In this article, an active fault-tolerant control system is designed and analyzed for a new model structure of unmanned aerial vehicles, which is referred to a swash mass helicopter. It consists of a double-blade coaxial shaft rotor for generating the necessary thrust and four masses that are located on the main body structure to maneuver the helicopter. The translational and rotational subsystems of the helicopter are highly coupled. Therefore, change of coordinate and feedback linearization methodologies are applied to address the problem and achieve the canonical form of the model. Next, actuator fault as a loss of effectiveness and bias faults are modeled for the dynamic. The main contribution of this article is to design an active fault-tolerant control system based on back-stepping and T-S fuzzy estimation methods for the swash mass helicopter which has never been designed in the last decades. T-S fuzzy methodology is responsible for obtaining the estimated value of the actuator faults during the flight. The Lyapunov theory illustrates that the proposed control strategy can stabilize the system despite the actuator’s fault. The simulation results show the effectiveness of the proposed scheme compared with another method.
PapaU. Introduction to Unmanned Aircraft Systems (UAS). In: PapaU (ed.) Embedded platforms for UAS landing path and obstacle detection 2018. Cham: Springer, pp.1–11.
2.
AntonelliGCataldiEArrichielloF, et al. Adaptive trajectory tracking for quadrotor MAVs in presence of parameter uncertainties and external disturbances. IEEE T Contr Syst T2017; 26(1): 248–254.
3.
AlmakhlesDJ.Robust back-stepping sliding mode control for a quadrotor trajectory tracking application. IEEE Access2019; 8: 5515–5525.
4.
RajJRaghuwaiyaKSVanualailaiJ.Novel Lyapunov-based autonomous controllers for quadrotors. IEEE Access2020; 8: 47393–47406.
5.
AllahverdyDFakharianAMenhajMB. Back-stepping integral sliding mode control with iterative learning control algorithm for quadrotor UAVs. J Electr Eng Technol2019; 14(6): 2539–2547.
6.
TianBMaYZongQ. A continuous finite-time output feedback control scheme and its application in quadrotor UAVs. IEEE Access2018; 6: 19807–19813.
7.
RyllMBülthoffHHGiordanoPR. A novel overactuated quadrotor unmanned aerial vehicle: modeling, control, and experimental validation. IEEE T Contr Syst T2014; 23(2): 540–556.
8.
TonelloAMSalamatB. A swash mass unmanned aerial vehicle: design, modeling and control. arXiv preprint arXiv:1909.06154,13 September2019.
9.
KeenerJPSneydJ. Mathematical physiology. New York: Springer, 1998.
10.
AngelovPPFilevDP. An approach to online identification of Takagi-Sugeno fuzzy models. IEEE T Syst Man Cy B2004; 34(1): 484–498.
11.
LamHKSeneviratneLD. Stability analysis of interval type-2 fuzzy-model-based control systems. IEEE T Syst Man Cy B2008; 38(3): 617–628.
12.
ZhuZCaoS. Back-stepping sliding mode control method for quadrotor UAV with actuator failure. J Eng2019; 22: 8374–8377.
13.
ZhengFZhenZGongH. Observer-based back-stepping longitudinal control for carrier-based UAV with actuator faults. J Syst Eng Electron2017; 28(2): 322–377.
14.
ZeghlacheSDjeriouiABenyettouL, et al. Fault tolerant control for modified quadrotor via adaptive type-2 fuzzy back-stepping subject to actuator faults. ISA Trans2019; 95: 330–345.
15.
BarikbinBFakharianA. Trajectory tracking for quadrotor UAV transporting cable-suspended payload in wind presence. T I Meas Control2019; 41(5): 1243–1255.
16.
ChengPGaoZQianM, et al. Active fault tolerant control design for UAV using nonsingular fast terminal sliding mode approach. In: 2018 Chinese control and decision conference (CCDC), 9–11 June 2018, pp.292–297. New York: IEEE.
17.
GongWZhangJLiB, et al. Integral-type Sliding Mode based Fault-tolerant Attitude Stabilization of A Quad-rotor UAV. In: 2018 international symposium in sensing and instrumentation in IoT era (ISSI), Shanghai, China, 6–7 September 2018, pp.1–6. New York: IEEE.
