4 papers
Foundations of Reinforcement Learning and Control:Connections and New Perspectives
Claire Vernade, Onno Eberhard, Martha White +4
Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback. While both fields…
Families of Control-Cost-Parametrized Inverse-Optimal Universal Stabilizers
Miroslav Krstic, Luke Bhan
A classical universal stabilization formula offers the practitioner no design freedom: it is a single, parameter-free object. We introduce a cost-parametrized family of stabilizing…
Sampling-Horizon Neural Operator Predictors for Nonlinear Control under Delayed Inputs
Luke Bhan, Peter Quawas, Miroslav Krstic +1
Modern control systems frequently operate under input delays and sampled state measurements. A common delay-compensation strategy is predictor feedback; however, practical implemen…
Predictor-Based Output-Feedback Control of Linear Systems with Time-Varying Input and Measurement Delays via Neural-Approximated Prediction Horizons
Luke Bhan, Miroslav Krstic, Yuanyuan Shi
Due to simplicity and strong stability guarantees, predictor feedback methods have stood as a popular approach for time delay systems since the 1950s. For time-varying delays, howe…