Command-filtered sensor-based backstepping controller for small unmanned aerial vehicles with actuator dynamics

Cao, Lu; Wang, Yongchao; Sheng-xiu, Zhang; Tan, Fei

doi:10.1080/00207721.2018.1540731

Cited by 8 publications

(7 citation statements)

References 37 publications

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“…Similarly to the processing method (26)- (28) in subsection III-A, it is easily verified that This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.…”

Section: Consider the Following Lyapunov Functionmentioning

confidence: 92%

“…where O i ( ρτ i ) denotes the degree of approximation between φ i,1 and Λ i,1 . Remark 3.1: Note that the command filters in [24]- [28] can merely ensure the asymptotic convergence, so the output signal of command filter can not faster approximate the derivative of virtual control signal. Although the command filter in [34] has the characteristic of finite-time convergence, the chattering phenomenon may arise.…”

Section: Position and Attitude Controllers Designmentioning

confidence: 99%

“…Remark 3.2: In previous works [24]- [28], the traditional ECM is designed to remove the effect of filter error. However, it is difficult to ensure faster convergence rate.…”

Section: Consider the Following Lyapunov Functionmentioning

confidence: 99%

“…Based on CFB technique, a new trajectory tracking control algorithm for quadrotor UAV was firstly proposed in [24]. Afterwards, several adaptive CFB control schemes have been developed in [25]- [28]. Nevertheless, these works can only achieve asymptotic convergence, and this means that the tracking error will converge to zero when the time approaches to infinity.…”

mentioning

confidence: 99%

“…1) Compared with the asymptotic convergence command filter used in [24]- [28], a novel command filter is introduced, which can not only approximate the derivative of virtual control signal but also realize the finite-time convergence. 2) Unlike the existing ECM results in [24]- [28] without considering the rapid convergence, the modified frac-tional order ECM is designed to quickly remove the effect of filter error. Meanwhile, in contrast to the symbolic function-based ECM proposed in [34], the modified fractional order ECM are constructed by the nonsmooth signal, so the chattering phenomenon is attenuated.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Finite-Time Adaptive Fuzzy Quantized Control for a Quadrotor UAV

Yang

Cui

et al. 2020

IEEE Access

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In this paper, a finite-time command filtered backstepping (FTCFB) adaptive trajectory tracking control strategy is proposed for a quadrotor unmanned aerial vehicle (UAV) with quantized inputs and external disturbances. For the position subsystem and attitude subsystem, a finite-time command filter is introduced to faster approximate the derivative of virtual control signal, which can effectively avoid the problem of explosion of complexity inherent in the traditional backstepping design procedure. The fractional order error compensation mechanism is designed to remove the filter error, and it further improves control performance. From the Lyapunov stability theory, the boundedness of all signals in the closed-loop system is rigorously proved, and the position and attitude tracking errors can converge to a small neighborhood of the origin in finite-time. Finally, a numerical example is conducted to intuitively show the validity of the developed control scheme.

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Section: Consider the Following Lyapunov Functionmentioning

confidence: 92%

Section: Position and Attitude Controllers Designmentioning

confidence: 99%

“…Remark 3.2: In previous works [24]- [28], the traditional ECM is designed to remove the effect of filter error. However, it is difficult to ensure faster convergence rate.…”

Section: Consider the Following Lyapunov Functionmentioning

confidence: 99%

mentioning

confidence: 99%