Exact docking flight controller for autonomous aerial refueling with back-stepping based high order sliding mode

Su, Zikang; Wang, Honglun; Li, Na; Yu, Yue; Wu, Jianfa

doi:10.1016/j.ymssp.2017.08.036

Cited by 61 publications

(25 citation statements)

References 35 publications

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“…Consider a group of N identical UAVs in the communication network and define the set of N UAVs as Ω = {1, 2, …, N}. With regard to the ith UAV, i ∈ Ω, the corresponding force equations, attitude kinematic model, and attitude dynamic model are given by (1)-(3), respectively, which are presented as [42,43]…”

Section: Preliminaries and Problem Formulation 21 Uav Modelmentioning

confidence: 99%

“…Lemma 3: Let the design parameters η p , η v satisfy η p > 0, η v > C f m . If Assumptions 2, 3 hold, under the DSME (42) and (43), the follower UAVs can obtain the precise estimations of the position and velocity of the leader UAV in finite time T 2 , i.e. X i1d → X 01 , V i1d → Ẋ 01 in finite time, i ∈ Ω.…”

Section: Distributed Adaptive Foftcc Designmentioning

confidence: 99%

“…From the aforementioned analysis, it can be seen that X i1d and V i1d can be utilised to replace X 01 and Ẋ 01 of the leader UAV, respectively, when t ≥ T 2 . □ Then, on the basis of the estimated attitude reference (42) and velocity reference (43) for each follower UAV, the distributed adaptive FOFTCC scheme is designed.…”

Section: Distributed Adaptive Foftcc Designmentioning

confidence: 99%

See 2 more Smart Citations

Distributed adaptive fractional‐order fault‐tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks

Zhang

Liu

et al. 2019

IET Control Theory & Applications

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Section: Preliminaries and Problem Formulation 21 Uav Modelmentioning

confidence: 99%

Section: Distributed Adaptive Foftcc Designmentioning

confidence: 99%

Section: Distributed Adaptive Foftcc Designmentioning

confidence: 99%

See 1 more Smart Citation

Distributed adaptive fractional‐order fault‐tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks

Zhang

Liu

et al. 2019

IET Control Theory & Applications

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“…In the position loop, the proper reference path angle is determined by fuzzy logic. In [21], Z. Su et al utilize high order sliding mode observer (HOSMO) to achieve better estimation effects. Moreover, some modern control methods are also studied for their application in AAR control, such as: fault-tolerant control [22], [23] and terminal iterative learning control (TILC) [6], [24].…”

Section: Introductionmentioning

confidence: 99%

Docking Controller for Autonomous Aerial Refueling With Adaptive Dynamic Surface Control

Luo

2020

IEEE Access

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The docking controller for autonomous aerial refueling (AAR) is intractable considering the high precision requirement and the complex disturbances of multiple environment flows. To solve the problems in the docking phase of AAR, such as the uncertainties of the aerodynamic parameters of receiver aircraft and the disturbances acting on the receiver aircraft, an adaptive dynamic surface control (ADSC) scheme based on radical basis function neural network (RBF-NN) is presented in this paper. Firstly, a nonlinear model of longitudinal dynamics of the receiver aircraft relative to the tanker aircraft is established, which incorporates the tanker vortex term. Secondly, a nonlinear strict-feedback form is introduced to design an adaptive dynamic surface controller with RBF-NN. Thirdly, the upper bounds of the ''total disturbances'' are estimated with the adaptive law, and the uncertain aerodynamic parameters of receiver aircraft are estimated with RBF-NN. It is proved that the proposed controller can guarantee the uniform boundedness of all the signals in the closed-loop system using Lyapunov theory. Finally, simulation results demonstrate the effectiveness of the proposed controller for the docking control of AAR. INDEX TERMS Autonomous aerial refueling, docking controller, adaptive dynamic surface control, RBF neural network.

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“…As an effective method for increasing the endurance and range of aircraft by refueling them in flight [3], UAV autonomous aerial refueling (AAR) has attracted an increasing interest over the last decades from both theoretical and practical aspects covering aircraft control, sensor systems, and their integration [4][5][6][7][8]. Generally, there are two refueling methods [3]: flying boom method and probe-drogue refueling (PDR) [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Antidisturbance Vibration Suppression of the Aerial Refueling Hose during the Coupling Process

Wang

2017

International Journal of Aerospace Engineering

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In autonomous aerial refueling (AAR), the vibration of the flexible refueling hose caused by the receiver aircraft's excessive closure speed should be suppressed once it appears. This paper proposed an active control strategy based on the permanent magnet synchronous motor (PMSM) angular control for the timely and accurate vibration suppression of the flexible refueling hose. A nonsingular fast terminal sliding-mode (NFTSM) control scheme with adaptive extended state observer (AESO) is proposed for PMSM take-up system under multiple disturbances. The states and the "total disturbance" of the PMSM system are firstly reconstituted using the AESO under the uncertainties and measurement noise. Then, a faster sliding variable with tracking error exponential term is proposed together with a special designed reaching law to enhance the global convergence speed and precision of the controller. The proposed control scheme provides a more comprehensive solution to rapidly suppress the flexible refueling hose vibration in AAR. Compared to other methods, the scheme can suppress the flexible hose vibration more fleetly and accurately even when the system is exposed to multiple disturbances and measurement noise. Simulation results show that the proposed scheme is competitive in accuracy, global rapidity, and robustness.

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Exact docking flight controller for autonomous aerial refueling with back-stepping based high order sliding mode

Cited by 61 publications

References 35 publications

Distributed adaptive fractional‐order fault‐tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks

Distributed adaptive fractional‐order fault‐tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks

Docking Controller for Autonomous Aerial Refueling With Adaptive Dynamic Surface Control

Antidisturbance Vibration Suppression of the Aerial Refueling Hose during the Coupling Process

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