Int. J. Com. Dig. Sys. 2, No. 3, 109-121 (2013) 109 A Practical Neuro-fuzzy Mapping and Control for a 2 DOF Robotic Arm System Ebrahim Mattar Dept. of Electrical and Electronics Engineering, College of Engineering, University of Bahrain, P.O. Box 32038, Kingdom of Bahrain. E-mail addresses: ebmattar@theiet.org, ebmattar@ieee.org Received 23 Sep. 2012, Revised 9 Feb. 2013, Accepted 15 Mar. 2013, Published 1 Sep. 2013 Abstract: Relating an arm Cartesian space to joint space and arm dynamics, is an essential issue in arm control that has been given a substantial attention by number of researches. Arm inverse kinematic, is a nonlinear relation, and a closed form solution is not a straight forward, or does not even always exist. This research is presenting a practical use of Neuro-Fuzzy system to solve inverse kinematics problem that used for a two links robotic arm. The concept here is to learn kinematics relations for a robotic arm system. This is to learn and map its environment and remembers what it learnt. For learning the inverse kinematics, Neuro-fuzzy needs information about coordinates, joint angles and actuator position. Information flow needed for the training for a Neuro-fuzzy network is slow and difficult to get by measuring the real structure. Desired Cartesian coordinates are given as input to a Neuro-fuzzy that returns actuator positions. Hence to express them as linguistics fuzzy rules. Neuro-fuzzy system is to generalize and produce an appropriate output. The assembled system has been equipped with C ++ interface routines, as being executed from a MATLAB environment, in addition to high-speed low-level communication with the robotic arm sensing devices. Keywords: UOB Robotic Arm; Inverse Dynamics; Computed Torque Law; Neuro-fuzzy mapping. I. INTRODUCTION Robotics arms are widely used and employed for industrial and non-industrial applications. However, for more precise and accurate motion control, dynamic model do play important role for such applications. It is always not an easy task to get the forward and the inverse models for robotics structure, specifically, once redundancy exists. Kinematics models are always nonlinear relations, and closed form solutions are not easy tasks to be achieved. There are a number of approaches that have been reported in literature regarding building kinematics models. In its boarder sense, manipulator kinematics is the study of motion without regard to the forces which cause it. Within kinematics, it is possible to study position, velocity and acceleration, and all higher order derivatives of an arm position variables. The kinematics of manipulators involves the study of the geometric and time based properties of the motion, and in particular how the various links move with respect to one another and with time. Typical robots are serial-link manipulators comprising a set of bodies, (links), in a chain, connected by joints. Each joint has a single Degree of Freedom (DOF), either translational or rotational. For a manipulator with (n joints numbered from 1 to n, there are (n+1) links, numbered from 0 to (n). Link 0 is the base of the manipulator, generally fixed, and link n carries the end-effector. Joint ( i) connects links (i) and i first and last links are meaningless, but are arbitrarily chosen to be 0. Joints may be described by two parameters. The link ( o) set is the distance from one link to the next along the axis of the joint. The joint angle is the rotation of one link with respect to the next about the joint axis. To facilitate describing the location of each link we affix a coordinate frame to it, frame (i) is attached to link (i). Denavit and Hartenberg [1] proposed a matrix method of systematically assigning coordinate systems to each link of an articulated chain. Axis of revolute joint (i) is aligned with (Z i ). Parallel link and serial/parallel hybrid structures are possible, though much less common in industrial manipulators. International Journal of Computing and Digital Systems http://dx.doi.org/10.12785/ijcds/020302 © 2013 UOB SPC, University of Bahrain