The weak spots have been examined, a solution has been suggested, the solution has been
applied, and a comparison between the simulation and experimental test results has been …

The rapid global spread of COVID-19 poses a huge threat to human security. Accurate and
rapid diagnosis is essential to contain COVID-19, and an artificial intelligence-based …

This paper presents a novel approach to learning free terminal time closed-loop control for
robotic manipulation tasks, enabling dynamic adjustment of task duration and control inputs …

This paper considers the sliding mode control issue for the complex neural network coupled
with multi-interconnected neurons. Firstly, the stability of the neural network is realized by …

As demonstrated in many areas of real-life applications, neural networks have the capability
of dealing with high dimensional data. In the fields of optimal control and dynamical systems …

Optimal control problem is typically solved by first finding the value function through the
Hamilton–Jacobi equation (HJE) and then taking the minimizer of the Hamiltonian to obtain …

Feedback control synthesis for nonlinear, parameter-dependent fluid flow control problems
is considered. The optimal feedback law requires the solution of the Hamilton-Jacobi …

We propose Nash Neural Networks ($ N^ 3$) as a new type of Physics Informed Neural
Network that is able to infer the underlying utility from observations of how rational …

Designing optimal feedback controllers for nonlinear dynamical systems requires solving
Hamilton-Jacobi-Bellman equations, which are notoriously difficult when the state dimension …

In this work the synthesis of approximate optimal and smooth feedback laws for infinite
horizon optimal control problems is addressed. In this regards, $ L^{p} $ type error bounds of …