KF-based Adaptive UKF Algorithm and its Application for Rotorcraft UAV Actuator Failure Estimation

Abstract

A new adaptive Unscented Kalman Filter (UKF) algorithm for actuator failure estimation is proposed. A novel filter method with the ability to adapt to the statistical characteristics of noise is presented to improve the estimation accuracy of traditional UKFs. A new algorithm (Kalman Filter (KF) -based adaptive UKF), with the ability to adapt to the statistical characteristic of noise, is proposed to improve the UKF's performance. Such an adaptive mechanism is intended to compensate for the lack of prior knowledge. The asymptotic property of the adaptive UKF is discussed. Actuator Healthy Coefficients (AHCs) are introduced to denote the actuator failure model while the adaptive UKF is employed for the online estimation of both the flight states and the AHCs' parameters of a rotorcraft UAV (RUAV). Simulations are conducted using the model of a ServoHeli-90 RUAV from the 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 this, we also compare this algorithm with the MIT-based one which we proposed in previous research.