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 large number of degrees of freedom of the system and uncertainties in the propeller model. For instance, a covariance scheduling 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.

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

Mohamed, Hosameldin Awadalla Omer;Bergonti, Fabio;Braghin, Francesco;
2021-01-01

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 large number of degrees of freedom of the system and uncertainties in the propeller model. For instance, a covariance scheduling 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.
2021
Estimation , Robot sensing systems , Humanoid robots , Propellers , Kalman filters , aerospace robotics , humanoid robots , nonlinear filters , robot dynamics , Aerial systems: perception and autonomy
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1207013
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 13
  • ???jsp.display-item.citation.isi??? 7
social impact