26 citations · 115 across the 68 of their papers we have counts for
7 papers · 1 filter
Learning Reachability of Energy Storage Arbitrage
Tomás Tapia, Agustin Castellano, Enrique Mallada +1
Power systems face increasing weather-driven variability and, therefore, increasingly rely on flexible but energy-limited storage resources. Energy storage can buffer this variabil…
Data-driven Acceleration of MPC with Guarantees
Agustin Castellano, Shijie Pan, Enrique Mallada
Model Predictive Control (MPC) is a powerful framework for optimal control but can be too slow for low-latency applications. We present a data-driven framework to accelerate MPC by…
Data-driven Practical Stabilization of Nonlinear Systems via Chain Policies: Sample Complexity and Incremental Learning
Roy Siegelmann, Enrique Mallada
We propose a method for data-driven practical stabilization of nonlinear systems with provable guarantees, based on the concept of Nonparametric Chain Policies (NCPs). The approach…
Recurrent Control Barrier Functions: A Path Towards Nonparametric Safety Verification
Jixian Liu, Enrique Mallada
Ensuring the safety of complex dynamical systems often relies on Hamilton-Jacobi (HJ) Reachability Analysis or Control Barrier Functions (CBFs). Both methods require computing a fu…
Safety-Critical Control via Recurrent Tracking Functions
Jixian Liu, Enrique Mallada
This paper addresses the challenge of synthesizing safety-critical controllers for high-order nonlinear systems, where constructing valid Control Barrier Functions (CBFs) remains c…
Safe Physics-Informed Machine Learning for Dynamics and Control
Jan Drgona, Truong X. Nghiem, Thomas Beckers +6
This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models…
Understanding the Learning Dynamics of LoRA: A Gradient Flow Perspective on Low-Rank Adaptation in Matrix Factorization
Ziqing Xu, Hancheng Min, Lachlan Ewen MacDonald +4
Despite the empirical success of Low-Rank Adaptation (LoRA) in fine-tuning pre-trained models, there is little theoretical understanding of how first-order methods with carefully c…