Abstract Machine learning (ML) is a branch of artificial intelligence centered on algorithms
which do not need explicit prior programming to function but automatically learn from …

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

Reinforcement learning (RL) is an efficient learning approach to solving control problems for
a robot by interacting with the environment to acquire the optimal control policy. However …

In order to prevent joints from being damaged by impact force in a space robot capturing
satellite, a spring-damper device (SDD) is added between the joint motor and manipulator …

Simple Summary Lung cancer therapeutics have dramatically improved in recent years.
Indeed, precision oncology could be exemplified by non-small cell lung cancer (NSCLC) …

The control objective of the vertical plane underactuated manipulator (VPUM) is generally to
swing up the end point (EP) from the vertical down equilibrium point (VDEP) to the vertical …

Abstract Machine Learning (ML) represents a computer science capable of generating
predictive models, by exposure to raw, training data, without being rigidly programmed. Over …

The robot control technology combined with a machine vision system provides a feasible
method for the autonomous operation of moving target. However, designing an effective …

This paper proposes a non-singular fast terminal sliding mode control strategy based on the
self-organizing radial basis function neural network (RBFNN) approximation for the train key …

Flexible-joint manipulators are governed by complex nonlinear dynamics, defining a
challenging control problem. In this work, we propose an approach to learn an outer-loop …