Momentum-Based Extended Kalman Filter for Thrust Estimation on Flying Multibody Robots

Abstract

Effective control design of flying vehicles requires a reliable estimation of the propellers’ thrust forces to secure a successful flight. Direct measurements of thrust forces, however, are seldom available in practice and on-line thrust estimation usually follows from the application of fusion algorithms that process on-board sensor data. This letter proposes a framework for the estimation of the thrust intensities on flying multibody systems that are not equipped with sensors for direct thrust measurement. The key ingredient of the proposed framework is the so-called centroidal momentum of a multibody system, which combined with the propeller model. It enables the design of Extended Kalman Filters (EKF) for on-line thrust estimation. The presented approach tackles the additional complexity in thrust estimation due to the possibly <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">large</i> number of degrees of freedom of the system and uncertainties in the propeller model. For instance, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">covariance scheduling</i> approach based on the turbines RPM error is proposed to ensure a reliable estimation even in case of turbine failures. Simulations are presented to validate the proposed algorithm during robot flight. Moreover, an experimental setup is designed to evaluate the accuracy of the estimation algorithm using iRonCub, a jet-powered humanoid robot, while standing on the ground.