Adaptive Neuro-Fuzzy based Forward Kinematics Analysis of a three DoF Delta Robot

It is always very challenging to obtain a unique solution for the forward kinematics of parallel robots due to nonlinear and coupled equations. Soft computing is one of the most widely used methods in identifying systems. In the present paper, some different supervised clustering methods are applied to obtain a model for the forward kinematics of the 3-DoF delta parallel robot. In the proposed process, a hybrid of fuzzy logic and neural networks is used for modeling. In this paper, the Subtractive Clustering Method (SCM), Fuzzy C-means (FCM), and Grid Partitioning (GP) techniques are described, and the simulation results obtained from the explained approaches are examined in detail. Finally, it concluded that the (GP) method for this application provides better performance than (FCM) and (SCM) techniques.