International Journal of Advances in Engineering & Technology, Nov 2011. ©IJAET ISSN: 2231-1963 158 Vol. 1, Issue 5, pp. 158-169 INTELLIGENT INVERSE KINEMATIC CONTROL OF SCORBOT-ER V PLUS ROBOT MANIPULATOR Himanshu Chaudhary and Rajendra Prasad Department of Electrical Engineering, IIT Roorkee, India ABSTRACT In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) method based on the Artificial Neural Network (ANN) is applied to design an Inverse Kinematic based controller forthe inverse kinematical control of SCORBOT-ER V Plus. The proposed ANFIS controller combines the advantages of a fuzzy controller as well as the quick response and adaptability nature of an Artificial Neural Network (ANN). The ANFIS structures were trained using the generated database by the fuzzy controller of the SCORBOT-ER V Plus.The performance of the proposed system has been compared with the experimental setup prepared with SCORBOT-ER V Plus robot manipulator. Computer Simulation is conducted to demonstrate accuracyof the proposed controller to generate an appropriate joint angle for reaching desired Cartesian state, without any error. The entire system has been modeled using MATLAB 2011. KEYWORDS: DOF, BPN, ANFIS, ANN, RBF, BP I. INTRODUCTION Inverse kinematic solution plays an important role in modelling of robotic arm. As DOF (Degree of Freedom) of robot is increased it becomes a difficult task to find the solution through inverse kinematics.Three traditional method used for calculating inverse kinematics of any robot manipulator are:geometric[1][2] , algebraic[3][4][5] and iterative [6] methods. While algebraic methods cannot guarantee closed form solutions. Geometric methods must have closed form solutions for the first three joints of the manipulator geometrically. The iterative methods converge only to a single solution and this solution depends on the starting point. The architecture and learning procedure underlying ANFIS, which is a fuzzy inference system implemented in the framework of adaptive networks was presented in [7]. By using a hybrid learning procedure, the proposed ANFIS was ableto construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. Neuro-Genetic approach for the inverse kinematics problem solution of robotic manipulators was proposed in [8]. A multilayer feed-forward networks was applied to inverse kinematic problem of a 3- degrees-of freedom (DOF) spatial manipulator robot in [9]to get algorithmic solution. To solve the inverse kinematics problem for three different cases of a 3-degrees-of freedom (DOF) manipulator in 3D space,a solution was proposed in [10]usingfeed-forward neural networks.This introduces the fault-tolerant and high-speed advantages of neural networks to the inverse kinematics problem. A three-layer partially recurrent neural network was proposed by [11]for trajectory planning and to solve the inverse kinematics as well as the inverse dynamics problems in a single processing stage for the PUMA 560 manipulator. Hierarchical control technique was proposed in[12]for controlling a robotic manipulator.It was based on the establishment of a non-linear mapping between Cartesian and joint coordinates using fuzzy logic in order to direct each individual joint. Commercial Microbot with three degrees of freedom was utilized to evaluate this methodology. Structured neural networks based solution was suggested in[13] that could be trained quickly. The proposed method yields multiple and precise solutions and it was suitable for real-time applications.