Improved strong tracking extended Kalman filter for identifying load disturbances and model uncertainties of serial–parallel mechanism

Abstract

Serial–parallel mechanisms have been applied in numerous fields; however, their estimation accuracies are challenged by load disturbances and model uncertainties. To solve this problem, a detailed mathematical model was developed for a serial–parallel mechanism. The model consists of three submodels: a feeding subsystem, an execution subsystem, and a drive subsystem. A novel method based on an improved strong tracking extended Kalman filter (IST-EKF) is proposed to estimate the speeds and positions of the serial–parallel mechanism. In this method, an identifying factor is designed to identify errors caused by load disturbances and model uncertainties. Thereafter, a suboptimal fading scaling factor is proposed to manage errors of different forms. Furthermore, the suboptimal fading scaling factor can keep the symmetry of an error covariance matrix. A numerical simulation and an experiment with a serial–parallel mechanism of a 3PTT-2R NC machine tool were conducted to verify the effectiveness of the proposed IST-EKF.