3 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
Gain Scheduling with a Neural Operator for a Transport PDE with Nonlinear Recirculation
Maxence Lamarque, Luke Bhan, Rafael Vazquez +1
To stabilize PDE models, control laws require space-dependent functional gains mapped by nonlinear operators from the PDE functional coefficients. When a PDE is nonlinear and its "…
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…