Inverse Kinematic of 1-DOF Robot Manipulator Using Sparse Identification of Nonlinear System

Abstract

Robot Manipulator is the most robot used in industry since it can act like a human arm that can move objects. Research on robot manipulator has been widely carried out in various problems such as control systems, intelligence robots, degrees of freedom, mechanics-electronics systems and various other problems. In control systems there are studies to design of robot motion through kinematics. However, modeling the kinematic motion which has nonlinear characteristics will be more difficult if the number of degrees of freedom increases. To overcome this problem, this research will proposed sparse regression to modeling the kinematics of a robotic arm with the black box principle modeling. The results obtained indicate that the method The proposed one has the ability to identify robots manipulator with a fitness score of up to 100%. This matter shows that the proposed method can modeling the kinematic inverse of the manipulator robot without through complex calculations. From this research is expected can provide other research opportunities related to identification kinematics with the identification system method