Quaternion broad learning system: A novel multi-dimensional filter for estimation and elimination tremor in teleoperation

Abstract

In this paper, a novel quaternion broad learning system is proposed in this paper for tremor estimation and elimination in teleoperation. In the new proposed QBLS, the architecture can be divided into three layers, including quaternion feature layer, enhancement layer and the output layer. In quaternion feature layer, a quaternion-value auto-encoder (QAE) based on the quaternion algebra is proposed and employed to extract the unsupervised features in quaternion domain. Moreover, the enhancement nodes are mapped to improve the system’s regression ability in enhancement layer. In the output layer, the nodes of feature layer and enhancement layer are concatenated to map the output of QBLS. The weight parameters of output layer can be calculated by the minimum norm least squares solutions. In addition, the semi-physical simulation experiment is completed and the new proposed QBLS has been compared with some existing methods. Finally, the effectiveness and efficiency of QBLS are demonstrated by experimental results.