Actuator Faults Estimation for a Helicopter UAV in the Presence of Disturbances
- PDF / 2,756,148 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 93 Downloads / 146 Views
Actuator Faults Estimation for a Helicopter UAV in the Presence of Disturbances Alireza Faraji1 · Zahra Nejati1 · Mostafa Abedi2 Received: 10 August 2019 / Revised: 9 May 2020 / Accepted: 8 June 2020 © Brazilian Society for Automatics--SBA 2020
Abstract The aim of this paper is to develop robust three-stage extended Kalman filter for a model based on a fault detection and identification for nonlinear hover mode system of helicopter unmanned aerial vehicle. In addition, we show that, in considered systems, the actuator faults are affected by each other motivated in five scenarios simulation results. More precisely, the proposed approach estimates and decouples actuator faults in the presence of external disturbances in nonlinear mathematical model. Moreover, we analyze and identify various faults such as bias fault and also catastrophic faults such as stuck and floating faults. Finally, the simulation results show effectiveness of the proposed robust method for detection and isolation of various actuator faults and differentiating bias and stuck faults. Keywords Helicopter unmanned aerial vehicle · Fault detection and identification · Three-stage extended Kalman filter · Actuator faults · Fault decoupling
1 Introduction In recent decades, unmanned aerial vehicles (UAVs) have become an important research topic in the academic and military communities worldwide (Hussain and Malik 2019). Among various UAVs, small-scale unmanned helicopters are an ideal platform for academic research (Cai et al. 2011a, b). The abilities of helicopter UAV (HUAV) to take off and landing vertically while also performing hover flight and various flight maneuvers have made them proper vehicles for a wide range of applications (Mettler et al. 2002). HUAVs are categorized in different weights and sizes, and used for various military and civilian purposes, such as tacking photos, rescue actions, inspection of oil and gas pipelines, and so on (Cai et al. 2011a, b). * Alireza Faraji [email protected] Zahra Nejati [email protected] Mostafa Abedi [email protected] 1
Electrical and Computer Engineering Department, University of Kashan, Kashan, Iran
Electrical Engineering Department, University of Shahid Beheshti, Tehran, Iran
2
To provide safe flight of helicopters, fault detection is necessary for preventing failures (Zhang and Jiang 2008). In HUAVs or other flying vehicles, faults may occur in actuators, sensors or structure (Marzat et al. 2012). The possibility of actuator fault is also more than sensor faults for mechanical reasons. (Liu et al. 2016; Avram et al. 2017). FDI is an important field in the modern control theory (Costa et al. 2014; Bia and Gao 2019; Gongora et al. 2019; Ghasemi et al. 2018; Iqbal et al. 2019). It plays an important role in reliability of UAVs (Guo et al. 2017; Ashokkumar and York 2017). Several methods have so far been proposed about it. Unknown input observers (UIOs) are used to separate faults from external disturbances (Cristofaro and Johansen 2014; Chaves et al. 2019). UIO has been used
Data Loading...
