Artificial neural networks, or neural networks, represent a technology that is rooted in many
disciplines like mathematics, physics, statistics, computer science and engineering. Neural …

Since neural networks have universal approximation capabilities, therefore it is possible to
postulate them as solutions for given differential equations that define unsupervised errors …

This paper is an effort towards develo** an online learning algorithm to find the optimal
control solution for continuous-time (CT) systems subject to input constraints. The proposed …

Differential equations (DEs) are widely employed in the mathematical modelling of a wide
range of scientific and engineering problems. The analytical solution of these DEs is …

In this paper, block neural network (BNN) method is proposed to solve several kinds of
differential equations. BNN is used to construct approximating functions and its derivatives …

The design of an artificial neural network (ANN) based sub-optimal controller to solve the
finite-horizon optimization problem for a class of systems with uncertainties is the main …

It is generally impossible to analytically solve the Hamilton‐Jacobi‐Bellman (HJB) equation
of an optimal control system. With the coming of the big‐data era, this paper first derives a …

In this paper, we have used a neural network to obtain an approximate solution to the value
function of the HJB (Hamilton–Jacobi–Bellman) equation. Then, we have used it to stabilize …

In this paper, the stability of a class of nonlinear control systems is analyzed. We first
construct an optimal control problem by inserting a suitable performance index; this problem …

This study solves a finite horizon optimal problem for linear systems with parametric
uncertainties and bounded perturbations. The control solution considers the uncertain part …