This review aims to present recent developments in modeling and control of multiagent
systems. A particular focus is set on crowd dynamics characterized by complex interactions …

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 …

In this letter we propose a new computational method for designing optimal regulators for
high-dimensional nonlinear systems. The proposed approach leverages physics-informed …

The power of DNN has been successfully demonstrated on a wide variety of high-
dimensional problems that cannot be solved by conventional control design methods. These …

Controlling systems of ordinary differential equations is ubiquitous in science and
engineering. For finding an optimal feedback controller, the value function and associated …

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 …

A supervised learning approach for the solution of large-scale nonlinear stabilization
problems is presented. A stabilizing feedback law is trained from a dataset generated from …

Recent research shows that supervised learning can be an effective tool for designing near-
optimal feedback controllers for high-dimensional nonlinear dynamic systems. But the …

A PDE-based accelerated gradient algorithm is proposed to seek optimal feedback controls
of McKean-Vlasov dynamics subject to nonsmooth costs, whose coefficients involve mean …

This work is concerned with solving neural network-based feedback controllers efficiently for
optimal control problems. We first conduct a comparative study of two prevalent approaches …