7 citations · 7 across the 6 of their papers we have counts for
8 papers
Full-Order Safety for Nonlinear Reduced-Order MPC
Luis Pabon, Johannes Köhler, Revant Kasichainula +1
Reduced-order models (ROMs) make model predictive control (MPC) of high-dimensional nonlinear systems tractable, but the resulting modeling error can lead to constraint violations…
frax: Fast Robot Kinematics and Dynamics in JAX
Daniel Morton, Marco Pavone
In robot control, planning, and learning, there is a need for rigid-body dynamics libraries that are highly performant, easy to use, and compatible with CPUs and accelerators. Whil…
Learning Actuator-Aware Spectral Submanifolds for Precise Control of Continuum Robots
Paul Leonard Wolff, Hugo Buurmeijer, Luis Pabon +6
Continuum robots exhibit high-dimensional, nonlinear dynamics which are often coupled with their actuation mechanism. Spectral submanifold (SSM) reduction has emerged as a leading…
Taming High-Dimensional Dynamics: Learning Optimal Projections onto Spectral Submanifolds
Hugo Buurmeijer, Luis A. Pabon, John Irvin Alora +3
High-dimensional nonlinear systems pose considerable challenges for modeling and control across many domains, from fluid mechanics to advanced robotics. Such systems are typically…
Data-Driven Soft Robot Control via Adiabatic Spectral Submanifolds
Roshan S. Kaundinya, John Irvin Alora, Jonas G. Matt +3
The mechanical complexity of soft robots creates significant challenges for their model-based control. Specifically, linear data-driven models have struggled to control soft robots…
Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer (-MPC)
Luis Pabon, Johannes Köhler, John Irvin Alora +4
In Model Predictive Control (MPC), discrepancies between the actual system and the predictive model can lead to substantial tracking errors and significantly degrade performance an…
Learning for CasADi: Data-driven Models in Numerical Optimization
Tim Salzmann, Jon Arrizabalaga, Joel Andersson +2
While real-world problems are often challenging to analyze analytically, deep learning excels in modeling complex processes from data. Existing optimization frameworks like CasADi…
Robust Nonlinear Reduced-Order Model Predictive Control
John Irvin Alora, Luis A. Pabon, Johannes Köhler +5
Real-world systems are often characterized by high-dimensional nonlinear dynamics, making them challenging to control in real time. While reduced-order models (ROMs) are frequently…