A Novel Approach to Fault Detection and Identification in Suction Foot Control of a Climbing Robot

Abstract

This paper presents a multiple-model and Boolean logic reasoning (MMBLR) approach to detect and identify faults in the suction foot control of a climbing robot. For this control system, some fault models are easily given by kinematics equations. Moreover, the logic relations of the system states have been known in advance. Based on the combination of the multiple-model adaptive estimation (MMAE) algorithm and the Boolean logic reasoning, the MMBLR approach is properly fit for the fault detection and identification (FDI) application to the climbing robot. In the MMBLR architecture, the MMAE algorithm is used to reliably detect and identify the model-known faults. Then based on the robot's states and the results of the MMAE, other faults are detected and identified using the Boolean logic reasoning. Experimental results validated that the faults of the sensors and actuators in the suction foot control of the robot can be readily detected and identified by the MMBLR approach.