Transmissibility-based Fault Detection in Systems with Unknown Time-Varying Parameters

Abstract

This paper investigates transmissibility operators for time-variant systems with bounded nonlinearities. Transmissibility operators are mathematical relations between a set of system responses to another set of responses within the same system. Both parameter variation and system nonlinearities are considered to be unknown. Transmissibility operators are shown in the literature to be independent of the system inputs. The bounded nonlinearities are considered as independent excitations on the system, which renders transmissibilities independent of these nonlinearities. To overcome the unknown parameter variation, we propose identifying transmissibilities using recursive least-squares in the form of noncausal FIR models. The recursive least squares algorithm is used to optimize what dynamics to include in the transmissibility operator, and what dynamics to exclude with the system nonlinearities. The identified transmissibilities then become robust against parameter variation and unknown nonlinearities. Next, transmissibilities are proposed for fault detection an autonomous multi-robotic system formulated to emulate connected autonomous vehicle platoons. The variant parameters are the robot mass and the ground friction coefficient.