Nonlinear Model Identification and Observer Design for Thrust Estimation of Small-scale Turbojet Engines

TL;DR

This paper develops a nonlinear model and observer for small-scale turbojet engines to accurately estimate thrust online, crucial for VTOL drone stability, using limited instrumentation and a grey-box approach.

Contribution

It introduces a grey-box nonlinear state-space model and an extended Kalman filter for thrust estimation from angular speed data, even during engine failures.

Findings

Thrust estimation error within 2% of rated peak thrust.

Effective online thrust estimation using limited sensor data.

Robustness of the method during engine failures.

Abstract

Jet-powered vertical takeoff and landing (VTOL) drones require precise thrust estimation to ensure adequate stability margins and robust maneuvering. Small-scale turbojets have become good candidates for powering heavy aerial drones. However, due to limited instrumentation available in these turbojets, estimating the precise thrust using classical techniques is not straightforward. In this paper, we present a methodology to accurately estimate the online thrust for the small-scale turbojets used on the iRonCub - an aerial humanoid robot. We use a grey-box method to capture the turbojet system dynamics with a nonlinear state-space model based on the data acquired from a custom engine test bench. This model is then used to design an extended Kalman filter that estimates the turbojet thrust only from the angular speed measurements. We exploited the parameter estimation algorithm to ensure that the EKF gives smooth and accurate estimates even at engine failures. The designed EKF was validated on the test bench where the mean absolute error in estimated thrust was found to be within 2% of rated peak thrust.