14 citations · 17 across the 13 of their papers we have counts for
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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…
Adaptive control of reaction-diffusion PDEs via neural operator-approximated gain kernels
Luke Bhan, Yuanyuan Shi, Miroslav Krstic
Neural operator approximations of the gain kernels in PDE backstepping has emerged as a viable method for implementing controllers in real time. With such an approach, one approxim…
Adaptive Neural-Operator Backstepping Control of a Benchmark Hyperbolic PDE
Maxence Lamarque, Luke Bhan, Yuanyuan Shi +1
To stabilize PDEs, feedback controllers require gain kernel functions, which are themselves governed by PDEs. Furthermore, these gain-kernel PDEs depend on the PDE plants' function…
Moving-Horizon Estimators for Hyperbolic and Parabolic PDEs in 1-D
Luke Bhan, Yuanyuan Shi, Iasson Karafyllis +2
Observers for PDEs are themselves PDEs. Therefore, producing real time estimates with such observers is computationally burdensome. For both finite-dimensional and ODE systems, mov…
Neural Operators of Backstepping Controller and Observer Gain Functions for Reaction-Diffusion PDEs
Miroslav Krstic, Luke Bhan, Yuanyuan Shi
Unlike ODEs, whose models involve system matrices and whose controllers involve vector or matrix gains, PDE models involve functions in those roles functional coefficients, depende…
Neural Operators for Bypassing Gain and Control Computations in PDE Backstepping
Luke Bhan, Yuanyuan Shi, Miroslav Krstic
We introduce a framework for eliminating the computation of controller gain functions in PDE control. We learn the nonlinear operator from the plant parameters to the control gains…