no code implementations • 15 Aug 2022 • Wenceslao Shaw Cortez, Soumya Vasisht, Aaron Tuor, Ján Drgoňa, Draguna Vrabie
Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs).
no code implementations • 25 Jul 2021 • Jan Drgona, Aaron Tuor, Soumya Vasisht, Elliott Skomski, Draguna Vrabie
We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems.
no code implementations • 6 Jan 2021 • Elliott Skomski, Soumya Vasisht, Colby Wight, Aaron Tuor, Jan Drgona, Draguna Vrabie
Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics.
no code implementations • 26 Nov 2020 • Jan Drgona, Soumya Vasisht, Aaron Tuor, Draguna Vrabie
In this paper, we provide sufficient conditions for dissipativity and local asymptotic stability of discrete-time dynamical systems parametrized by deep neural networks.