Robust estimation-free prescribed performance back-stepping control of air-breathing hypersonic vehicles without affine models

Bu, Xiangwei; Wu, Xiaoyan; Huang, Jiaqi; Wei, Daozhi

doi:10.1080/00207179.2016.1151080

Cited by 55 publications

(48 citation statements)

References 38 publications

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“…One of the major issues with respect to the developments of AHVs is to maintain controllability of the vehicle during various phases of flight [1][2][3]. However, flight control design for such vehicles is very challenging due to significant uncertainties, strong couplings, nonminimum phase characteristics, and significant flexible effects [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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

Bu¹,

Wang²,

Zhao³

et al. 2017

International Journal of Aerospace Engineering

Self Cite

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“…3. By comparison with traditional prescribed performance control approaches, 28,29 better transient performance guaranteeing small overshoot can be imposed on tracking errors based on a newly constructed prescribed performance mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, guaranteeing transient performance-based prescribed performance control methods have been presented for AHVs to provide robust tracking of reference commands with the tracking errors satisfying the predesigned transient and steady-state performance. [25][26][27][28] Unfortunately, none of them can guarantee the tracking error with small even zero overshoot. In this article, a novel prescribed performance control approach using nonaffine models is proposed for an AHV based on neural approximation.…”

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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“…Subsequently, a low-complexity approximation-free PPC scheme has been employed to the control of HFV. 45 Although the prescribed performance problem has been tackled, the actuator saturation problem is not considered in it, and there indispensably exist the strict assumptions for nonlinear function with this control scheme. To the best of the author's knowledge, so far, a composite low-computational neural controller, which is capable of handling actuator and state constraints as well as guaranteeing prescribed performance on transient and steady-state behaviour of the output tracking errors, simultaneously, has not been investigated for HFV.…”

Section: Introductionmentioning

confidence: 99%

Tracking error constrained robust adaptive neural prescribed performance control for flexible hypersonic flight vehicle

Shi

et al. 2017

International Journal of Advanced Robotic Systems

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A robust adaptive neural control scheme based on a back-stepping technique is developed for the longitudinal dynamics of a flexible hypersonic flight vehicle, which is able to ensure the state tracking error being confined in the prescribed bounds, in spite of the existing model uncertainties and actuator constraints. Minimal learning parameter technique-based neural networks are used to estimate the model uncertainties; thus, the amount of online updated parameters is largely lessened, and the prior information of the aerodynamic parameters is dispensable. With the utilization of an assistant compensation system, the problem of actuator constraint is overcome. By combining the prescribed performance function and sliding mode differentiator into the neural back-stepping control design procedure, a composite state tracking error constrained adaptive neural control approach is presented, and a new type of adaptive law is constructed. As compared with other adaptive neural control designs for hypersonic flight vehicle, the proposed composite control scheme exhibits not only low-computation property but also strong robustness. Finally, two comparative simulations are performed to demonstrate the robustness of this neural prescribed performance controller.

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Robust estimation-free prescribed performance back-stepping control of air-breathing hypersonic vehicles without affine models

Cited by 55 publications

References 38 publications

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

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

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

Tracking error constrained robust adaptive neural prescribed performance control for flexible hypersonic flight vehicle

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