4 papers
Adaptive and Neural Operator Control of Nonlinear Volterra Hyperbolic PDEs
Miroslav Krstic
Adaptive control learns the plant online; neural-operator control learns the control gains offline. We bring the two together for a class of nonlinear hyperbolic PDEs whose dynamic…
Approximate Feedback Linearization for a Nonlinear Hyperbolic PDE Class -- Part II: Neural Operator
Miroslav Krstic
Volterra series feedback linearizes a class of nonlinear hyperbolic PDEs but produces a controller that, even after truncation, demands solving a tower of plant-specific kernel PDE…
Beyond Nonlinear Small-Gain Design: DADS with Partial-State Feedback
Iasson Karafyllis, Miroslav Krstic
Eduardo Sontag and coauthors studied Input-to-Output Stability (IOS) and the output asymptotic gain property. These notions changed control theory and recently had an impact on rob…
Backstepping Neural Operators for Hyperbolic PDEs
Shanshan Wang, Mamadou Diagne, Miroslav KrstiÄ
Deep neural network approximation of nonlinear operators, commonly referred to as DeepONet, has proven capable of approximating PDE backstepping designs in which a single Goursat-f…