Dynamics Learning-Based Fault Isolation for A Soft Trunk Robot

摘要

In this paper, we investigate the fault isolation (FI) problem of a soft trunk robot and propose a dynamics learning-based FI approach which is generic and applicable to general types of faults. Specifically, an adaptive radial basis function neural network (RBF NN) based dynamics learning scheme is first developed to achieve accurate identification of the robot’s dominant dynamics under different faulty modes, and the learned knowledge is stored and represented by constant RBF NN models. The learned results are then merged by using a novel merging mechanism to construct a bank of global RBF NN models, for capturing the characteristics of the robot’s dynamics under each specific faulty mode. Based on these models, a bank of FI observers are designed to develop an important capability of accurately reconstructing the robot’s dynamics under various faulty modes. The FI scheme is developed using these FI observers, which monitors the robot’s operation status online to provide accurate isolation of faults occurring in the robot. Physical experiments are performed on the soft trunk robot to validate the effectiveness of our proposed approaches.