A new adaptive neural control scheme for hypersonic vehicle with actuators multiple constraints

Luo, Changxin; Lei, Humin; Li, Jiong; Zhou, Chijun

doi:10.1007/s11071-020-05707-2

Cited by 32 publications

(15 citation statements)

References 44 publications

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“…In the design of the HSV control system, not only the nonlinear dynamic characteristics but also the strong coupling between aerodynamic and propulsion effects and the elastic vibration caused by flexible materials at high temperature should be considered. 2,3 In the previous studies, most researchers focused on the affine models of HSV, that is, based on certain assumptions, the HSV models are simplified to the affine forms of control inputs. He et al combine dynamic surface control and radial basis function neural network (RBFNN), and introduce robust components into the controller to offset the effects of composite disturbances to improve the robustness of the system.…”

Section: Introductionmentioning

confidence: 99%

“…11,12 The theory of funnel control can take both transient and steady-state performance into account when dealing with control problems, but the design methods are complex. 2,3,13 Bu proposes a new input-to-state stable system, from which we get a strict-feedback non-affine model that is convenient for control designing based on a model transformation approach. 14 In this article, we propose a concise funnel robust model-free control mechanism based on error driving.…”

Section: Introductionmentioning

confidence: 99%

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A concise funnel robust model-free control mechanism for hypersonic space vehicles based on error driving

Zhang

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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Based on non-affine models of hypersonic space vehicles, the tracking control problem of hypersonic vehicles is studied and analyzed in this article using funnel robust model-free control mechanism considering parametric uncertainty and external disturbances. First, the control system is decomposed into altitude subsystem and velocity subsystem. For altitude subsystem, we propose a concise funnel robust model-free control mechanism based on error driving, and a novel model transformation approach is applied to the controller design. The new model-free controller only contains a Hurwitz stable term and a filtering term, and does not need precise motion model and too much calculation, so it can improve the calculation speed of the system. For velocity subsystem, only a concise proportional-integral controller is needed to meet the tracking requirements. Moreover, the devised controller is capable of guaranteeing funnel performance on the altitude and velocity tracking errors. Finally, numerical simulation results are presented to verify the efficiency of the design.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A concise funnel robust model-free control mechanism for hypersonic space vehicles based on error driving

Zhang

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…Ref. [37] proposes an adaptive control scheme with multiple constraints on the amplitude and rate of the actuator. Although the amplitude and rate of the control input are constrained effectively by this scheme, the controller design is too complicated.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with Ref. [36,37], the control law design in this paper is simpler and easier to implement in practice, and the theoretical proof is completed to guarantee the effectiveness of proposed method…”

Section: Introductionmentioning

confidence: 99%

Backstepping Control Based on Constrained Command Filter for Hypersonic Flight Vehicles with AOA and Actuator Constraints

Wei

Guo

et al. 2021

International Journal of Aerospace Engineering

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An antisaturation backstepping control scheme based on constrained command filter for hypersonic flight vehicle (HFV) is proposed with the consideration of angle of attack (AOA) constraint and actuator constraints of amplitude and rate. Firstly, the HFV system model is divided into velocity subsystem and height subsystem. Secondly, to handle AOA constraint, a constrained command filter is constructed to limit the amplitude of the AOA command and retain its differentiability. And the constraint range is set in advance via a prescribed performance method to guarantee that the tracking error of the AOA meets the constraint conditions and transient and steady performance. Thirdly, the proposed constrained command filter is combined with the auxiliary system for actuator constraints, which ensures that the control input meets the limited requirements of amplitude and rate, and the system is stable. In addition, the tracking errors of the system are proved to be ultimately uniformly bounded based on the Lyapunov stability theory. Finally, the effectiveness of the proposed method is verified by simulation.

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“…Neural networks are currently being applied to solve many complex engineering problems, and the demand is increasing with time. A wide range of applications starting from dynamics modeling to intelligent control is being handled by neural networks [4,27,26,13].…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network-based Nonlinear Dynamics Model for the Prediction of Lateral-longitudinal Vehicle Dynamics

Pan

Nie

Dai

et al. 2021

Preprint

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The vehicle multibody model can be used for accurate coupling dynamics, but it has higher computational complexity. Numerical stability during integration is also very challenging, especially in complicated driving situations. This issue can be substantially alleviated by using a data-driven nonlinear dynamics model owing to its computational speed and robust generalization. In this work, we propose a deep neural network (DNN)-based modeling approach for predicting lateral-longitudinal vehicle dynamics. Dynamic simulations of vehicle systems are performed based on a semirecursive multibody formulation for data acquisition. The data are then used for training and testing the DNN model. The DNN inputs are the torque applied on wheels and the initial vehicle speed that imitates a double lane change maneuver with acceleration and deceleration. The DNN outputs are the longitudinal driving distance, lateral driving distance, final longitudinal velocities, final lateral velocities, and yaw angle. The dynamic responses obtained from the DNN model are compared with the multibody results. Furthermore, the accuracy of the DNN model is investigated in terms of error functions. The DNN model is finally verified via the results of a commercial software package. The results show that the DNN vehicle dynamics model predicts accurate dynamic responses in real time. The DNN model can be used for real-time simulation and preview control in autonomous vehicles.

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A new adaptive neural control scheme for hypersonic vehicle with actuators multiple constraints

Cited by 32 publications

References 44 publications

A concise funnel robust model-free control mechanism for hypersonic space vehicles based on error driving

A concise funnel robust model-free control mechanism for hypersonic space vehicles based on error driving

Backstepping Control Based on Constrained Command Filter for Hypersonic Flight Vehicles with AOA and Actuator Constraints

A Deep Neural Network-based Nonlinear Dynamics Model for the Prediction of Lateral-longitudinal Vehicle Dynamics

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