Rotorcraft UAV actuator failure estimation with KF-based adaptive UKF algorithm

Abstract

A new adaptive Unscented Kalman Filter (UKF) algorithm for actuator failure estimation is proposed. The novel filter method with adaptability to statistical characteristic of noise is presented to improve the estimation accuracy of traditional UKF. The algorithm with the adaptability to statistical characteristic of noise, named Kalman Filter (KF) -based adaptive UKF, is proposed to improve the UKF performance. Such an adaptive mechanism is intended to compensate the lack of a prior knowledge. The asymptotic property of the adaptive UKF is discussed. The Actuator Healthy Coefficients (AHCs) is introduced to denote the actuator failure model while the adaptive UKF is employed for on-line estimation of both the flight states and the AHCs parameters of rotorcraft UAV (RUAV). Simulations are conducted using the model of SIA- Heli-90 RUAV of Shenyang Institute of Automation, CAS. The results are compared with those obtained by normal UKF to demonstrate the effectiveness and improvements of the adaptive UKF algorithm. Besides, we also compare this algorithm with the MIT-based one which we propose in previous research.