Adaptive Extended Kalman Filter designing based on Non-singular Fast Terminal Sliding Mode control for robotic manipulators

Abstract

This paper presents a new method for position estimation of the robotic manipulators, based on the principles of the Extended Kalman Filter (EKF). The standard EKF suffers from performance depreciation and may even diverge from the true estimation in case the statistics of the noises which affect the system were unknown. Hence an Adaptive EKF has been proposed that has better outcome than conventional EKF in terms of robustness, convergence speed and estimation accuracy. Furthermore, the position of each joint is estimated to use in a Non-singular Fast Terminal Sliding Mode (NFTSM) controller. This controller will makes the states to reach in finite time. It also solves the singularity problem of Terminal sliding mode control. Computer simulations given for 2-DOF robot manipulator demonstrate the outperformance of the AEKF in compared with EKF. It has also been shown that the NFTSM controller has the ability to track the trajectory path properly and accurately.