Impact of the Order of Redundancy Relation in Robust Fault Detection of Robotic Systems

Abstract

This paper presents a new approach, called robust nonlinear analytic redundancy (RNLAR) technique to actuator fault detection for input-affine nonlinear multivariable dynamic systems that include most robotic systems. Robust fault detection is important because of the universal existence of model uncertainties and process disturbances in most systems. Analytic redundancy, which is a basis for residual generation to detect fault, is primarily used in the linear domain. In this paper we characterized the order of redundancy relation for nonlinear systems in terms of robustness. We propose and prove that increase in the order of redundancy relation increases the robustness in the sense of a performance index defined in this paper. We further develop an algorithm to select the redundancy relation order and design robust nonlinear fault detection residuals. Experimental results on a PUMA 560 robotic arm are presented to verify the claim