In recent years, a large number of manipulator robots have been deployed to replace or
assist humans in many repetitive and dangerous tasks. Yet, these robots have complex …

Recently, robotic manipulators have been playing an increasingly critical part in scientific
research and industrial applications. However, modeling of robotic manipulators is …

In this article, we present a metaheuristic-based control framework, called beetle antennae
olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance …

Robot manipulators are playing increasingly significant roles in scientific researches and
engineering applications in recent years. Using manipulators to save labors and increase …

Extreme learning machine (ELM) is a new learning algorithm for the single hidden layer
feedforward neural networks. Compared with the conventional neural network learning …

This paper considers cooperative kinematic control of multiple manipulators using
distributed recurrent neural networks and provides a tractable way to extend existing results …

In this study, a novel metaheuristic algorithm, namely, carnivorous plant algorithm (CPA),
inspired by how the carnivorous plants adapting to survive in the harsh environment, was …

The problem of dynamic task allocation in a distributed network of redundant robot
manipulators for pathtracking with limited communications is investigated in this paper …

In this paper, a neural-dynamic distributed scheme is proposed for the cooperative control of
multiple redundant manipulators with limited communications. It is guaranteed that, with the …

Using neural networks to handle intractability problems and solve complex computation
equations is becoming common practices in academia and industry. It has been shown that …