus benefitting the U.S. economy and society.

Hypersampled MPC can reduce the computational burden of traditional MPC by decoupling the prediction model, used to formulate the optimal control problem, from the control model, used to prove that the closed-loop system is asymptotically stable and satisfies constraints. This reformulation requires an extensive revision of existing MPC theory, where the two models are implicitly assumed the same.

This research project will lay the foundational groundwork for Hypersampled MPC and investigate its implications on how to handle warm-starting, preconditioning, implicit recursions, parametric uncertainties, and disturbance rejection. Experimental validation will be performed on standardized educational platforms and will be released as an open source plug-and-play toolbox.

The supporting documentation will then be expanded into a series of self-contained teaching modules for a hands-on laboratory class in constrained control, thereby promoting access to this transformational technology.

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.