NFTSM-based Fault Tolerant Control for Quadrotor Unmanned Aerial Vehicle with Finite-Time Convergence

Wang, Jing; Zhao, Zhihui; Zheng, Ying

doi:10.1016/j.ifacol.2018.09.614

Cited by 11 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[1]- [4]. It is well known that the QUAVs are under-actuated, strongly nonlinear and coupling systems, thus the stability analysis and controller design for QUAVs are still challenging [5], [6]. Fortunately, as to these issues, some efforts have been tried by scholars, and lots of results were achieved [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

ASMC and ARSMC Cascaded Controller Design for QUAVs With Time-Varying Load and Unknown Disturbances

Weng,

Wei,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In this paper, the cascaded controllers, which combine the adaptive sliding mode control and recursive technique, are presented to control the quadrotor unmanned aerial vehicles with time-varying load and unknown disturbances. In the outer-loop control subsystem, an adaptive sliding mode controller, which includes the adaptive laws estimating the time-varying load and unknown disturbances, is presented to control the position of quadrotor. In the inner-loop control subsystem, an adaptive recursive sliding mode controller, which includes a self-turning law estimating the unknown inner-loop disturbances, is designed to control the attitude of quadrotor. Moreover, a nonsingular terminal sliding mode surface is given to converge the tracking error of attitude angles to zero in a finite time; and an integral sliding mode surface is designed in the adaptive recursive sliding mode controller to improve the attitude control performance of quadrotor. Simultaneously, the adaptive gain adjustment laws are given to estimate those unknown upper bounds of the disturbances in the system. In the end, the efficiency and feasibility of the proposed control method are demonstrated by some computer simulations.INDEX TERMS Unmanned aerial vehicle, time-varying load, trajectory tracking control, adaptive control, unknown disturbance.

show abstract

Section: Introductionmentioning

confidence: 99%

ASMC and ARSMC Cascaded Controller Design for QUAVs With Time-Varying Load and Unknown Disturbances

Weng,

Wei,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…However, the instability of the control system due to external or self-disturbances, as well as actuator and sensor failures, is a common phenomenon for UAVs [22,23]. Therefore, many researchers have considered combining intelligent control algorithms with robust and adaptive control to propose fault-tolerant control algorithms that can solve a class of faults [24][25][26]. Boche et al proposed a fault-tolerant control scheme that utilizes continuous and discrete methods to address multiple actuator failures [27].…”

Section: Introductionmentioning

confidence: 99%

Fractional Gradient Descent RBFNN for Active Fault-Tolerant Control of Plant Protection UAVs

Hua,

Zhang,

et al. 2024

CMES

View full text Add to dashboard Cite

With the increasing prevalence of high-order systems in engineering applications, these systems often exhibit significant disturbances and can be challenging to model accurately. As a result, the active disturbance rejection controller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmanned aerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances and the possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address these issues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neural network (RBFNN) with a second-order ADRC and leverages a fractional gradient descent (FGD) algorithm. We integrate the plant protection UAV model's uncertain parameters, load disturbance parameters, and actuator fault parameters and utilize the RBFNN for system parameter identification. The resulting ADRC exhibits load disturbance suppression and fault tolerance capabilities, and our proposed active fault-tolerant control law has Lyapunov stability implications. Experimental results obtained using a multi-rotor fault-tolerant test platform demonstrate that the proposed method outperforms other control strategies regarding load disturbance suppression and fault-tolerant performance.

show abstract

“…Nevertheless, the control schemes of the above controllers may cause the problem of singularity because they contain negative fractional power terms [27]. Nonsingular FTSMC (NFTSMC) has been designed to avoid singularity [28]. But, the singularity is avoided at the cost of a slowdown in its convergence rate.…”

Section: Introductionmentioning

confidence: 99%

An Augmented Sliding Mode Control for Fixed-Wing UAVs with External Disturbances and Model Uncertainties

Pan

Zou

et al. 2023

Drones

View full text Add to dashboard Cite

Model uncertainties and external disturbances present significant challenges for controlling fixed-wing unmanned aerial vehicles (UAVs). An adaptive smooth second-order time-varying nonsingular fast terminal sliding mode control method is proposed in this paper for attitude and airspeed control of fixed-wing UAVs with model uncertainties and external disturbances. This control method does not require information about the bounds of disturbances and can avoid overestimation of the control gains. A radial basis function neural network observer is designed to mitigate the influence caused by sudden disturbances. The convergence of the attitude and airspeed controllers is proven by using the Lyapunov stability theory. Simulation results demonstrate the effectiveness of the proposed method for controlling a six-degrees-of-freedom fixed-wing UAV.

show abstract

NFTSM-based Fault Tolerant Control for Quadrotor Unmanned Aerial Vehicle with Finite-Time Convergence

Cited by 11 publications

References 14 publications

ASMC and ARSMC Cascaded Controller Design for QUAVs With Time-Varying Load and Unknown Disturbances

ASMC and ARSMC Cascaded Controller Design for QUAVs With Time-Varying Load and Unknown Disturbances

Fractional Gradient Descent RBFNN for Active Fault-Tolerant Control of Plant Protection UAVs

An Augmented Sliding Mode Control for Fixed-Wing UAVs with External Disturbances and Model Uncertainties

Contact Info

Product

Resources

About