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Modeling, Identification and Control of Inverse Kinematic of PUMA Robots

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The inverse kinematics solution of robotic manipulator based on Neural Network is presented in this paper. The problem in robotics manipulators is Inverse kinematics for PUMA robotics. The structure of joint for the manipulator is not easy if traditional solutions such as algebraic, geometric, and iterative are inadequate. The proposed method yields precise and multiple solutions and it is suitable and comfortable for real – time applications. The neural network is used to solve the inverse kinematics for the arm of robotics 6-dof. This approach is essential to calculate the end-effector in space Cartesian for each joint. The identification will be done in each joint for PUMA by neural network and PID controller will be applied on each joint to get the response; then, the reference input is done by tuning the values of coefficients of PID.
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Modeling; Nonlinear Analysis; Neural Network Identification; PUMA Robotics; Control

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