Fuzzy-approximation-based prescribed performance control of air-breathing hypersonic vehicles with input constraints

Li, Xingge; Li, Gang; Zhao, Yan; Kang, Xuchao

doi:10.1177/0036850419877359

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…In the intricate and demanding flight environments hypersonic aircraft navigate, unpredictable airflow disturbances introduce a critical challenge, leading to actuator saturation. This undesirable condition significantly hampers the effectiveness of control inputs, impairing the aircraft's maneuverability and responsiveness [4,5]. The limitations imposed on the actuators result in a constrained control force, which, in turn, has adverse effects on the aircraft's overall flight performance.…”

Section: Introductionmentioning

confidence: 99%

RBFNN-Based Anti-Input Saturation Control for Hypersonic Vehicles

Zhang,

Liang,

Rao

et al. 2024

Aerospace

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In hypersonic flight control, characterized by challenges posed by input saturation, model parameter uncertainties, and external disturbances, this paper introduces a pioneering anti-input saturation control method based on RBFNN adaptivity. We have developed adaptive laws to enhance control system adaptability and robustness by integrating mission profiles, actuator saturation failure modes, and self-evolving neural network design. Furthermore, our approach introduces a novel anti-input saturation auxiliary system, effectively addressing input saturation constraints. This innovation ensures system stability and precise tracking, even in severe input saturation constraints. The results reveal that the system’s steady-state tracking error remains under 2% under input saturation constraints, and the convergence speed demonstrates an impressive 20% improvement. These findings underscore this research’s substantial advancement in hypersonic flight control. It may significantly enhance the controllability and performance of hypersonic vehicles in real-world scenarios.

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Section: Introductionmentioning

confidence: 99%

RBFNN-Based Anti-Input Saturation Control for Hypersonic Vehicles

Zhang,

Liang,

Rao

et al. 2024

Aerospace

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“…Besides, a disturbance observer based on sliding mode in An et al 10 are equivalent to a low order filter in Xu et al 9 However, due to the complicated characteristics in HVs' non-affine model, it is inevitable that at least two control procedures need to cooperate to achieve the desired performance. Adaptive control 11,12 and intelligent control 13,14 are respected as significantly auxiliary components, which are kindly linked with uncharted designed parameters. By devising adaptive law for the ideal weights of neural networks, Bu et al 11 improve a neural backstepping strategy for the HVs' non-affine dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, fuzzy control and neural networks control are widely employed in estimating intricate items. Li et al 13 give a fuzzy approximator to obtain the strict feedback form of nonlinear model based on backstepping mode. Pertaining to some continuously differentiable unknown functions, the radial basis function (RBF) neural network is adequate to imitate them in Bu and colleagues.…”

Section: Introductionmentioning

confidence: 99%

“…Once the actuator is saturated, the ideal control law could not be executed; finally, the system will diverge out of control. Li and colleagues 13,23 provide two different novel auxiliary systems, which tend to zero when the actuator works properly and compensates control law when it is limited. Unlike Li and Li, 23 Wang and Hu 24 describe actuator saturation as unknown nonlinear input dead zone.…”

Section: Introductionmentioning

confidence: 99%

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A novel neural prescribed performance control design for constrained flexible hypersonic vehicles with compensation system

Liu

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this study, a novel prescribed performance function and auxiliary system are investigated for the constrained flexible hypersonic vehicles. The non-affine model of hypersonic vehicles is decomposed into velocity and altitude dynamic systems. For the velocity one, the adaptive neural prescribed controller is devised to guarantee that the tracking error converges to an arbitrary small compact. Then, the altitude dynamics subsystem is delivered as a pure feedback non-affine pattern, which gets the controller out of the dependence on precise affine model. On this premise, the exploited prescribed performance mechanism can not only avoid obtaining the initial sign of tracking error in advance but also can be employed in solving the problem that the conventional prescribed performance function could not cover the tracking error when the altitude subsystem is input constraint. And a Nussbaum-type function is employed to handle the control direction. Meanwhile, the controller utilizes a new auxiliary compensation algorithm to pursue the stable situation for the input restricted system. In order to cut down the computational burden, neural networks employ the min-learning parameter theory to approximate uncertainties in the subsystems. Finally, the simulation results convey that the presented approach has a superior dynamic and steady-state performance.

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