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
| Name | Member Since | Degree | Program | |
|---|---|---|---|---|
|
Tyler Hanks | 2021 | PhD | CISE |
|
Samuel Cohen | |||
|
Richard Samuelson | 2024 |
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Project Articles
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Modeling Model Predictive Control: A Category Theoretic Framework for Multistage Control Problems
American Control Conference (2024)
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Generalized Gradient Descent is a Hypergraph Functor
Applied Category Theory (2024)
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A Compositional Framework for First-Order Optimization
arxiv (2024)
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Characterizing Compositionality of LQR from the Categorical Perspective
IEEE Conf. Decision and Control (2023)
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Typed and stratified models with slice categories
Applied Category Theory 2022 (2022)
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An Algebraic Framework for Structured Epidemic Modeling
Proc. of the Royal Society Phil. Trans. (2022)
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Operadic Modeling of Dynamical Systems: Mathematics and Computation
Applied category Theory (Proceedings) (2021)
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AlgebraicDynamics: Compositional dynamical systems
JuliaCon, Online (2021)
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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