Abstract
Flapping flight dynamics is quite an intricate problem that is typically represented by a multi-body, multi-scale, nonlinear, time-varying dynamical system. The unduly simple modeling and analysis of such dynamics in the literature has long obstructed the discovery of some of the fascinating mechanisms that these flapping-wing creatures possess. Neglecting the wing inertial effects and directly averaging the dynamics over the flapping cycle are two major simplifying assumptions that have been extensively used in the literature of flapping flight balance and stability analysis. By relaxing these assumptions and formulating the multi-body dynamics of flapping-wing micro-air-vehicles in a differential-geometric-control framework, we reveal a vibrational stabilization mechanism that greatly contributes to the body pitch stabilization. The discovered vibrational stabilization mechanism is induced by the interaction between the fast oscillatory aerodynamic loads on the wings and the relatively slow body motion. This stabilization mechanism provides an artificial stiffness (i.e., spring action) to the body rotation around its pitch axis. Such a spring action is similar to that of Kapitsa pendulum where the unstable inverted pendulum is stabilized through applying fast-enough periodic forcing. Such a phenomenon cannot be captured using the overly simplified modeling and analysis of flapping flight dynamics.
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The authors gratefully acknowledge the support of the National Science Foundation Grant CMMI-1709746.
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Appendices
Appendix
Derivation of the Five-DOF Equations of Motion
We use the principle of virtual power (Greenwood 2003), as explained in Sect. 2, to derive the five-DOF equations of motion (2) in detail. The various terms in Eq. (1) for the body and wing are given below.
1.1 Body
The linear velocity of the reference point of the body axis system (the body’s center of gravity) and the corresponding angular velocity are written as
where \({\varvec{i}}\), \({\varvec{j}}\), and \({\varvec{k}}\) are unit vectors along the x, y, and z directions in the axis system indicated by the subscript. Thus, one obtains
and
The angular momentum vector of the body about its center of gravity and its inertial derivative are given by
The aerodynamic contribution of the body is neglected, and hence, the body exhibits gravitational forces only with no moments. Thus, the body force in the inertial frame is written as
1.2 Wing
The linear velocity of the reference point of the wing frame (the hinge root) and its angular velocity are written as
Thus, one obtains
and
The rotation matrix from the inertial frame to the stroke plane frame is given by
and rotation matrices from the stroke plane frame to the wing frame are
and
The wing angular velocity vector in the wing frame is
The position vector pointing from the hinge root to the wing center of gravity is \({{\varvec{\rho _c}}}_{\mathrm{w}}=-{\hat{d}}{\varvec{i}}_{\mathrm{w}} + r_{\mathrm{cg}} {\varvec{j}}_{\mathrm{w}}\) where \({\hat{d}}\) and \(r_{\mathrm{cg}}\) are the distances between the wing root hinge point and the wing center of gravity along the negative \(x_{\mathrm{w}}\)-axis and the \(y_{\mathrm{w}}\)-axis, respectively. Thus, the inertial acceleration is obtained as
Assuming the wing reference frame is fixed in the wing principal axes, the inertial time derivative of the angular momentum vector represented in the wing frame is written as
The wing is subject to aerodynamic and gravitational forces. Noting that the \(y_b\)-components of the aerodynamic force on each wing are equal and opposite, the force vector applied on the wing is written as
where \(F_x\) and \(F_z\) are the aerodynamic loads along the \(x_{\mathrm{w}}\) and \(z_{\mathrm{w}}\) directions, respectively. The moment vector comprises three contributions: aerodynamic, gravitational, and the control torque. The aerodynamic contribution \({\varvec{M}}_{\mathrm{a}_{\mathrm{w}}}\) is determined by integrating the radial distributions of the forces \(F_x\) and \(F_z\) over the wing. That is, \({\varvec{M}}_{\mathrm{a}_{\mathrm{w}}}=M_x {\varvec{i}}_{\mathrm{w}}+M_y {\varvec{j}}_{\mathrm{w}}+M_z {\varvec{k}}_{\mathrm{w}}\), where
where \(F_x'(r)\) and \(F_z'(r)\) are the two-dimensional aerodynamic loads on an airfoil that is at distance r from the wing root and \(d_{\mathrm{ac}}(r)\) is the distance between the hinge line and the quarter chord line (aerodynamic center) at each airfoil section along \(x_{\mathrm{w}}\) direction. The gravitational contribution is written as \( {\varvec{M}}_{\mathrm{g}_{\mathrm{w}}}= (-{\hat{d}}{\varvec{i}}_{\mathrm{w}}+ r_{\mathrm{cg}}{\varvec{j}}_{\mathrm{w}}) \times m_{\mathrm{w}} g{\varvec{k_{\mathrm{I}}}}\). The last contribution (the control torque) is written as \({\varvec{M}}_{\mathrm{c}_{\mathrm{w}}}= -\tau _\varphi {\varvec{k}}_{\mathrm{s}}+\tau _\eta {\varvec{j}}_{\mathrm{w}}\), where \(\tau _\varphi \) and \(\tau _\eta \) are the actuating torque along the flapping and pitching directions, respectively.
