LAUSR

This article presents a sensor fusion method to estimate the three-degrees-of-freedom (DOFs) orientation of a ball-joint-like permanent magnet spherical motor (PMSM) and its angular velocity using embedded sensors that simultaneously measure the existing magnetic flux density (MFD) field and the back electromotive force (back EMF), which serve as inputs to a Kalman filter (KF)-based sensor fusion system for full-state estimation of three-DOF angular displacement and velocity in real time. Formulated in quaternion representation, the effectiveness and accuracy of the sensor fusion system consisting of an artificial neural network (ANN) that determines the three-DOF orientation from measured MFD and an EMF-velocity model have been experimentally evaluated on an additive manufactured prototype PMSM with a weight compensating regulator (WCR) by comparing the estimated orientation and angular velocity with that measured by two most used methods, optical laser-beam system, and inertial measurement unit (IMU). The experimental findings demonstrate that the KF-based sensor fusion effectively overcomes the MFD sensor noise and IMU drift problems and is capable of simultaneous measurements of three-DOF angular displacement and velocity with improved accuracy relative to the popular IMU measurements.