Interval type‐2 fuzzy‐model‐based fault‐tolerant sliding mode tracking control of a quadrotor UAV under actuator saturation

Abstract

In this study, a fault‐tolerant position tracking control of a quadrotor unmanned aerial vehicle (UAV) is addressed by proposing a model reference interval type‐2 (IT‐2) fuzzy‐model‐based sliding mode tracking control methodology. Considering the underactuated characteristic of the quadrotor UAV, first, the authors separate the overall dynamics of the quadrotor into the attitude, altitude, and position subsystems. Moreover, each of them is represented via IT‐2 fuzzy model to deal with uncertainties of its membership functions. After then, a linear reference model supposed to be tracked by each subsystem is designed, on which each tracking error dynamics is derived. Given the tracking error dynamics and additive actuator faults, an IT‐2 integral fuzzy sliding surface is proposed to enhance the robust tracking performance of the entire system. As a result, a linear matrix inequality‐based sufficient condition is derived to guarantee the asymptotic stabilisation as well as satisfying an H∞ tracking performance. Furthermore, a reachability condition of the designed sliding surface is also proposed. Finally, the authors provide design examples to demonstrate the effectiveness of the proposed position tracking control methodology.

Decentralized Sampled-Data Fuzzy Tracking Control for a Quadrotor UAV with Communication Delay

“…In addition, the quadrotor UAV is an underactuated system with 6 degrees of freedom using 4 thrusts generated from 2 pairs of rotors rotating in opposite directions. To resolve these difficulties, studies were conducted on applying various control techniques to control quadrotor UAVs [1], such as PID control [2][3][4][5] and LQR control [6][7][8][9][10] as a linear control method and backstepping control [11][12][13][14], sliding mode control [13][14][15][16][17][18][19], and fuzzy control [20][21][22][23][24][25][26][27] as a nonlinear control method. As for the linear control technology, although PID control is easy to implement and LQR control can cope with disturbances, the dynamics of quadrotor UAV has nonlinearity and have to be linearized, so their performance is somewhat limited.…”

FTESO-adaptive neural network based safety control for a quadrotor UAV under multiple disturbances: algorithm and experiments

“…In the past several years, quadrotors have been applied in various industries (Aloqaily et al, 2022;Menouar et al, 2017). However, it is difficult to control the unmanned aerial vehicle (UAV) to achieve high-precision tracking performance for some reasons (Cui et al, 2021;Kim et al, 2020;Wang et al, 2022):…”

“…In the past several years, quadrotors have been applied in various industries (Aloqaily et al , 2022; Menouar et al , 2017). However, it is difficult to control the unmanned aerial vehicle (UAV) to achieve high-precision tracking performance for some reasons (Cui et al , 2021; Kim et al , 2020; Wang et al , 2022): The UAV is the nonlinear system with strong coupling between translational motion and rotational motion It is difficult to build model accurately because of the uncertainty of some parameters in the dynamic model. Actuator failure threatens the stability and safety of the system. The quadrotor usually flies in outdoor environments and are affected by gust winds. …”

Fault estimation and fault tolerant control for interval type-2 Takagi–Sugeno fuzzy systems via membership-function-dependent approach

“…FTC strategies under the framework of IT2 fuzzy model have also arisen some attention in recent years. The works in [26,[31][32][33] focus on the passive FTC techniques which cannot be used in online system repair after fault occurrence. In [34], an active FTC method was studied where the IT2 fuzzy observer and controller are designed separately with the extremely strict assumption that they share totally the same membership functions with the IT2 fuzzy model, resulting in a rather limited application scope.…”