ROBUST FAULT DETECTION AND ISOLATION IN MOBILE ROBOT

Abstract

Robust nonlinear analytical redundancy (RNLAR) technique is used to detect and isolate actuator and sensor faults in a mobile robot. Both model-plant-mismatch (MPM) and process disturbance are considered during fault detection. The RNLAR is used to design primary residual vectors (PRV), which are highly sensitive to the faults and less sensitive to MPM and process disturbance, for sensor and actuator fault detection. The PRVs are transformed into set of structured residual vectors (SRV) for fault isolation. Experimental results on a Pioneer 3-DX are presented to justify the effectiveness of the RNLAR scheme.