High-precision prescribed-time path following for quadrotor

Ding, Yibo; Wang, Xiaogang; Bai, Yuliang; Cui, Naigang

doi:10.1177/1729881420920058

Cited by 4 publications

(9 citation statements)

References 26 publications

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“…Remark In most previous papers, 38,42,43,45‐50

α (t)

is considered a positive constant. Besides, most systems in practice are one or two order nonlinear system such as unmanned aerial vehicle, 28 robotic manipulator, 48 suspension system, 49 and so on. For some high‐order systems, one can use order reduction techniques such as the method of the dominant pole to decrease their orders.…”

Section: Problem Formulationmentioning

confidence: 99%

“…With the completion of LESO (5), the prescribed-time sub-controller (25) and adaptive RBF neural network compensator (28), the 𝛼-MFPTC is proposed as follows:…”

Section: Adaptive Rbf Neural Network Compensator Designmentioning

confidence: 99%

“…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%

“…Besides, the prescribed time control strategies were applied to the control of multi‐agents, 24,25 linearized Schrödinger equation, 26 and cluster synchronization 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%

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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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“…Remark In most previous papers, 38,42,43,45‐50

α (t)

Section: Problem Formulationmentioning

confidence: 99%

“…With the completion of LESO (5), the prescribed-time sub-controller (25) and adaptive RBF neural network compensator (28), the 𝛼-MFPTC is proposed as follows:…”

Section: Adaptive Rbf Neural Network Compensator Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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 order to provide all aforementioned flight missions with necessarily planned reference trajectories, it is convenient to choose Dubins trajectories [5], [6], [16] for flight mission implementation. They are universal for the variety of flight missions, and segments of straight lines and arcs of circles define them.…”

Section: Planning Trajectories Of Quadrotorsmentioning

confidence: 99%

On Features of Planning Trajectories for Quadrotors

Tunik

Larin²,

Sushchenko³

et al. 2021

ECS

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The paper deals with studying the process of planning trajectories during the quadrotor flight. The typical trajectories of the drone flights have been analyzed. Block diagram of the control system in the nonholonomic case and the horizontal flight is given. Comparative analysis of the most widespread trajectories is represented. The possibility to use two types of Dubins trajectory is analysed. Introducing polar coordinates for forming flight trajectories is proposed. The grounded choice of the quadrotor trajectories for different cases of the holonomic and nonholonomic closed-loop control systems was proposed. The advantages and disadvantages of the trajectories planning in each of these cases were analyzed. The Simulink models for generators of the quadrotor trajectories have been developed. The simulation results of generations of these trajectories have been represented. The possibilities of MATLAB for simulating flight trajectories are shown. The obtained results can be applied for unmanned aerial vehicles of different types.

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