Adaptive output feedback fault-tolerant control design for hypersonic flight vehicles

He, Jingjing; Qi, Ruiyun; Jiang, Bin; Qian, Jiasong

doi:10.1016/j.jfranklin.2015.01.016

Cited by 94 publications

(47 citation statements)

References 27 publications

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“…By combining fractional order proportion integral and derivative with active disturbance rejection control method, a nonlinear flight controller is devised for an AHV to provide accurate and fast tracking of desired attitude signals. 9 In addition, fault-tolerant controllers, 10,11 predictive control law, 12 and constrained control methodologies [13][14][15] are also widely studied for AHVs. Under strict assumptions, the altitude dynamics of AHVs can be rewritten as a strict-feedback system of affine formulation.…”

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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“…9 In the study by Qi et al, 7 an adaptive backstepping FTC scheme was proposed for a hypersonic vehicle with parameter uncertainties and actuator fault. The dynamic inversion control technique was introduced to handle the problem of velocity control, and the adaptive backstepping method was applied to guarantee the attitude tracking.…”

Section: Introductionmentioning

confidence: 99%

Adaptive sliding mode fault-tolerant control for hypersonic vehicle based on radial basis function neural networks

Zhai

Jiang

2017

International Journal of Advanced Robotic Systems

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In this article, an adaptive sliding mode fault-tolerant control scheme is proposed to address the problem of robust and fast attitude tracking for a hypersonic vehicle in the presence of unknown external disturbances, additive fault and partial loss of effectiveness fault. Firstly, the healthy and faulty models of the vehicle are given. Then, a radial basis function neural network is designed to estimate the unknown additive fault, and the adaptive method is applied to deal with the unknown partial loss of effectiveness fault. Combined with the sliding mode control theory, the fault-tolerant controllers are designed for the outer and inner loops of the faulty system, respectively. The adaptive laws are designed to update parameter estimates to implement the inner-loop controller. Closed-loop stability is analysed and simulation results verify the effectiveness of the proposed fault-tolerant control scheme.

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“…However, the design of control systems for HFVs is a challenging work due to the longitudinal dynamics of HFVs being highly nonlinear and strong couplings between the propulsive and aerodynamic forces [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Neural Networks Approximator Based Robust Adaptive Controller Design of Hypersonic Flight Vehicles Systems Coupled with Stochastic Disturbance and Dynamic Uncertainties

Zhu

Sun

Zhang

2017

Mathematical Problems in Engineering

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A neural network robust control is proposed for a class of generic hypersonic flight vehicles with uncertain dynamics and stochastic disturbance. Compared with the present schemes of dealing with dynamic uncertainties and stochastic disturbance, the outstanding feature of the proposed scheme is that only one parameter needs to be estimated at each design step, so that the computational burden can be greatly reduced and the designed controller is much simpler. Moreover, by introducing a performance function in controller design, the prespecified transient and performance of tracking error can be guaranteed. It is proved that all signals of closed-loop system are uniformly ultimately bounded. The simulation results are carried out to illustrate effectiveness of the proposed control algorithm.

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Adaptive output feedback fault-tolerant control design for hypersonic flight vehicles

Cited by 94 publications

References 27 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

Adaptive sliding mode fault-tolerant control for hypersonic vehicle based on radial basis function neural networks

Neural Networks Approximator Based Robust Adaptive Controller Design of Hypersonic Flight Vehicles Systems Coupled with Stochastic Disturbance and Dynamic Uncertainties

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