Robotic Arm Control Using Neural Network-Based Learning

Abstract

This paper presents an overview of the application of neural network-based learning techniques in the control of robotic arms. Robotic arms, integral to various industries, require precise control mechanisms to perform complex tasks. Traditional control methods often struggle with the nonlinear dynamics and uncertainties inherent in robotic systems. Neural networks, with their adaptive learning capabilities, offer a promising solution to these challenges. This study explores different neural network architectures, including feedforward and recurrent models, and their effectiveness in solving inverse kinematics, trajectory tracking, and real-time control of robotic arms. The paper also discusses the integration of neural networks with other control strategies, such as proportional-integral-derivative (PID) controllers, to enhance performance. Through simulation and experimental results, the paper demonstrates the superiority of neural network-based controllers in terms of accuracy, adaptability, and robustness compared to conventional methods.