Model with Neural Network Component for Adaptive Manipulator Control under Variable Load

Abstract

The method of adaptive control of a manipulator under variable load using a neural-adaptive PID controller, which combines the classical principles of the theory of automatic control with intelligent self -learning algorithms, is considered. The purpose of this study is to develop a mathematical model of the manipulation system and implement a method for adaptive compensation of dynamic disturbances resulting from changes in the mass of the working tool or load. The object of analysis is the nonlinear system "manipulator – regressor-compensation –adaptive PID", for which the dynamics equation, adaptation laws are constructed, and numerical simulation of control processes is carried out.