This paper reviews the identification and optimal control problems using recurrent neural
networks and reinforcement learning for nonlinear systems both in discrete-and continuous …

In recent years, neural networks have become a common practice in academia for handling
complex problems. Numerous studies have indicated that complex problems can generally …

Nonlinear activation functions play an important role in zeroing neural network (ZNN), and it
has be proved that ZNN can achieve finite-time convergence when the sign-bi-power (SBP) …

For the existing repetitive motion generation (RMG) schemes for kinematic control of
redundant manipulators, the position error always exists and fluctuates. This article gives an …

It has been reported that some specially designed recurrent neural networks and their
related neural dynamics are efficient for solving quadratic programming (QP) problems in …

Collision between dual robot manipulators during working process will lead to task failure
and even robot damage. To avoid mutual collision of dual robot manipulators while doing …

In order to leverage the unique advantages of redundant manipulators, avoiding the
singularity during motion planning and control should be considered as a fundamental issue …

A nonconvex distributed optimization problem involving nonconvex objective functions and
inequality constraints within an undirected multi-agent network is considered. Each agent …

Zeroing neural network (ZNN) has a wide application in various fields, which is a very
important and novel type of recurrent neural network (RNN). To deepen and expand the …

Many methods are known to solve the problem of real-valued and static quadratic
programming (QP) effectively. However, few of them are still useful to solve the time-varying …