In this article, position and attitude tracking control of the quadrotor subject to complex nonlinearities, input couplings, aerodynamic uncertainties, and external disturbances coupled with faults in multiple motors is investigated. A robustified nonlinear dynamic inversion (NDI)-based fault-tolerant control (FTC) scheme is proposed for the purpose. The proposed scheme is not only robust against aforementioned nonlinearities, disturbances, and uncertainties but also tolerant to unexpected occurrence of faults in multiple motors. The proposed scheme employs uncertainty and disturbance estimator (UDE) technique to robustify the NDI-based controller by providing estimate of the lumped disturbance, thereby enabling rejection of the same. In addition, the UDE also plays the role of fault detection and identification module. The effectiveness and benefits of the proposed design are confirmed through 6-DOF simulations and experimentation on a 3-DOF Hover platform.
BlankeMChristian FreiWKrausF, et al.What is fault-tolerant control?IFAC Proc Vol2000; 33(11): 41–52.
2.
IslamSLiuPXEl SaddikA. Robust control of four–rotor unmanned aerial vehicle with disturbance uncertainty. IEEE Trans Ind Electron2014; 62(3): 1563–1571.
3.
WangXShirinzadehBAngMH. Nonlinear double–integral observer and application to quadrotor aircraft. IEEE Trans Ind Electron2014; 62(2): 1189–1200.
4.
FethallaNSaadMMichalskaH, et al.Robust observer-based dynamic sliding mode controller for a quadrotor UAV. IEEE Access2018; 6: 45846–45859.
5.
MahonyRKumarVCorkeP. Multirotor aerial vehicles: modeling, estimation, and control of quadrotor. IEEE Rob Autom Mag2012; 19(3): 20–32.
6.
GaiWLiuJQuC, et al.Trajectory tracking control for a quadrotor UAV via extended state observer. Syst Sci Control Eng2018; 6(3): 126–135.
7.
ZhengBZhongY. Robust attitude regulation of a 3–DOF helicopter benchmark: theory and experiments. IEEE Trans Ind Electron2010; 58(2): 660–670.
8.
ZhaoBXianBZhangY, et al.Nonlinear robust sliding mode control of a quadrotor unmanned aerial vehicle based on immersion and invariance method. Int J Robust Nonlinear Control2015; 25(18): 3714–3731.
9.
ZhaoBXianBZhangY, et al.Nonlinear robust adaptive tracking control of a quadrotor UAV via immersion and invariance methodology. IEEE Trans Ind Electron2014; 62(5): 2891–2902.
10.
LiuHLuGZhongY. Robust LQR attitude control of a 3–DOF laboratory helicopter for aggressive maneuvers. IEEE Trans Ind Electron2012; 60(10): 4627–4636.
11.
ShaoXLiuJCaoH, et al.Robust dynamic surface trajectory tracking control for a quadrotor UAV via extended state observer. Int J Robust Nonlinear Control2018; 28(7): 2700–2719.
12.
ShiXSunYShaoX. Robust output feedback trajectory tracking for quadrotors. Proc Inst Mech Eng G: J Aerosp Eng2019; 233(5): 1596–1610.
13.
SanzRGarciaPZhongQC, et al.Robust control of quadrotors based on an uncertainty and disturbance estimator. J Dyn Syst Meas Control2016; 138(7): 071006.
14.
LuQRenBParameswaranS, et al.Uncertainty and disturbance estimator–based robust trajectory tracking control for a quadrotor in a global positioning system–denied environment. J Dyn Syst Meas Control2018; 140(3): 031001.
15.
SanzRGarciaPZhongQC, et al.Predictor–based control of a class of time–delay systems and its application to quadrotors. IEEE Trans Ind Electron2016; 64(1): 459–469.
16.
DouJKongXWenB. Altitude and attitude active disturbance rejection controller design of a quadrotor unmanned aerial vehicle. Proc Inst Mech Eng Part G: J Aerosp Eng2017; 231(9): 1732–1745.
17.
BaldiniAFelicettiRFreddiA, et al.Fault–tolerant disturbance observer based control for altitude and attitude tracking of a quadrotor. In: 2018 26th Mediterranean conference on control and automation (MED), Zadar, 19–22 June 2018, 2018, pp. 1–6. IEEE.
18.