Constructing all the required terms to apply the principle of virtual power (1), the five-DOF equations of motion are obtained as (with obvious correspondence to the abstract form (2))
where \(m_{\mathrm{v}}=m_{\mathrm{b}}+m_{\mathrm{w}}\).
Aerodynamic Model
The aerodynamic derivatives in Eq. (7) are defined below
where \(k=c_r (1-x_{or})\), \(c_r\) is the wing root chord, \(x_{or}\) is the position of the hinge point along \(x_w\) normalized by the root chord, and \(x_{\mathrm{h}}\) is the distance from the vehicle center of mass to the root of the wing hinge line (i.e., the intersection of the hinge line with the \(x_b\)-axis). Also, \(\rho \) is the air density, \(C_{L_\alpha }\) is the three-dimensional lift curve slope of the wing, c(r) is the spanwise chord distribution, R is the wing radius, and \(I_{mn}=2\int _0^R {r^m c^n(r) \,\hbox {d}r}\).
The Linearized Dynamics of the Averaged Three-DOF System
The linearized averaged version of the three-DOF system (8) at the trim condition can be written abstractly as
where the elements of the matrix \({\varvec{A}}\) can be written as (some elements have quite lengthy expressions, and hence, we only write their limits as the wing mass goes to zero):
Hawkmoth Morphological Parameters
The morphological parameters and the wing planform for the hawkmoth, as given in Sun et al. (2007) and Ellington (1984b), are
where R is the semi-span of the wing, S is the area of one wing, \({\overline{c}}\) is the mean chord, f is the flapping frequency, \(\Phi \) is the flapping angle amplitude, \(m_b\) is the mass of the body, and \(I_{yb}\) is the body moment of inertia around the body y-axis. The moments of the wing chord distribution \({\hat{r}}_1\) and \({\hat{r}}_2\) are defined as
As for the wing planform, the method of moments used by Ellington Ellington (1984b) is adopted here to obtain a chord distribution for the insect that matches the documented first two moments \({\hat{r}}_1\) and \({\hat{r}}_2\); that is,
where
The mass of one wing is taken as 5.7% of the body mass according to Wu et al. (2009) and is assumed uniform with an areal mass distribution \(m'\) The inertial properties of the wing are then estimated as
where \({\hat{d}}\) is the chord-normalized distance from the wing hinge line to the center of gravity line.
Optimized Shooting Method
Periodic shooting methods have been used in the literature of FWMAVs/insects to capture the periodic orbits associated with different equilibrium configurations (e.g., hovering) (Dietl and Garcia 2008b; Wu and Sun 2012; Stanford et al. 2013; Hussein et al. 2018). The stability of these orbits are then analyzed using the Floquet theorem (Nayfeh and Balachandran 1995). Dednam and Botha (2015) provided an optimized shooting approach to capture a periodic solution of a nonlinear system. This optimized shooting approach adopts the Levenberg–Marquardt optimization algorithm to minimize the residual. This algorithm is based on two methods: the gradient descent method and the Gauss–Newton method. According to Gavin (2011), when the parameters are far from the optimal values, the Levenberg–Marquardt algorithm operates in a way similar to gradient descent. However, it operates similar to the Gauss–Newton method when approaching the optimal point.
Consider the following system of equations
where \({\varvec{x}}\)\(\in \)\(\mathbb {\mathbb {R}}^{n}\) and \({\varvec{f}}\): \(\mathbb {\mathbb {R}}^{n} \times \mathbb {\mathbb {R}}^{k} \times \mathbb {\mathbb {R}}_{\ge 0}\)\(\rightarrow \)\(\mathbb {\mathbb {R}}^{n}\), and \({\varvec{\alpha }}\) are the system parameters. This system corresponds to a non-autonomous vector field. Thus, a solution \({\varvec{x}}(t)\) to the system (38) is periodic if there exists a constant \(T>0\) such that
The optimized shooting method can be applied to any system that can be expressed in the form of (38), and, for convenience, a dimensionless time \(\tau \) is introduced such that \(t =\tau ~T\). Equation (38) is then written as
Thus, this new variable \(\tau \) allows the simplification of the boundary conditions in Eq. (39) so that \(x(\tau = 0)= x(\tau = 1)\) and Eq. (40) can be integrated over one period (i.e., letting \(\tau \) run from zero to one). Now, the residual can be written as
According to Dednam and Botha (2015), the residual depends on the number of quantities to be optimized and can be expressed as
where \(\varDelta \tau \) is the integration step size and \(p \in {\mathbb {N}}\). For solvability, the natural number p is chosen so that the number pn of components of the residual is greater than or equal to the number of unknowns (initial point on the periodic orbit and any unknown parameters such as the period in autonomous systems).
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Hassan, A.M., Taha, H.E. Differential-Geometric-Control Formulation of Flapping Flight Multi-body Dynamics. J Nonlinear Sci 29, 1379–1417 (2019). https://doi.org/10.1007/s00332-018-9520-8
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DOI: https://doi.org/10.1007/s00332-018-9520-8
Keywords
- Differential-geometric-control
- Flapping flight
- Time-periodic systems
- Vibrational stabilization
- Multi-body dynamic