18.
ShiXChengYYinC, et al. Actuator fault tolerant controlling using adaptive radical basis function neural network SMC for quadrotor UAV. In: 2019 Chinese control and decision conference (CCDC), Nanchang, China, 3–5 June 2019, pp.5409–5414. New York: IEEE.
19.
MallavalliSFekihA. Dynamic inversion and integral terminal sliding mode-based control design for a quadrotor UAV. In: 2018 IEEE 14th international conference on control and automation (ICCA), Anchorage, AK, 12–15 June 2018, pp.300–305. New York: IEEE.
20.
YuZLiuZZhangY, et al. Distributed finite-time fault-tolerant containment control for multiple unmanned aerial vehicles. IEEE T Neur Net Lear2020; 31: 2077–2091.
21.
TayariRBrahimABHmidaFB. Actuator fault tolerant control design for linear parameter varying systems. In: 2019 international conference on advanced systems and emergent technologies (IC_ASET), Hammamet, Tunisia, 19–22 March 2019, pp.17–21. New York: IEEE.
22.
MallavalliSFekihA. Sliding mode-based fault tolerant control designs for quadrotor UAVs-A comparative study. In: 2017 13th IEEE international conference on control & automation (ICCA), Ohrid, Macedonia, 3–6 July 2017, pp.154–159. New York: IEEE.
23.
HaoWXianB. Nonlinear fault tolerant control for a tri-rotor UAV against rear servo’s stuck fault. In: 2017 36th Chinese control conference (CCC), 26–28 July 2017, pp.7109–7114). New York: IEEE.
24.
AzimiVNekouiMAFakharianA. Robust multi-objective H2/H∞ tracking control based on the Takagi–Sugeno fuzzy model for a class of nonlinear uncertain drive systems. Proc IMechE, Part I: J Systems and Control Engineering2012; 226(8): 1107–1118.
25.
AzimiVMenhajMBFakharianA. Tool position tracking control of a nonlinear uncertain flexible robot manipulator by using robust H 2/H∞ controller via T–S fuzzy model. Sadhana2015; 40(2): 307–333.
26.
AllahverdyDFakharianA. Back-stepping controller design for altitude subsystem of hypersonic missile with Takagi-Sugeno fuzzy estimator. In: 2018 9th conference on artificial intelligence and robotics and 2nd Asia-Pacific international symposium, Kish Island, Iran, 10 December 2018, pp.80–86. New York: IEEE.
27.
XieXYueDPengC. Multi-instant observer design of discrete-time fuzzy systems: a ranking-based switching approach. IEEE T Fuzzy Syst2016; 25(5): 1281–1292.
28.
LiuXGaoZChenMZ. Takagi–Sugeno fuzzy model based fault estimation and signal compensation with application to wind turbines. IEEE T Ind Electron2017; 64(7): 5678–5689.
29.
LendekZBernaAGuzmán-GiménezJ, et al. Application of Takagi-Sugeno observers for state estimation in a quadrotor. In: 2011 50th IEEE conference on decision and control and European control conference, 12–15 December 2011, pp.7530–7535. New York: IEEE.
30.
XuBShiZYangC. Composite fuzzy control of a class of uncertain nonlinear systems with disturbance observer. Nonlinear Dynam2015; 80(1–2): 341–351.
31.
NianXChenWChuXXuZ. Robust adaptive fault estimation and fault tolerant control for quadrotor attitude systems. Int J Control2020; 93(3): 725–737.
32.
SongYHeLZhangD, et al. Neuroadaptive fault-tolerant control of quadrotor UAVs: a more affordable solution. IEEE T Neur Net Lear2018; 30(7): 1975–1983.
33.
SalamatBTonelloAM. Energy based control of a swash mass helicopter through decoupling change of coordinates. IEEE Access2020; 8: 77449–77458.
34.
AvramRCZhangXMuseJ. Nonlinear adaptive fault-tolerant quadrotor altitude and attitude tracking with multiple actuator faults. IEEE T Contr Syst T2017; 26(2): 701–707.
35.
EscareñoJSalazarSRomeroH, et al. Trajectory control of a quadrotor subject to 2D wind disturbances. J Intell Robot Syst2013; 70(1): 51–63.
