14 citations · 49 across the 17 of their papers we have counts for
8 papers · 1 filter
Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles
Wenceslao Shaw Cortez, Soumya Vasisht, Aaron Tuor +2
Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs). In contrast, purely d…
Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach
Wenceslao Shaw Cortez, Jan Drgona, Aaron Tuor +2
We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions. DPC is an unsupervised learning-based m…
Structural Inference of Networked Dynamical Systems with Universal Differential Equations
James Koch, Zhao Chen, Aaron Tuor +2
Networked dynamical systems are common throughout science in engineering; e.g., biological networks, reaction networks, power systems, and the like. For many such systems, nonlinea…
Neural Lyapunov Differentiable Predictive Control
Sayak Mukherjee, Ján Drgoňa, Aaron Tuor +2
We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees. The neural Lyapuno…
Neuro-physical dynamic load modeling using differentiable parametric optimization
Shrirang Abhyankar, Jan Drgona, Andrew August +2
In this work, we investigate a data-driven approach for obtaining a reduced equivalent load model of distribution systems for electromechanical transient stability analysis. The pr…
Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem
Ethan King, Jan Drgona, Aaron Tuor +4
The dynamics-aware economic dispatch (DED) problem embeds low-level generator dynamics and operational constraints to enable near real-time scheduling of generation units in a powe…
Neural Ordinary Differential Equations for Nonlinear System Identification
Aowabin Rahman, Ján Drgoňa, Aaron Tuor +1
Neural ordinary differential equations (NODE) have been recently proposed as a promising approach for nonlinear system identification tasks. In this work, we systematically compare…
Learning Stochastic Parametric Differentiable Predictive Control Policies
Ján Drgoňa, Sayak Mukherjee, Aaron Tuor +2
The problem of synthesizing stochastic explicit model predictive control policies is known to be quickly intractable even for systems of modest complexity when using classical cont…