collaborators

5 papers

eess.SY2026

A Weak Notion of Symmetry for Dynamical Systems

Jake Welde, Pieter van Goor

Many nonlinear dynamical systems exhibit symmetry, affording substantial benefits for control design, observer architecture, and data-driven control. While the classical notion of…

eess.SY2026

Scalable Distributed Nonlinear Control Under Flatness-Preserving Coupling

Fengjun Yang, Jake Welde, Nikolai Matni

We study distributed control for a network of nonlinear, differentially flat subsystems subject to dynamic coupling. Although differential flatness simplifies planning and control…

eess.SY2026

Learning Flatness-Preserving Residuals for Pure-Feedback Systems

Fengjun Yang, Jake Welde, Nikolai Matni

We study residual dynamics learning for differentially flat systems, where a nominal model is augmented with a learned correction term from data. A key challenge is that generic re…

cs.RO2025

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking

Pratik Kunapuli, Jake Welde, Dinesh Jayaraman +1

Learning-based control approaches like reinforcement learning (RL) have recently produced a slew of impressive results for tasks like quadrotor trajectory tracking and drone racing…

cs.RO2025

Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic Systems

Jake Welde, Nishanth Rao, Pratik Kunapuli +2

Tracking controllers enable robotic systems to accurately follow planned reference trajectories. In particular, reinforcement learning (RL) has shown promise in the synthesis of co…