A practical neuro-fuzzy mapping and control for a 2 DOF robotic arm system

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.