Publications (10)
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…
Coordinating Planning and Tracking in Layered Control Policies via Actor-Critic Learning
Fengjun Yang, Nikolai Matni
We propose a reinforcement learning (RL)-based algorithm to jointly train (1) a trajectory planner and (2) a tracking controller in a layered control architecture. Our algorithm ar…
Decentralized Role Assignment in Multi-Agent Teams via Empirical Game-Theoretic Analysis
Fengjun Yang, Negar Mehr, Mac Schwager
We propose a method, based on empirical game theory, for a robot operating as part of a team to choose its role within the team without explicitly communicating with team members,…
Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation Flight
Pei-An Hsieh, Fengjun Yang, Nikolai Matni +1
The paper introduces a physics‑informed residual dynamics learning method that keeps a multi‑quadrotor system differentially flat, enabling a fast feedback‑linearization controller…
Communication Topology Co-Design in Graph Recurrent Neural Network Based Distributed Control
Fengjun Yang, Nikolai Matni
When designing large-scale distributed controllers, the information-sharing constraints between sub-controllers, as defined by a communication topology interconnecting them, are as…
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…