2016
DOI: 10.1007/s11071-016-2666-8
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Adaptive attitude and position control of an insect-like flapping wing air vehicle

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Cited by 56 publications
(21 citation statements)
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“…Both the position and attitude tracking errors are successfully kept within acceptable bounds even under the interference of random noises. Although the FMAV model in [13] is not identical with the one studied in this paper, it can be concluded from [23] that the control strategy of [13] degrades quickly due to the chattering problem when the attitude error is relatively large. Input constrained tracking control of FMAV was also discussed in [25], compared with their method, our proposed control strategy yields smoother tracking trajectories, which means the proposed control law is more robust and suitable for practical applications.…”
Section: Simulation Results and Analysismentioning
confidence: 99%
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“…Both the position and attitude tracking errors are successfully kept within acceptable bounds even under the interference of random noises. Although the FMAV model in [13] is not identical with the one studied in this paper, it can be concluded from [23] that the control strategy of [13] degrades quickly due to the chattering problem when the attitude error is relatively large. Input constrained tracking control of FMAV was also discussed in [25], compared with their method, our proposed control strategy yields smoother tracking trajectories, which means the proposed control law is more robust and suitable for practical applications.…”
Section: Simulation Results and Analysismentioning
confidence: 99%
“…Lin et al use a modified P‐control to stabilise the altitude of their Golden Snitch [12]. Banazadeh et al design an adaptive sliding mode technique for the attitude and position control of a rigid body, insect‐like flapping wing model in the presence of uncertainties [13]. An adaptive tracking controller is proposed in [14] for the Harvard fly.…”
Section: Introductionmentioning
confidence: 99%
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“…Actuation force qe and damping force qa represent the fringing electrostatic force and the aerodynamic force per unit length respectively. The distributed aerodynamic force qa is [29,30] |qa|=0.5ρawbca(u/t)2, in which the direction of the aerodynamic force is opposite to the direction of the velocity; ca is the drag coefficient; ρa is the density of the air.…”
Section: Problem Formulation and Dynamic Modelingmentioning
confidence: 99%
“…An observer was introduced to estimate angular velocity and external disturbances to accomplish robust tracking control in [18]. Banazadeh and Taymourtash [19] introduced adaptive sliding mode and neural networks to achieve attitude and position control for FWMAVs with parametric uncertainties. Dierks and Jagannathan [20] introduced the neural networks based observer to estimate translational and angular velocities, and designed an output feedback control scheme.…”
Section: Introductionmentioning
confidence: 99%