Robust Estimation for Nonlinear Continuous-Discrete Systems With Missing Outputs: Application to Automatic Train Control

Abstract

This brief is about robust estimation for a sampled output system with continuous dynamics using unknown input observers (UIOs). A novel worst case estimation error bound is developed using a particular form of Gronwall inequality. The motivation is to assess the accuracy of the estimator in real-world conditions, considering slow sensor sampling times and possible corruption of the measurements due to faults. The theoretical results are applied for robust velocity estimation of a braking train when wheel jamming faults affect odometric sensors. Simulations and experimental data analysis are used to evaluate the proposed approach.