Open AccessImplementation of Adaptive Neural Networks Controller for NXT SCARA Robot System
Author(s) -
Abouzaid Wessam M. F.,
Sallam Elsayed. A.
Publication year - 2017
Publication title -
recent innovations in mechatronics
Language(s) - English
Resource type - Journals
ISSN - 2064-9622
DOI - 10.17667/riim.2017.1/3.
Subject(s) - scara , controller (irrigation) , artificial neural network , control engineering , computer science , control theory (sociology) , robot , trajectory , stability (learning theory) , artificial intelligence , engineering , control (management) , machine learning , agronomy , biology , physics , astronomy
Several neural network controllers for robotic manipulators have been developed during the last decades due to their capability to learn the dynamic properties and the improvements in the global stability of the system. In this paper, an adaptive neural controller has been designed with self learning to resolve the problems caused by using a classical controller. A comparison between the improved unsupervised adaptive neural network controller and the P controller for the NXT SCARA robot system is done, and the result shows the improvement of the self learning controller to track the determined trajectory of robotic automated controllers with uncertainties. Implementation and practical results were designed to guarantee online real-time.