9 papers
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…
Stabilization of nonlinear systems with unknown delays via delay-adaptive neural operator approximate predictors
Luke Bhan, Miroslav Krstic, Yuanyuan Shi
This work establishes the first rigorous stability guarantees for approximate predictors in delay-adaptive control of nonlinear systems, addressing a key challenge in practical imp…
Delay compensation of multi-input distinct delay nonlinear systems via neural operators
Filip Bajraktari, Luke Bhan, Miroslav Krstic +1
In this work, we present the first stability results for approximate predictors in multi-input non-linear systems with distinct actuation delays. We show that if the predictor appr…
Delay-adaptive Control of Nonlinear Systems with Approximate Neural Operator Predictors
Luke Bhan, Miroslav Krstic, Yuanyuan Shi
In this work, we propose a rigorous method for implementing predictor feedback controllers in nonlinear systems with unknown and arbitrarily long actuator delays. To address the an…
Neural Operators for Predictor Feedback Control of Nonlinear Delay Systems
Luke Bhan, Peijia Qin, Miroslav Krstic +1
Predictor feedback designs are critical for delay-compensating controllers in nonlinear systems. However, these designs are limited in practical applications as predictors cannot b…