Integrated Guidance and Control for Autonomous Rendezvous of Unmanned Aerial Vehicle During Aerial Refueling

Zhao, Wenbi; Duan, You; Yu, Ziquan; Qu, Yaohong; Zhang, Youmin

doi:10.1109/icuas51884.2021.9476886

Cited by 4 publications

(7 citation statements)

References 11 publications

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“…where 𝜅 and 𝜒 are two positive adjustable constants which determine the update speed, 𝛼 min denotes the positive lower bound of 𝛼(t). From (30) and Figure 1, one can find that the value of 𝛼(t) can be reduced which makes the open loop gain increase. When the tracking error goes to zero, 𝛼(t) tends to be stable.…”

Section: 𝜶-Variable Model-free Prescribed-time Controller (𝜶(T)-mfptc...mentioning

confidence: 97%

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An α‐variable model‐free prescribed‐time control for nonlinear system with uncertainties and disturbances

Wang

Tian

2022

Intl J Robust & Nonlinear

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In this article, an 𝛼-variable model-free prescribed-time controller (𝛼(t)-MFPTC) is proposed for a nonlinear system with uncertainties and disturbances. First, an ultra-local model is employed to formulate the plant dynamic by using input and output data. Second, to observe the state variables and compensate for the lumped uncertainties, a linear extended state observer (LESO) is designed.Then, a corresponding LESO-based 𝛼-fixed model-free controller (LESO-iPD) is proposed. Third, based on LESO-iPD, a prescribed-time sub-controller (PTC) is adopted to converge tracking error within a prescribed finite time. Furthermore, an adaptive RBF neural network compensator is constructed to approximate and compensate for LESO error. Correspondingly, an 𝛼-fixed model-free prescribed-time controller (𝛼-MFPTC) is proposed. Fourth, based on 𝛼-MFPTC, a tracking error-based 𝛼-variable method is applied to improve the controller performance, and an 𝛼-variable model-free prescribed-time controller (𝛼(t)-MFPTC) is subsequently proposed. Moreover, stability and prescribed-time convergence of closed-loop system with 𝛼(t)-MFPTC are analyzed by using the Lyapunov theorem. Ultimately, to demonstrate the performance and effectiveness of the proposed control strategy, the numerical simulation with sliding mode control, LESO-iPD, 𝛼-MFPTC, and 𝛼(t)-MFPTC and co-simulation results on quadrotor have been obtained.

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Section: 𝜶-Variable Model-free Prescribed-time Controller (𝜶(T)-mfptc...mentioning

confidence: 97%

“…27 In the aerospace field, Ding et al developed a piecewise polynomial function to complete the objective of prescribed-time control. 28 In the real application, the prescribed time control method can be used in missile guidance, 29 autonomous vehicle rendezvous 30 and so forth.…”

Section: Introductionmentioning

confidence: 99%

An α‐variable model‐free prescribed‐time control for nonlinear system with uncertainties and disturbances

Wang

Tian

2022

Intl J Robust & Nonlinear

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“…, where T  and T  are the elevation angle and the azimuth angle of the virtual tanker, respectively. Taking the derivative of equation (10) and combining equation ( 8) and equation ( 9) give…”

Section: Design Of Nonlinear Rendezvous Guidance Lawmentioning

confidence: 99%

“…Proof. The Lyapunov function candidate is chosen as (10), and its derivative is expressed as equation (11). Substituting equation (12) and equation (13) into equation (11), we can get ( ) cos ( )…”

Section: Design Of Nonlinear Rendezvous Guidance Lawmentioning

confidence: 99%

“…Yuan et al [9] studied the control strategy of a UAV in the rendezvous process, and applied the Dubins path to produce the rendezvous trajectory. Zhao et al [10] analyzed the relative relationship between a virtual tanker and a UAV, and used the fast terminal sliding mode control strategy to design the UAV's guidance law and flight control law.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear rendezvous guidance law design for a receiver UAV based on the Lyapunov stability theory

Zhao¹,

Zhao

Xu³

2022

J. Phys.: Conf. Ser.

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The rendezvous problem in the autonomous aerial refueling (AAR) was researched for the receiver unmanned aerial vehicle (UAV). The three-dimensional guidance model was constructed for the UAV to pursue the virtual tanker. In order to guarantee the zero miss distance and the intercept angle constraints, a nonlinear rendezvous guidance law was designed for the UAV based on the Lyapunov stability theory. The guidance law generated the normal acceleration command and the lateral acceleration command. The velocity control system was designed by the sliding mode control method. It controled the UAV’s velocity to equal to that of the virtual tanker at the end of the rendezvous phase. The simulation results validate the effectiveness of the designed nonlinear guidance law and the velocity control system.

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Review of Genetic Algorithms Applied to Agriculture

Barzola,

Baños,

Baños

et al. 2024

2024 International Conference on Electrical, Computer and Energy Technologies (ICECET

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Integrated Guidance and Control for Autonomous Rendezvous of Unmanned Aerial Vehicle During Aerial Refueling

Cited by 4 publications

References 11 publications

An α‐variable model‐free prescribed‐time control for nonlinear system with uncertainties and disturbances

An α‐variable model‐free prescribed‐time control for nonlinear system with uncertainties and disturbances

Nonlinear rendezvous guidance law design for a receiver UAV based on the Lyapunov stability theory

Review of Genetic Algorithms Applied to Agriculture

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