Algebraic Dynamics, Optimization, and Control
Model predictive control (MPC) is an optimal control technique which involves solving a sequence of constrained optimization problems across a given time horizon. We present a novel Julia library that leverages our theoretical results to automate the implementation of correct-by-construction MPC problems in software.
Project team
| Photo | Name | Member since | Degree | Program |
|---|---|---|---|---|
 | Tyler Hanks | 2021 | PhD | CISE |
 | Samuel Cohen | 2024 | — | — |
 | Richard Samuelson | 2024 | PhD | — |
Project articles
A Compositional Framework for First-Order Optimization
Generalized Gradient Descent is a Hypergraph Functor
Modeling Model Predictive Control: A Category Theoretic Framework for Multistage Control Problems
Characterizing Compositionality of LQR from the Categorical Perspective
An Algebraic Framework for Structured Epidemic Modeling
Typed and stratified models with slice categories
Operadic Modeling of Dynamical Systems: Mathematics and Computation
AlgebraicDynamics: Compositional dynamical systems
Sponsors
AlgebraicOptimization and Control has been supported by the following programs:
- NSF: Graduate Research Fellowship Program
- ONR: Domain Transfer for Continuity of Performance
- AFRL: Griffis Summer Internship Program