Presents an approach and a systematic design methodology to adaptive motion control
based on neural networks (NNs) for high-performance robot manipulators, for which stability …

Although adaptive control with neural networks has been widely studied for robotic systems,
the classical adaptive laws have been derived by using the gradient algorithm to minimize …

In this paper, the global adaptive neural control with finite-time (FT) convergence learning
performance for a general class of nonlinear robot manipulators has been investigated. The …

In this paper, a neural network (NN)-based methodology is developed for the motion control
of mobile manipulators subject to kinematic constraints. The dynamics of the mobile …

The need to meet demanding control requirements in increasingly complex dynamical
control systems under significant uncertainties makes neural networks very attractive …

In recent years, neural networks have fulfilled the promise of providing model-free learning
controllers for nonlinear systems; however, it is very difficult to guarantee the stability and …

A neural network (NN)-based adaptive controller with an observer is proposed for the
trajectory tracking of robotic manipulators with unknown dynamics nonlinearities. It is …

A robust neural control scheme for mechanical manipulators is presented. The design
basically consists of an adaptive neural controller which implements a feedback …

A robust neural network output feedback scheme is developed for the motion control of robot
manipulators without measuring joint velocities. A neural network observer is presented to …

In the conventional adaptive neural network control of robotic manipulator, the desired
position of robot end effector is specified as a point or trajectory. In addition, it is usually …