A guaranteed transient performance-based adaptive neural control scheme with low-complexity computation for flexible air-breathing hypersonic vehicles

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

doi:10.1007/s11071-016-2637-0

Cited by 65 publications

(39 citation statements)

References 42 publications

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“…The reference command of g is chosen as 25, 31 We further conclude that the prescribed performance ofh can be guaranteed according to theorem 1.…”

Section: Controller Designmentioning

confidence: 90%

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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“…The reference command of g is chosen as 25, 31 We further conclude that the prescribed performance ofh can be guaranteed according to theorem 1.…”

Section: Controller Designmentioning

confidence: 90%

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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“…Following this success, a low‐complexity control (LCC) is designed guaranteeing preselected tracking quality for uncertain strict‐feedback systems with deadzone input nonlinearity and disturbances . A guaranteed transient performance‐based adaptive neural control scheme with low‐complexity computation is designed for flexible air‐breathing hypersonic vehicles . A control method that combining prescribed tracking performance and controller simplicity is designed for a class of uncertain MIMO nonlinear systems with input quantization .…”

Section: Introductionmentioning

confidence: 99%

Robust resilient control for parametric strict feedback systems with prescribed output and virtual tracking errors

Gao

Dong

Zheng

2019

Intl J Robust & Nonlinear

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Summary This paper investigates the robust resilient control problem for a class of parametric strict feedback nonlinear systems with prescribed output and virtual tracking errors performance. The resilience is governed by a continuously nonlinear control gains function, which endows the virtual and actual controllers with self‐adjusting abilities with respect to transformed error surfaces. The proposed control scheme is adaptation, estimation, and approximation‐free in the presence of unknown parameters and nonlinearities, and only a number of control gains, which is equal to the relative degree of the considered plants, need to be selected in applications. It is proved by rigorous analysis that the output tracking errors are confined in predefined prescribed performance functions under some nonrestrictive initial conditions, and the bounds of states are obtained characterized as control gains and systemic function‐associated constants. Finally, comparative illustrative examples are given to demonstrate the effectiveness of the proposed control scheme.

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“…The problem has been addressed in the works of Bechlioulis and Rovithakis 13,14 and Theodorakopoulos and Rovithakis 15 by means of prescribed performance control (PPC) techniques, which permit the designer to prescribe the transient and steady-state performance of the closed-loop system, regarding the overshoot, the convergence rate, and the steady-state error. The concept of PPC was further extended to flight control systems, [16][17][18][19] vessel control systems, 20 and output-feedback control systems [21][22][23] successfully. Even so, it is noteworthy that the aforementioned PPC approaches 13,14,[21][22][23] require the a priori known control direction (ie, the sign of the control coefficient).…”

Section: Introductionmentioning

confidence: 99%

Adaptive prescribed performance control of nonlinear output‐feedback systems with unknown control direction

Zhang

Yang

2018

Intl J Robust & Nonlinear

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Summary This paper focuses on the output‐feedback tracking control problem for a class of nonlinear systems with both unknown nonlinearities and unknown control directions. An adaptive prescribed performance controller combined with a Nussbaum gain and a dividing line is proposed to solve the problem. Compared with the existing results, (i) both the convergence rate and the ultimate bound of the tracking error can be prescribed; (ii) no approximating structures such as neuro/fuzzy systems are used, regardless of unknown nonlinear functions; and (iii) the computational burden is alleviated in the sense that the iterative calculation of command derivatives is avoided and the number of online learning parameters is largely reduced. Simulation results are given to further illustrate the established theoretical findings.

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A guaranteed transient performance-based adaptive neural control scheme with low-complexity computation for flexible air-breathing hypersonic vehicles

Cited by 65 publications

References 42 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

Robust resilient control for parametric strict feedback systems with prescribed output and virtual tracking errors

Adaptive prescribed performance control of nonlinear output‐feedback systems with unknown control direction

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