Study on Neural Immune PD Type Tracking Control for DC Actuating Mechanism

TL;DR

This paper introduces a neural immune PD control method that combines artificial immune systems with neural networks to improve trajectory tracking accuracy and speed in DC actuating mechanisms.

Contribution

It proposes a novel neural immune PD control law that integrates immune system concepts with neural networks for enhanced control performance.

Findings

Faster and more accurate trajectory tracking compared to previous methods

Effective approximation of unknown nonlinear functions using neural networks

Validated through simulation results

Abstract

Artificial Immune Systems(AIS) have been widely used in different fields, such as control, robotics, computer science and multi-agent systems. In this paper is proposed a new approach of neural immune PD type tracking control combining artificial immune control with neural network. It is assumed that the output of the helper T-cell is concerned with not only the error of system but also its changing rate, while the output of suppressor T-cell is unknown nonlinear function with respect to the amount and changing rate of antigens and the changing rate of antibodies, which is approximated by the output of neural network. From this, we derive neural immune PD type control law and apply it to the trajectory tracking of DC actuating mechanism. The validity of the proposed method is verified by simulation and the simulation results show that this method can follow the desired trajectory more rapidly and more accurately compared to the previous method.