Adaptive Neuro Fuzzy Controller for Tracking Control of Robot Manipulators

Industrial robots are playing an increasingly important role in industry to meet out the demands of automated system and are expected to sense environmental information and also process that information and perform appropriate action for a wide variety of tasks. A major challenge for these robots is that the traditional control techniques generally require an accurate model of the system and its environment. Inaccurate modeling will hamper the mathematical optimization process and have a direct negative effect on their performance. For these reasons, computation intelligence technique is now regularly being employed, particularly evolutionary computation, fuzzy logic and neural computation etc.
Fuzzy controllers have been proven to be good tool for real time processes but the designer has to manually derive the “if-then rules by trail and error. On the other hand, Neural networks perform approximation of the system but cannot interpret the solution. Neurofuzzy concept combines the two approaches in which Neural networks brings the learning capabilities and Knowledge representation from fuzzy logic.
This paper considers the application of Neurofuzzy adaptive techniques in the design and development for Industrial robot PUMA 560. In real world situation, the environment around the robot is ever changing one. So this research work aims to develop a best controller design for online control of PUMA 560 robot.
For validating results various experiments are going to be conducted. The outcome from this research work will strengthen the field of robot controller design using Adaptive Neuro Fuzzy interface system.