ChenFZhangKWangZ, et al.Trajectory tracking of a quadrotor with unknown parameters and its fault-tolerant control via sliding mode fault observer. Proc Inst Mech Eng J Syst Control Eng2015; 229(4): 279–292.
19.
LanzonAFreddiALonghiS. Flight control of a quadrotor vehicle subsequent to a rotor failure. J Guid, Control Dyn2014; 37(2): 580–591.
20.
ChenFJiangRZhangK, et al.Robust backstepping sliding–mode control and observer–based fault estimation for a quadrotor UAV. IEEE Trans Ind Electron2016; 63(8): 5044–5056.
21.
IzadiHAZhangYGordonBW. Fault tolerant model predictive control of quad-rotor helicopters with actuator fault estimation. IFAC Proc Vol2011; 44(1): 6343–6348.
22.
FreddiALanzonALonghiS. A feedback linearization approach to fault tolerance in quadrotor vehicles. IFAC Proc Vol2011; 44(1): 5413–5418.
23.
SongYHeLZhangD, et al.Neuroadaptive fault-tolerant control of quadrotor UAVs: a more affordable solution. IEEE Trans Neural Netw Learn Syst2019; 30(7): 1975–1983.
24.
YanKChenMWuQ. Neural network–based adaptive fault tolerant tracking control for unmanned autonomous helicopters with prescribed performance. Proc Inst Mech Eng Part G: J Aerosp Eng2019; 233(12): 4350–4362.
25.
HanWWangZShenY. Fault estimation for a quadrotor unmanned aerial vehicle by integrating the parity space approach with recursive least squares. Proc Inst Mech Eng Part G: J Aerosp Eng2018; 232(4): 783–796.
26.
Youcef-ToumiKItoO. A time delay controller for systems with unknown dynamics. J Dyn Syst Meas Control1990; 112(1): 133–142.
27.
ZhongQ-CReesD. Control of uncertain LTI systems based on an uncertainty and disturbance estimator. J Dyn Syst Meas Control2004; 126(4): 905–910.
28.
KodhandaAKolheJPZeruT, et al.Robust aircraft control based on UDE theory. Proc Inst Mech Eng Part G: J Aerosp Eng2017; 231(4): 728–742.
PhadkeSBTaloleSE. Sliding mode and inertial delay control based missile guidance. IEEE Trans Aerosp Electron Syst2012; 48(4): 3331–3346.
31.
DhadekarDDPatreBM. UDE-based decoupled full-order sliding mode control for a class of uncertain nonlinear MIMO systems. Nonlinear Dyn2017; 88(1): 263–276.
32.
KodhandaAAliNSucheendranMM, et al.Robust control of nonlinear resonance in a clamped rectangular plate. J Vib Control2018; 24(18): 4176–4194.
33.
TaloleSEPhadkeSB. Model following sliding mode control based on uncertainty and disturbance estimator. ASME J Dyn Sys, Meas Control2008; 130(3): 034501–034505.
34.
KodhandaATaloleSE. Performance analysis of UDE based controllers employing various filters. IFAC-Papers OnLine2016; 49(1): 83–88.
35.
TaloleSEPhadkeSB. Robust input-output linearisation using uncertainty and disturbance estimation. Int J Control2009; 82(10): 1794–1803.
36.
3–DOF Hover User Manual. Canada: Quanser, 2013.
37.
BouadiHMora–CaminoF. Modeling and adaptive flight control for quadrotor trajectory tracking. J Aircr2017; 55(2): 666–681.
38.
GarciaPCLozanoRDzulAE. Modelling and Control of Mini–Flying Machines. Berlin: Springer Science & Business Media, 2006.
MenonPKIragavarapuVROhlmeyerEJ. Nonlinear missile autopilot design using time–scale separation. In: American institute of aeronautics and astronautics guidance, navigation, and control conference, New Orleans, LA, 11–13 August 1997, pp. 1791–1803.
41.
PanchalBSubramanianKTaloleSE. Robust missile autopilot design using two time–scale separation. IEEE Trans Aerosp Electron Syst2018; 54(3): 194–199.
42.
TiwariSNPadhiR. Simultaneous attitude control and trajectory tracking of a micro quadrotor: a snac aided nonlinear dynamic inversion approach. In: 2013 American control conference, Washington DC, USA, 17–19 June 2013, 2013, pp. 1791–1803.
43.
KhalilHK. Nonlinear Systems. Upper Saddle River, NJ: Prentice-Hall, 2002.
