Nonlinear Fault-Tolerant Trajectory Tracking Control of a Quadrotor UAV

Emami, Seyyed Ali; Banazadeh, Afshin

doi:10.7763/ijmo.2019.v9.724

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…The panoramic image of the robot is stitched to obtain a larger field of view of the robot for global path planning [24]. The image stitch block matching process is divided into two steps, image registration and strategy search.…”

Section: Principles Of Image Matching and Image Stitchingmentioning

confidence: 99%

Research on Visual Image Stitching Method of Micro Flapping Wing Flying Robot Based on Block Matching

2022

Proceedings of WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications

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In order to solve the problem of limited field of view and the inability to carry out wide-field navigation planning during the visual navigation of the flapping-wing flying robot, and realizing the widefield navigation planning of the flapping-wing flying robot, this paper proposes a visual image stitching method for the flapping-wing flying robot based on block matching. Block matching is used to perform image registration on the visual image data of the flapping-wing flying robot, and the image fusion algorithm is used to improve the image clarity. While ensuring the large field of view perception and navigation of the flapping-wing flying robot, the features of the flapping-wing flying robot's visual target are extracted through block matching, which provides technical support for the flapping-wing flying robot's target tracking based on dynamic vision. An experimental verification system was built with a flapping-wing flying robot system and an airborne camera. The visual image stitching method of the flapping-wing flying robot based on block matching is experimentally verified, and a series of experimental results verify the feasibility and effectiveness of the method in this paper. The innovation of this paper is embodied in two aspects, the visual image registration method of flapping-wing flying robot based on block matching and the visual image fusion method of flapping-wing flying robot based on wavelet transform.

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Section: Principles Of Image Matching and Image Stitchingmentioning

confidence: 99%

Research on Visual Image Stitching Method of Micro Flapping Wing Flying Robot Based on Block Matching

2022

Proceedings of WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications

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“…The non‐linear dynamic model of a conventional quadrotor UAV (using the small angle approximation) can be formulated as follows (see Fig. 2) [29–31]:

{\ddot{x}}_{I} = - \frac{F}{m} ()(, \cos ψ \sin θ \cos ϕ + \sin ψ \sin ϕ) + {\bar{d}}_{1},

{\ddot{y}}_{I} = - \frac{F}{m} ()(, \sin ψ \sin θ \cos ϕ - \cos ψ \sin ϕ) + {\bar{d}}_{2},

{\ddot{z}}_{I} = - \frac{F}{m} ()(, \cos θ \cos ϕ) + g + {\bar{d}}_{3},

\ddot{ϕ} = \frac{1}{I_{x}} ()(, u_{ϕ} + false(I_{y} - I_{z} false) \dot{θ} \dot{ψ}) + {\bar{d}}_{4},

\ddot{θ} = \frac{1}{I_{y}} ()(, u_{θ} + false(I_{z} - I_{x} false) \dot{ϕ} \dot{ψ}) + {\bar{d}}_{5},

\ddot{ψ} = \frac{1}{I_{z}} ()(, u_{ψ} + false(I_{x} - I_{y} false) \dot{θ} \dot{ϕ})

…”

Section: Acceleration‐based Mpcmentioning

confidence: 99%

Fault‐tolerant predictive trajectory tracking of an air vehicle based on acceleration control

Emami

Banazadeh

2020

IET Control Theory & Applications

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“…It has a robust behavior in the presence of disturbances and uncertainties. [1][2][3][4][5] Furthermore, it has been recently employed in the case of constrained nonlinear systems. 6 While SMC is a simple approach to robust control, 7 implementing this method for designing controllers is more difficult.…”

Section: Introductionmentioning

confidence: 99%

Effective identification of sliding mode control parameters employing linear quadratic regulator and evolutionary computation techniques

2019

Self Cite

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In this paper, a new technique to reduce the search space in evolutionary algorithms for operative identification of sliding mode control parameters is proposed, especially in the case of a rigid satellite. The reduced domain provides a solution closer to the actual optimum at a lower computational cost. The optimized sliding mode controller provides superior performance that eliminates the need for trial‐and‐error exploration. In this approach, a linear quadratic regulator is initially designed for the equivalent linear system. In order to reduce the convergence time and computational cost, the regulator history is then used as a reference to provide optimal space for the sliding mode control parameters. Consequently, boundaries of the optimal space are fed to the genetic algorithm to optimize the control parameters. On the other hand, the particle swarm optimization uses the midpoint of the optimal space as an initial guess. The validation process is performed by tuning the sliding mode control parameters for a rigid satellite, taking into account uncertainties associated with the inertia matrix. The simulation results suggest the convergence to the near‐optimal solution, and the stability and robustness of the controller are enhanced by this technique.

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Nonlinear Fault-Tolerant Trajectory Tracking Control of a Quadrotor UAV

Cited by 4 publications

References 13 publications

Research on Visual Image Stitching Method of Micro Flapping Wing Flying Robot Based on Block Matching

Research on Visual Image Stitching Method of Micro Flapping Wing Flying Robot Based on Block Matching

Fault‐tolerant predictive trajectory tracking of an air vehicle based on acceleration control

Effective identification of sliding mode control parameters employing linear quadratic regulator and evolutionary computation techniques

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