Synthesis of Neural Networks and PID Control for Performance Improvement of Industrial Robots

Abstract

In this article, an approach for improving the performance of industrial robots using multilayer feedforward neural networks is presented. The controller based on this approach consists of two main components: a PID control and a neural network. The function of the neural network is to complement the PID control for the specific purpose of improving the performance of the system over time. Analytical and experimental results concerning this synthesis of neural networks and PID control are presented. The analytical results assert that the performance of PID-controlled industrial robots can be improved through proper utilization of the learning and generalization ability of neural networks. The experimental results, obtained through actual implementation using a commercial industrial robot, demonstrate the effectiveness of such control synthesis for practical applications. The results of this work suggest that neural networks could be added to existing PID-controlled industrial robots for performance improvement.