In this paper, a full-regulated neural network (NN) with a double hidden layer recurrent
neural network (DHLRNN) structure is designed, and an adaptive global sliding-mode …

In this paper, a neural network (NN) controller is designed to suppress the vibration of a
flexible robotic manipulator system with input deadzone. The NN aims to approximate the …

In this paper, a neurodynamics-based output feedback scheme is proposed for distributed
containment maneuvering of marine vessels guided by multiple parameterized paths without …

The broad learning system (BLS) is an emerging approach for effective and efficient
modeling of complex systems. The inputs are transferred and placed in the feature nodes …

For nonlinear dynamical systems, an optimal control problem generally requires solving a
partial differential equation called the Hamilton–Jacobi–Bellman equation, the analytical …

This paper is concerned with the target tracking of underactuated autonomous surface
vehicles with unknown dynamics and limited control torques. The velocity of the target is …

This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks
(RNN) in control design applications. The main families of RNN are considered, namely …

In this study, a probabilistic neural network (PNN) is used to estimate the state of health
(SOH) of Li-ion batteries. The accurate prediction of SOH can help avoid inconveniences or …

The containment maneuvering of marine surface vehicles has two objectives. The first one is
to force the marine vehicles to follow a convex hull spanned by multiple parameterized …

A continuous stirred-tank reactor (CSTR) system is widely applied in wastewater treatment
processes. Its control is a challenging industrial-process-control problem due to great …