10 papers
Physics-Informed Learning of Feedback-Linearizing Representations
Pavlos Kallinikidis, Fengjun Yang, David Snyder +4
Feedback linearization is a powerful tool in nonlinear control, but finding the linearizing coordinate transform remains challenging. Feedback linearizing transforms are governed b…
Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation Flight
Pei-An Hsieh, Fengjun Yang, Nikolai Matni +1
Quadrotors flying in tight formations are severely affected by turbulent aerodynamic interactions, such as downwash, that can cause catastrophic collisions if left unmodeled. To co…
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…
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…
A Data-Driven Approach to Synthesizing Dynamics-Aware Trajectories for Underactuated Robotic Systems
Anusha Srikanthan, Fengjun Yang, Igor Spasojevic +3
We consider joint trajectory generation and tracking control for under-actuated robotic systems. A common solution is to use a layered control architecture, where the top layer use…
Distributed Optimal Control of Graph Symmetric Systems via Graph Filters
Fengjun Yang, Fernando Gama, Somayeh Sojoudi +1
Designing distributed optimal controllers subject to communication constraints is a difficult problem unless structural assumptions are imposed on the underlying dynamics and infor…
Toward Certified Robustness Against Real-World Distribution Shifts
Haoze Wu, Teruhiro Tagomori, Alexander Robey +6
We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts. To do so, we bridge the gap between hand-crafted specifications…
Data-driven Distributed and Localized Model Predictive Control
Carmen Amo Alonso, Fengjun Yang, Nikolai Matni
Motivated by large-scale but computationally constrained settings, e.g., the Internet of Things, we present a novel data-driven distributed control algorithm that is synthesized di…
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,…