118 citations · 123 across the 5 of their papers we have counts for
5 papers
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
Robustly Learning Regions of Attraction from Fixed Data
Matteo Tacchi, Yingzhao Lian, Colin Jones
While stability analysis is a mainstay for control science, especially computing regions of attraction of equilibrium points, until recently most stability analysis tools always re…
A comparison of methods to eliminate regularization weight tuning from data-enabled predictive control
Manuel Koch, Colin N. Jones
Data-enabled predictive control (DeePC) is a recently established form of Model Predictive Control (MPC), based on behavioral systems theory. While eliminating the need to explicit…
Stability Verification of Neural Network Controllers using Mixed-Integer Programming
Roland Schwan, Colin N. Jones, Daniel Kuhn
We propose a framework for the stability verification of Mixed-Integer Linear Programming (MILP) representable control policies. This framework compares a fixed candidate policy, w…
On Turnpike and Dissipativity Properties of Continuous-Time Optimal Control Problems
Timm Faulwasser, Milan Korda, Colin N. Jones +1
This paper investigates the relations between three different properties, which are of importance in optimal control problems: dissipativity of the underlying dynamics with respect…