A learning based method for obtaining feedback laws for nonlinear optimal control problems
is proposed. The learning problem is posed such that the open loop value function is its …

A gradient-enhanced functional tensor train cross approximation method for the resolution of
the Hamilton–Jacobi–Bellman (HJB) equations associated with optimal feedback control of …

In this paper we develop an algebraic framework for analyzing neural network
approximation of compositional functions, a rich class of functions that are frequently …

Solving high-dimensional optimal control problems and corresponding Hamilton–Jacobi
PDEs are important but challenging problems in control engineering. In this paper, we …

Numerical methods for the optimal feedback control of high-dimensional dynamical systems
typically suffer from the curse of dimensionality. In the current presentation, we devise a …

The synthesis of control laws for interacting agent-based dynamics and their mean-field limit
is studied. A linearization-based approach is used for the computation of suboptimal …

The synthesis of suboptimal feedback laws for controlling nonlinear dynamics arising from
semi-discretized PDEs is studied. An approach based on the State-dependent Riccati …

In this paper, we investigate the ability of neural networks to mitigate the curse of
dimensionality in representing control Lyapunov functions. To achieve this, we first prove an …

A learning technique for finite horizon optimal control problems and its approximation based
on polynomials is analyzed. It allows to circumvent, in part, the curse dimensionality which is …

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 by …