Decentralized finite-time adaptive fault-tolerant synchronization tracking control for multiple UAVs with prescribed performance

Yu, Ziquan; Zhang, Youmin; Qu, Yaohong; Su, Chun‐Yi; Jiang, Bin

doi:10.1016/j.jfranklin.2019.11.056

Cited by 66 publications

(54 citation statements)

References 49 publications

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“…In [8], [18], the FTCC schemes were developed for multi-UAVs in the longitudinal plane within a distributed control design. [7], [19]- [22] further investigated the FTCC for attitude dynamics of multi-UAVs in the presence of actuator faults. However, it should be noted that the FTCC methods presented in [7], [8], [18]- [22] were developed to only regulate the longitudinal motions and attitude dynamics of multi-UAVs, with no consideration of six-DOF dynamics.…”

Section: Flight Tasksmentioning

confidence: 99%

“…• In comparison with the existing results which are concentrated on two-degree model [9]- [10], longitudinal motion [8], [18] and attitude dynamics [7], [19]- [22], [31]- [32] this study considers the distributed cooperative control for multi-UAVs where six-DOF UAV dynamics is considered subject to actuator faults, modeling uncertainties and external disturbances.…”

Section: Flight Tasksmentioning

confidence: 99%

See 1 more Smart Citation

Event-Triggered Adaptive Fault-Tolerant Synchronization Tracking Control for Multiple 6-DOF Fixed-Wing UAVs

Zhang

Sun

et al. 2022

IEEE Trans. Veh. Technol.

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Section: Flight Tasksmentioning

confidence: 99%

Section: Flight Tasksmentioning

confidence: 99%

Event-Triggered Adaptive Fault-Tolerant Synchronization Tracking Control for Multiple 6-DOF Fixed-Wing UAVs

Zhang

Sun

et al. 2022

IEEE Trans. Veh. Technol.

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“…In [25], the dynamic surface control technique was introduced into the neural network adaptive control design framework, and the backstepping-based control design was carried out for a class of nonlinear systems with arbitrary uncertainties and strict feedback forms. In [26], neural networks were utilized to identify the unknown nonlinear terms induced by uncertainties and actuator faults. To reduce the computational burden caused by estimating the weight vectors, the norms of weight vectors were used for the estimation.…”

Section: Introductionmentioning

confidence: 99%

Radial Basis Function Neural Network Sliding Mode Control for Ship Path Following Based on Position Prediction

Zhang

2021

JMSE

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In the process of ship navigation, due to the characteristics of large inertia and large time delay, overshoot can easily occur in the process of path following. Once the ship deviates from the waypoint, it is prone to grounding and collision. Considering this problem, a sliding mode control algorithm based on position prediction using the radial basis function (RBF) neural network is proposed. The desired heading angle is designed according to a backstepping algorithm. The hyperbolic tangent function is used to design the sliding surface, and the course is controlled by sliding mode control. The second-order Taylor expansion is used to predict the future position, the current error and future error functions are constructed, and the total errors are fed back to the desired heading angle. In the sliding mode control system, the RBF neural network is used to approximate the total unknown term, and a velocity observer is introduced to obtain the surge velocity and sway velocity. To verify the effectiveness of the algorithm, the mathematical model group (MMG) model is used for simulation. The simulation results show the effectiveness and superiority of the designed controller. Therefore, the RBF neural network sliding mode controller based on predicted position has robustness for ship path following.

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“…Various FTC strategies were reported in specialised literature: linear matrix inequality (Andrade et al, 2017), the pseudo-inverse (Tchon & Janiak, 2009), multiple model (Pandey, Kar & Mahanta, 2017) and adaptive control methods (Yu et al, 2019), robust controls (Zhi & al., 2018), the Algebraic Riccati Equation (ARE), the Hamilton-Jacobi Equation (HJE), the sliding mode control (SMC) (Zhang et al, 2018) and intelligent controls based on artificial neural network (Yen & Ho, 2004). The "suitable" technique for FTC depends on the type of system considered and the nature/gravity of the fault.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Fault Recovery of a Quadrotor Using Progressive Accommodation

Mlayeh¹,

Ghachem²,

Nasri³

et al. 2020

STUD INFORM CONTROL

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This paper proposes a nonlinear (NL) fault-tolerant control (FTC) strategy for stabilizing the attitude of a quadrotor affected by multiple rotor failures. The recovery process relies mainly on progressive accommodation (PA) strategy. The latter uses the Newton-Raphson (NR) algorithm in order to solve the state-dependent Riccati Equation (SDRE) in a recursive way. Simulations are performed using the NL model of the quadrotor in fault-free and post-fault cases. Both PA and the direct SDRE methods are employed so that the attitude control of the quadrotor can be stabilized. The results show that the PA strategy performs better than the classical method when it comes to convergence speed and to preserving the control objectives of the nominal system.

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Decentralized finite-time adaptive fault-tolerant synchronization tracking control for multiple UAVs with prescribed performance

Cited by 66 publications

References 49 publications

Event-Triggered Adaptive Fault-Tolerant Synchronization Tracking Control for Multiple 6-DOF Fixed-Wing UAVs

Event-Triggered Adaptive Fault-Tolerant Synchronization Tracking Control for Multiple 6-DOF Fixed-Wing UAVs

Radial Basis Function Neural Network Sliding Mode Control for Ship Path Following Based on Position Prediction

Nonlinear Fault Recovery of a Quadrotor Using Progressive Accommodation

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