Nonsingular direct neural control of air-breathing hypersonic vehicle via back-stepping

Bu, Xiangwei; Wu, Xiaoyan; Ma, Zhen; Zhang, Rui

doi:10.1016/j.neucom.2014.11.040

Cited by 46 publications

(48 citation statements)

References 25 publications

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“…On this basis, several backstepping control methods are investigated for AHVs employing disturbance observer, 16 projection algorithm, 17,18 perturbed system, 19 and neural network. [19][20][21] It is noted that traditional backstepping control needs a complicated recursive design procedure, yielding a series of virtual control laws and causing a problem of "explosion of terms." In each virtual controller, there is a neural network utilized to approach the subsystem uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Neural network–based nonaffine control of air-breathing hypersonic vehicles with prescribed performance

Wang

2018

International Journal of Advanced Robotic Systems

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This article investigates a novel nonaffine control strategy using neural networks for an air-breathing hypersonic vehicle. Actual actuators are regarded as additional state variables and virtual control inputs are derived from low-computational cost neural approximations, while a new altitude control design independent of affine models is addressed for airbreathing hypersonic vehicles. To further reduce the computational load, an advanced regulation algorithm is applied to devise adaptive laws for neural estimations. Moreover, a new prescribed performance mechanism is exploited, which imposes preselected bounds on the transient and steady-state tracking error performance via developing new performance functions, capable of guaranteeing altitude and velocity tracking errors with small overshoots. Unlike some existing neural control methodologies, the proposed prescribed performance-based nonaffine control approach can ensure tracking errors with preselected transient and steady-state performance. Meanwhile, the complex design procedure of backstepping is also avoided. Finally, simulation results are presented to validate the design.

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Section: Introductionmentioning

confidence: 99%

Neural network–based nonaffine control of air-breathing hypersonic vehicles with prescribed performance

Wang

2018

International Journal of Advanced Robotic Systems

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“…In recent years, various strategies are utilized to design fault tolerant controller for nonlinear HSV systems, such as sliding mode control, predictive control, backstepping control, adaptive control, intelligent control and so on [1][2][3][4]. In the study of [5], an active FTC approach is proposed for the T-S fuzzy models of near space vehicle attitude dynamics using adaptive sliding mode techniques, but only the actuator loss of effectiveness fault was considered.…”

Section: Introductionmentioning

confidence: 99%

Attitude Stabilization of Hypersonic Vehicle under Actuator Faults and Saturation

Xu¹,

Huang²

2018

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Abstract. The paper focuses on fault tolerant attitude stabilization control for hypersonic vehicles (HSV) in the presence of loss of actuator effectiveness, input saturation and modeling uncertainties simultaneously. Firstly, an adaptive fuzzy observer was constructed to estimate the actuator faults and lumped disturbance. Then, the auxiliary system is introduced to compensate for saturation effect with the loss fault. Accordingly, a fault tolerant control law based on backstepping method is reconfigured under the reconstructed fault information. Finally based on the Lyapunov theory analysis, tracking error converges to arbitrary neighborhood around zero. It is shown that the proposed controller guarantees all the signals in the closed-loop system to be uniformly ultimately bounded. Finally, simulation results are given to illustrate the effectiveness of the proposed controller.

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“…Thus, improved backstepping control methodologies with robust performance can be achieved if the unknown nonlinearities are approximated by NNs [26][27][28][29]. To reduce the computational cost of neural approximation, the NNs are employed to approach the devised backstepping controllers rather than unknown functions while a nonsingular direct neural control strategy is addressed for an AHV [30]. Furthermore, a novel control scheme with low computational loads is exploited for an AHV based on a pure feedback affine model [31].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the strict requirements [30] that the unknown functions have to be strictly positive and bounded are released in this paper. Thereby, the proposed control scheme possesses excellent practicability and reliability.…”

Section: Introductionmentioning

confidence: 99%

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Concise Neural Nonaffine Control of Air-Breathing Hypersonic Vehicles Subject to Parametric Uncertainties

Bu¹,

Wang²,

Zhao³

et al. 2017

International Journal of Aerospace Engineering

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In this paper, a novel simplified neural control strategy is proposed for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV) directly using nonaffine models instead of affine ones. For the velocity dynamics, an adaptive neural controller is devised based on a minimal-learning parameter (MLP) technique for the sake of decreasing computational loads. The altitude dynamics is rewritten as a pure feedback nonaffine formulation, for which a novel concise neural control approach is achieved without backstepping. The special contributions are that the control architecture is concise and the computational cost is low. Moreover, the exploited controller possesses good practicability since there is no need for affine models. The semiglobally uniformly ultimate boundedness of all the closed-loop system signals is guaranteed via Lyapunov stability theory. Finally, simulation results are presented to validate the effectiveness of the investigated control methodology in the presence of parametric uncertainties.

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Nonsingular direct neural control of air-breathing hypersonic vehicle via back-stepping

Cited by 46 publications

References 25 publications

Neural network–based nonaffine control of air-breathing hypersonic vehicles with prescribed performance

Neural network–based nonaffine control of air-breathing hypersonic vehicles with prescribed performance

Attitude Stabilization of Hypersonic Vehicle under Actuator Faults and Saturation

Concise Neural Nonaffine Control of Air-Breathing Hypersonic Vehicles Subject to Parametric Uncertainties

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