Design of a neural control applied to a high-precision robotic arm

Abstract

The main objective of this work is to present a neural control proposal based on a reference tracking model applied to a high-precision robotic arm, where it will begin by explaining and deducing the mathematical model of the robotic arm which will be represented by a space model, where the input variable will be the mechanical torque and the output variable will be the angle and position control of the robot at each instant of time, what is sought through neural control is through the different changes in the variables input and output find and calibrate a control system that guarantees reference tracking, that is, given a desired path or position, we want the robotic arm to accurately follow the desired position or path with pressure. The reference tracking control system is based on artificial intelligence method of machine learning especially in the field of intelligent neural networks.