68 citations · 76 across the 9 of their papers we have counts for
6 papers · 1 filter
Data-Driven Control with Inherent Lyapunov Stability
Youngjae Min, Spencer M. Richards, Navid Azizan
Recent advances in learning-based control leverage deep function approximators, such as neural networks, to model the evolution of controlled dynamical systems over time. However,…
Learning Control-Oriented Dynamical Structure from Data
Spencer M. Richards, Jean-Jacques Slotine, Navid Azizan +1
Even for known nonlinear dynamical systems, feedback controller synthesis is a difficult problem that often requires leveraging the particular structure of the dynamics to induce a…
Adaptive Robust Model Predictive Control via Uncertainty Cancellation
Rohan Sinha, James Harrison, Spencer M. Richards +1
We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally…
Control Barrier Functions for Cyber-Physical Systems and Applications to NMPC
Jan Schilliger, Thomas Lew, Spencer M. Richards +3
Tractable safety-ensuring algorithms for cyber-physical systems are important in critical applications. Approaches based on Control Barrier Functions assume continuous enforcement,…
Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty
Rohan Sinha, James Harrison, Spencer M. Richards +1
We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally…
The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems
Spencer M. Richards, Felix Berkenkamp, Andreas Krause
Learning algorithms have shown considerable prowess in simulation by allowing robots to adapt to uncertain environments and improve their performance. However, such algorithms are…