Adaptive fault-tolerant controller for morphing aircraft based on the L2 gain and a neural network

Yuan, Laohu; Wang, LiDong; Xu, JiangTao

doi:10.1016/j.ast.2022.107985

Cited by 15 publications

(4 citation statements)

References 38 publications

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“…Salahudden [11] proposed a new method to generate an optimal climb trajectory with a standard stabilized climb equation that produced a unique combination of flight speed and track angle at each altitude to ensure a minimum fuel consumption and time-efficient climb. Yuan [12] developed a nonlinear dynamics model of a deformed vehicle and used recursive damped least squares to estimate and optimize the aircraft system model.…”

Section: State-space Model Methodsmentioning

confidence: 99%

“…Existing TP methods can be categorized as state-space model methods and data-driven methods. For the state-space model methods [9][10][11][12] combined with flight environmental constraints, the flight trajectory is predicted by modeling the trajectory of the vehicle in the case of relative motion with air. For the data-driven approach [13][14][15][16][17][18], the data are organized to form information based on the large amount of collected flight trajectory data.…”

Section: Introductionmentioning

confidence: 99%

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TCN-Informer-Based Flight Trajectory Prediction for Aircraft in the Approach Phase

Dong,

Fan,

et al. 2023

Sustainability

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Trajectory prediction (TP) is a vital operation in air traffic control systems for flight monitoring and tracking. The approach phase of general aviation (GA) aircraft is more of a visual approach, which is related to the safety of the flight and whether to go around. Therefore, it is important to accurately predict the flight trajectory of the approach phase. Based on the historical flight trajectories of GA aircraft, a TP model is proposed with deep learning after feature extraction in this study, and the hybrid model combines a time convolution network and an improved transformer model. First, feature extraction of the spatiotemporal dimension is performed on the preprocessed flight data by using TCN; then, the extracted features are executed by adopting the Informer model for TP. The performance of the novel architecture is verified by experiments based on real flight trajectory data. The results show that the proposed TCN-Informer architecture performs better according to various evaluation metrics, which means that the prediction accuracies of the hybrid model are better than those of the typical prediction models widely used today. Moreover, it has been verified that the proposed method can provide valuable suggestions for decision-making regarding whether to go around during the approach.

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Section: State-space Model Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TCN-Informer-Based Flight Trajectory Prediction for Aircraft in the Approach Phase

Dong,

Fan,

et al. 2023

Sustainability

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“…Chen et al transformed the nonlinear dynamics equations of the vehicle into an affine nonlinear form, and then a backstepping method was applied to design the controller [11]. Yuan et al presented an adaptive controller designed by backstepping and based on the L 2 gain [12]. In [13,14], an adaptive backstepping controller has been proposed, in which a radial basis function (RBF) neural network was used to estimate the uncertain terms of the aircraft.…”

Section: Introductionmentioning

confidence: 99%

L1 Adaptive Control Based on Dynamic Inversion for Morphing Aircraft

Cheng,

Li,

Yuan

et al. 2023

Aerospace

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Morphing aircraft are able to keep optimal performance in diverse flight conditions. However, the change in geometry always leads to challenges in the design of flight controllers. In this paper, a new method for designing a flight controller for variable-sweep morphing aircraft is presented—dynamic inversion combined with L1 adaptive control. Firstly, the dynamics of the vehicle is analyzed and a six degrees of freedom (6DOF) nonlinear dynamics model based on multibody dynamics theory is established. Secondly, nonlinear dynamic inversion (NDI) and incremental nonlinear dynamic inversion (INDI) are then employed to realize decoupling control. Thirdly, linear quadratic regulator (LQR) technique and L1 adaptive control are adopted to design the adaptive controller in order to improve robustness to uncertainties and ensure the control accuracy. Finally, extensive simulation experiments are performed, wherein the demonstrated results indicate that the proposed method overcomes the drawbacks of conventional methods and realizes an improvement in control performance.

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“…A robust

FTC scheme to regulate the quadrotor system was adopted in [ 29 ]: the approach was based on linear matrix inequalities (LMI), so the overall dynamics of the quadrotor were linearized. In [ 30 ], backstepping and adaptive control methods based on

-gain were combined together, to provide a passive fault-tolerant attitude controller of a nonlinear dynamic model of morphing aircraft.…”

Section: Introductionmentioning

confidence: 99%

Min–Max Optimal Control of Robot Manipulators Affected by Sensor Faults

Milić

Kasač

Lukas

2023

Sensors

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This paper is concerned with the control law synthesis for robot manipulators, which guarantees that the effect of the sensor faults is kept under a permissible level, and ensures the stability of the closed-loop system. Based on Lyapunov’s stability analysis, the conditions that enable the application of the simple bisection method in the optimization procedure were derived. The control law, with certain properties that make the construction of the Lyapunov function much easier—and, thus, the determination of stability conditions—was considered. Furthermore, the optimization problem was formulated as a class of problem in which minimization and maximization of the same performance criterion were simultaneously carried out. The algorithm proposed to solve the related zero-sum differential game was based on Newton’s method with recursive matrix relations, in which the first- and second-order derivatives of the objective function are calculated using hyper-dual numbers. The results of this paper were evaluated in simulation on a robot manipulator with three degrees of freedom.

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Adaptive fault-tolerant controller for morphing aircraft based on the L2 gain and a neural network

Cited by 15 publications

References 38 publications

TCN-Informer-Based Flight Trajectory Prediction for Aircraft in the Approach Phase

TCN-Informer-Based Flight Trajectory Prediction for Aircraft in the Approach Phase

L1 Adaptive Control Based on Dynamic Inversion for Morphing Aircraft

Min–Max Optimal Control of Robot Manipulators Affected by Sensor Faults

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