A multi-body control approach for flapping wing micro aerial vehicles

Khosravi, Mahdi; Novinzadeh, Alireza Basohbat

doi:10.1016/j.ast.2021.106525

Cited by 13 publications

(13 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The multi‐body dynamics model of FWMAV contains three rigid bodies: the FWMAV body, the left and right wings. The translational and rotational motion dynamics of FWMAV are expressed as Equations (4) and (5), respectively 33

\sum \limits_{i=1}^3\left[{m}_i\left({\dot{\mathbf{v}}}_i+{\ddot{\boldsymbol{\rho}}}_{ci}\right)\right]={\mathbf{F}}_{aero}+{\mathbf{F}}_g

{m}_2{\mathbf{r}}_2\times \left({\dot{\mathbf{v}}}_2+{\ddot{\boldsymbol{\rho}}}_{c2}\right)+{m}_3{\mathbf{r}}_3\times \left({\dot{\mathbf{v}}}_3+{\ddot{\boldsymbol{\rho}}}_{c3}\right)+\sum \limits_{i=1}^3\left[\left({\dot{\mathbf{H}}}_i+{m}_i{\boldsymbol{\rho}}_{ci}\times {\dot{\mathbf{v}}}_i\right)\right]={\mathbf{M}}_{aero}+{\mathbf{M}}_g

where

{\overset{bold-italicρ}{true}}_{c 2} = ...

…”

Section: Problem Formulationmentioning

confidence: 99%

Robust model‐free adaptive longitudinal flight control for a flapping wing micro air vehicle with wind disturbances

Yang

Hou

Jin

2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this paper, two robust model‐free adaptive control (RMFAC) methods are proposed for a nonlinear flapping wing micro air vehicle (FWMAV) subject to measurable and unmeasurable wind disturbances. Firstly, a novel disturbance‐related full‐form dynamic linearization technique (DFFDL) is developed to transform the FWMAV with measurable disturbances into a dynamic linearization data model. Then, a DFFDL based RMFAC method is designed based on the obtained data model for the FWMAV to suppress the wind disturbances. Next, the stability analysis shows that the system output tracking error converges into a bounded range. And the above result is extended to the case of unmeasurable disturbances. Finally, the simulation comparison results demonstrate the effectiveness of two proposed RMFAC methods.

show abstract

\sum \limits_{i=1}^3\left[{m}_i\left({\dot{\mathbf{v}}}_i+{\ddot{\boldsymbol{\rho}}}_{ci}\right)\right]={\mathbf{F}}_{aero}+{\mathbf{F}}_g

{m}_2{\mathbf{r}}_2\times \left({\dot{\mathbf{v}}}_2+{\ddot{\boldsymbol{\rho}}}_{c2}\right)+{m}_3{\mathbf{r}}_3\times \left({\dot{\mathbf{v}}}_3+{\ddot{\boldsymbol{\rho}}}_{c3}\right)+\sum \limits_{i=1}^3\left[\left({\dot{\mathbf{H}}}_i+{m}_i{\boldsymbol{\rho}}_{ci}\times {\dot{\mathbf{v}}}_i\right)\right]={\mathbf{M}}_{aero}+{\mathbf{M}}_g

where

{\overset{bold-italicρ}{true}}_{c 2} = ...

…”

Section: Problem Formulationmentioning

confidence: 99%

Robust model‐free adaptive longitudinal flight control for a flapping wing micro air vehicle with wind disturbances

Yang

Hou

Jin

2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…They are derived computationally or experimentally with various assumptions, ranging from time-invariant linear to nonlinear time-periodic models [58][59][60]. One of the main purposes of obtaining a dynamic model is to design a flight controller with various control techniques and schemes, such as PID [34,61], linear-quadratic-Gaussian [62], state feedback [63,64], nonlinear [65][66][67], sliding mode [68,69], robust [70,71], adaptive [72], adaptive neural network [73,74], and model-free [75]. While many dynamic models and controllers have been successfully developed, only a limited number have been implemented on tailless FWMAVs because some tailless FWMAVs cannot generate sufficient lift to perform free flight for implementation and verification.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal Mode System Identification of an Insect-like Tailless Flapping-Wing Micro Air Vehicle Using Onboard Sensors

Aurecianus

Park

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

In this paper, model parameter identification results are presented for a longitudinal mode dynamic model of an insect-like tailless flapping-wing micro air vehicle (FWMAV) using angle and angular rate data from onboard sensors only. A gray box model approach with indirect method was utilized with adaptive Gauss–Newton, Levenberg–Marquardt, and gradient search identification methods. Regular and low-frequency reference commands were mainly used for identification since they gave higher fit percentages than irregular and high-frequency reference commands. Dynamic parameters obtained using three identification methods with two different datasets were similar to each other, indicating that the obtained dynamic model was sufficiently reliable. Most of the identified dynamic model parameters had similar values to the computationally obtained ones, except stability derivatives for pitching moment with forward velocity and pitching rate variations. Differences were mainly due to certain neglected body, nonlinear dynamics, and the shift of the center of gravity. Fit percentage of the identified dynamic model (~49%) was more than two-fold higher than that of the computationally obtained one (~22%). Frequency domain analysis showed that the identified model was much different from that of the computationally obtained one in the frequency range of 0.3 rad/s to 5 rad/s, which affected transient responses. Both dynamic models showed that the phase margin was very low, and that it should be increased by a feedback controller to have a robustly stable system. The stable dominant pole of the identified model had a higher magnitude which resulted in faster responses. The identified dynamic model exhibited much closer responses to experimental flight data in pitching motion than the computationally obtained dynamic model, demonstrating that the identified dynamic model could be used for the design of more effective pitch angle-stabilizing controllers.

show abstract

“…Micro Aerial Vehicles (MAV) [1]- [6] have attracted much attention because of their special applications. An important application is military reconnaissance, which can be equipped to soldiers' squads for enemy reconnaissance and surveillance, monitoring chemical, nuclear or biological weapons, and reconnoitre inside buildings.…”

Section: Introductionmentioning

confidence: 99%

The Application of Micro Coaxial Rotorcraft in Warfare: An Overview, Key Technologies, and Warfare Scenarios

Wang¹,

Liu²,

Cui³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

The mode of future wars is mainly local wars and regional conflicts. This style of war is characterized by unpredictability and instability, which can happen anytime and anywhere. Once it happens, the battlefield is vast, short-range and fast-changing. In order to adapt to the characteristics of this kind of warfare, simple equipment and short reaction time have become two important factors in selecting weapons. Coaxial rotorcraft has been widely studied and applied in future wars due to its convenient carrying, strong environmental adaptability, and rich load capacity. This paper conducts an overview of the Coaxial rotorcraft. Furthermore, we also identify its key technologies, warfare scenarios and future directions. We anticipate that this survey can shed new light on the coaxial rotorcraft systems. Micro aerial vehicles, coaxial, rotorcraft, warfare, application. INDEX TERMS

show abstract

A multi-body control approach for flapping wing micro aerial vehicles

Cited by 13 publications

References 56 publications

Robust model‐free adaptive longitudinal flight control for a flapping wing micro air vehicle with wind disturbances

Robust model‐free adaptive longitudinal flight control for a flapping wing micro air vehicle with wind disturbances

Longitudinal Mode System Identification of an Insect-like Tailless Flapping-Wing Micro Air Vehicle Using Onboard Sensors

The Application of Micro Coaxial Rotorcraft in Warfare: An Overview, Key Technologies, and Warfare Scenarios

Contact Info

Product

Resources

About