The sigma-point central difference smooth variable structure filter application into a robotic arm

Abstract

Recent Mobile-robots/Robotic-manipulators based industrial applications require accurate control despite the blurry and the noisy feedback signals. As a result, there is an increasing demand for new estimation techniques and filters to overcome accompanying system disturbances especially when nonlinearity present in the system. Industrial applications control quality will improve if a robust filter is used to reduce the effect of noise and to improve the quality of feedback signals by handling those nonlinearities. In this work, a new filter that combines the Smooth Variable Structure Filter (SVSF) with the Central Difference Kalman Filter (CDKF) is proposed. The presented method results in robust, stable and accurate estimation algorithm for motion states which are measured to be feedback signals. Results are demonstrated by applying the proposed filter to estimate the states of a 4-axis industrial robot arm with one Prismatic, and three Rotational joints (PRRR).