Nonlinear robust neuro-adaptive flight control for hypersonic vehicles with state constraints

Sachan, Kapil; Padhi, Radhakant

doi:10.1016/j.conengprac.2020.104526

Cited by 19 publications

(3 citation statements)

References 9 publications

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“…Due to the effect on limiting the designated states or outputs, the BLF is an effective tool for state‐constrained problems 26‐29 . In References 30‐32, the BLF is utilized in the HFV control, where the tracking errors of the relative states are restricted. Considering the asymmetric constraint on AOA, the asymmetric BLF is utilized in Reference 33, while in Reference 34, the asymmetric time‐varying error bounds is guaranteed using an tan‐type BLF.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric integral BLF based state‐constrained flight control using NN and DOB

Guo

Yan

et al. 2022

Intl J Robust & Nonlinear

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This article proposes an adaptive back‐stepping flight controller under angle of attack (AOA) constraints. Taking the hypersonic flight vehicle (HFV) for example, considering the AOA constraints caused by the scramjet intake requirements, the asymmetric integral barrier Lyapunov function is used to design the controller, while the AOA is directly limited to a predefined interval without setting the tracking error and virtual control constraints separately. Furthermore, the neural networks and disturbance observer are applied in the controller to deal with the model uncertainty and external disturbance. The system stability and the AOA constraints are proved via Lyapunov analysis. Simulation tests on HFV longitudinal dynamics present that the proposed controller can prevent the AOA from exceeding the given bounds while altitude and velocity tracking.

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Section: Introductionmentioning

confidence: 99%

Asymmetric integral BLF based state‐constrained flight control using NN and DOB

Guo

Yan

et al. 2022

Intl J Robust & Nonlinear

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“…Although the failure observer's assistance was used to estimate failure information, the overall system's stability was not revealed, making it impossible to assess the process's condition at any time. We investigate the failure tracking control of a hypersonic vehicle employing terminal sliding mode theory and a neural network approach in this research [25][26][27][28][29]. In this paper, a fault-tolerant control (FTC) method combining radial basis function neural network (RBFNN) and adaptive terminal sliding mode (ATSM) is proposed, which can track the ABHV trajectory of an ABHV in the presence of air density, mass, and moment of inertia uncertainties as well as actuator faults.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Control of Hypersonic Vehicle Using Neural Network and Sliding Mode

Cui

Gao

Jing

et al. 2022

International Journal of Aerospace Engineering

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In this paper, the tracking control of the air-breathing hypersonic vehicle with model parameter uncertainties and actuator faults is studied. Firstly, a high-order linearization model is used to build an adaptive terminal sliding mode that eliminates chattering and provides increased robustness for unknown disturbances in the system. Second, a fault-tolerant control method mixing the radial basis function neural network with adaptive sliding mode control is suggested, with the addition of a hyperbolic tangent function to avoid controller input saturation. Finally, the stability of the controller is proved strictly by the Lyapunov theory, and the robustness and effectiveness of the controller are further verified by numerical simulations of the longitudinal model of the hypersonic vehicle.

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“…Basic structure diagram of state detection system.The traditional flight test acquisition system consists of a unified time service subsystem, a sensor regulator for sensing the changes of physical quantities, a general acquisition subsystem for conditioning and collecting data, and a recording subsystem for recording all data. As shown in Figure1, the distributed optical fiber FBG acquisition subsystem can be accessed into the test system in two ways[5]. The first one is directly connected to the recording subsystem, which is used in the case of large amount of data acquisition and needs to match with the relevant interface of the recording subsystem; the second way is to enter the recording subsystem after being collected by the general collection subsystem, which is applicable to the case of small amount of data.Generally, both methods are available, but the second method is more efficient.…”

mentioning

confidence: 99%

Aircraft Condition Detection Based on Distributed Optical Fiber Sensing Technology

Zhang

Tian

2022

J. Phys.: Conf. Ser.

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This paper describes the principle of distributed optical fiber sensing technology and its application in the field of aircraft condition detection. Firstly, the differences between two kinds of distributed fiber sensing technologies are introduced, and the characteristics of the existing typical distributed fiber Bragg grating sensing system and the system type selection need system coverage, parameter sampling rate and reflected light demodulation mode. Then it introduces the principle and steps of using wavelet transform algorithm to analyze the aircraft state information. Finally, the system architecture suitable for the aircraft state detection environment is analyzed and proposed, and the integration scheme of the system with the state detection information acquisition system is discussed based on the characteristics of the system, and the problems needing attention such as time synchronization, data frame format and system interface are summarized.

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Nonlinear robust neuro-adaptive flight control for hypersonic vehicles with state constraints

Cited by 19 publications

References 9 publications

Asymmetric integral BLF based state‐constrained flight control using NN and DOB

Asymmetric integral BLF based state‐constrained flight control using NN and DOB

Fault-Tolerant Control of Hypersonic Vehicle Using Neural Network and Sliding Mode

Aircraft Condition Detection Based on Distributed Optical Fiber Sensing Technology

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