activity
20142024
most citedTowards a Theory of Control Architecture: A quantitative framework for layered multi-rate control

4 citations · 19 across the 16 of their papers we have counts for

collaborators

16 papers

cs.LG2024

Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control

Bruce D. Lee, Leonardo F. Toso, Thomas T. Zhang +2

Representation learning is a powerful tool that enables learning over large multitudes of agents or domains by enforcing that all agents operate on a shared set of learned features…

cs.LG2024

Single Trajectory Conformal Prediction

Brian Lee, Nikolai Matni

We study the performance of risk-controlling prediction sets (RCPS), an empirical risk minimization-based formulation of conformal prediction, with a single trajectory of temporall…

math.OC20244 cited

Towards a Theory of Control Architecture: A quantitative framework for layered multi-rate control

Nikolai Matni, Aaron D. Ames, John C. Doyle

This paper focuses on the need for a rigorous theory of layered control architectures (LCAs) for complex engineered and natural systems, such as power systems, communication networ…

cs.RO20242 cited

Why Change Your Controller When You Can Change Your Planner: Drag-Aware Trajectory Generation for Quadrotor Systems

Hanli Zhang, Anusha Srikanthan, Spencer Folk +2

Motivated by the increasing use of quadrotors for payload delivery, we consider a joint trajectory generation and feedback control design problem for a quadrotor experiencing aerod…

math.OC20233 cited

Augmented Lagrangian Methods as Layered Control Architectures

Anusha Srikanthan, Vijay Kumar, Nikolai Matni

For optimal control problems that involve planning and following a trajectory, two degree of freedom (2DOF) controllers are a ubiquitously used control architecture that decomposes…

eess.SY2023

Safety Filter Design for Neural Network Systems via Convex Optimization

Shaoru Chen, Kong Yao Chee, Nikolai Matni +2

With the increase in data availability, it has been widely demonstrated that neural networks (NN) can capture complex system dynamics precisely in a data-driven manner. However, th…