SYNTHESIS OF NEURAL NETWORKS AND PID CONTROL FOR PERFORMANCE IMPROVEMENT OF INDUSTRIAL ROBOTS

In this article, an approach for improving the performance of industri al robots using multilayer feedforward neural networks is presented. T he 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 improvin g 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 R ID-controlled industrial robots can be improved through proper utiliza tion of the learning and generalization ability of neural networks. Th e experimental results, obtained through actual implementation using a commercial industrial robot, demonstrate the effectiveness of such co ntrol synthesis for practical applications. The results of this work s uggest that neural networks could be added to existing PID-controlled industrial robots for performance improvement.