Experimental application of extended Kalman filtering for sensor validation

Abstract

A sensor failure detection and identification scheme for a closed loop nonlinear system is described. Detection and identification tasks are performed by estimating parameters directly related to potential failures. An extended Kalman filter is used to estimate the fault-related parameters, while a decision algorithm based on threshold logic processes the parameter estimates to detect possible failures. For a realistic evaluation of its performance, the detection scheme has been implemented on an inverted pendulum controlled by real-time control software. The failure detection and identification scheme is tested by applying different types of failures on the sensors of the inverted pendulum. Experimental results are presented to validate the effectiveness of the approach.