5 citations · 17 across the 15 of their papers we have counts for
6 papers · 1 filter
Separable Approximations of Optimal Value Functions and Their Representation by Neural Networks
Mario Sperl, Luca Saluzzi, Dante Kalise +1
The use of separable approximations is proposed to mitigate the curse of dimensionality related to the approximation of high-dimensional value functions in optimal control. The sep…
State Dependent Riccati for dynamic boundary control to optimize irrigation in Richards' Equation framework
Alessandro Alla, Marco Berardi, Luca Saluzzi
We present an approach for the optimization of irrigation in a Richards' equation framework. We introduce a proper cost functional, aimed at minimizing the amount of water provided…
Statistical Proper Orthogonal Decomposition for model reduction in feedback control
Sergey Dolgov, Dante Kalise, Luca Saluzzi
Feedback control synthesis for nonlinear, parameter-dependent fluid flow control problems is considered. The optimal feedback law requires the solution of the Hamilton-Jacobi-Bellm…
Separable approximations of optimal value functions under a decaying sensitivity assumption
Mario Sperl, Luca Saluzzi, Lars Grüne +1
An efficient approach for the construction of separable approximations of optimal value functions from interconnected optimal control problems is presented. The approach is based o…
Approximation of Optimal Control Problems for the Navier-Stokes equation via multilinear HJB-POD
Maurizio Falcone, Gerhard Kirsten, Luca Saluzzi
We consider the approximation of some optimal control problems for the Navier-Stokes equation via a Dynamic Programming approach. These control problems arise in many industrial ap…
Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control
Sergey Dolgov, Dante Kalise, Luca Saluzzi
An optimized variant of the State Dependent Riccati Equations (SDREs) approach for nonlinear optimal feedback stabilization is presented. The proposed method is based on the constr…