RotorSuite: A MATLAB/Simulink Toolbox for Tilt Multi-Rotor UAV Modeling

Nicola Cigarini; Giulia Michieletto; Angelo Cenedese

cs.ROFeb 21, 2026

RotorSuite: A MATLAB/Simulink Toolbox for Tilt Multi-Rotor UAV Modeling

Authors: Nicola Cigarini, Giulia Michieletto, Angelo Cenedese

Organizations: Department of Information Engineering University of Padova, Vicenza, Italy · Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy · Department of Management and Engineering, University of Padova, Padova, Italy · Department of Industrial Engineering, University of Padova, Padova, Italy

Abstract

In recent years, aerial platforms have evolved from passive flying sensors into versatile, contact-aware robotic systems, leading to rapid advances in platform design. Standard coplanar and collinear quadrotors have been complemented by modern tilted and tilting multi-rotor platforms with enhanced maneuverability. To properly analyze, control, and validate the performance of these emerging platforms, an accurate modeling step is required; however, this can be time-consuming, user-dependent and error-prone. To address this issue, we propose a MATLAB/Simulink toolbox for modeling and simulating the dynamics of a broad class of multi-rotor platforms through both an analytical and physics-based approaches. The toolbox, named RotorSuite, is provided with comprehensive documentation and example use cases, representing a valuable tool for didactic, research, and industrial development purposes.

Figures & tables

position			orientation					number
coplanar(C)	OR	not coplanar (nC)	zero-tilt (-)	OR	not zero-tilt (T)			$n$ -rotor ( $n$ R)
	AND				tilted (Ted)	OR	tilting (Ting)
star-shaped (S)	OR	not star-shaped (nS)			cant ( $\alpha_{\bullet}$ )	AND/OR	dihedral ( $\beta_{\bullet})$
					interdependent ( $\bullet=0$ )	OR	independent ( $\bullet=i)$

TABLE I : Summary of possible modeling multi-rotor platforms in terms of propeller specifications.

Fig. 1 :

i

-th propeller position and orientation with respect to the body frame described by parameters

(\ell,\alpha_{i},\beta_{i},\gamma_{i},\delta_{i})

Panel of Figure 3 — Fig. 3 : Use case physics-based platform model.

Fig. 4 : Use case physics-based simulation setup.

Panel of Figure 5 — Fig. 5 : Snapshots of the platform executing the reference maneuver in the physics-based simulation.

\begin{aligned} K_{p}&=\text{diag}\left(\scalebox{.9}{

\begin{bmatrix}7;7;60\end{bmatrix}

}\right)\\ K_{p_{i}}&=\text{diag}\left(\scalebox{.9}{

\begin{bmatrix}3;3;5\end{bmatrix}

}\right)\\ K_{v}&=\text{diag}\left(\scalebox{.9}{

\begin{bmatrix}14;14;20\end{bmatrix}

}\right)\end{aligned}

\begin{aligned} K_{R}&=\text{diag}\left(\scalebox{.9}{

\begin{bmatrix}90;90;90\end{bmatrix}

}\right)\\ K_{R_{i}}&=\text{diag}\left(\scalebox{.9}{

\begin{bmatrix}25;25;10\end{bmatrix}

}\right)\\ K_{\omega}&=\text{diag}\left(\scalebox{.9}{

\begin{bmatrix}25;25;5\end{bmatrix}

}\right)\end{aligned}

TABLE III : Controller Gains

Panel of Figure 7 — Fig. 6 : Analytical VS. physics-based simulation tracking performance: 3D position trajectory.

	$\diamond=a$	$\diamond=p$	difference
$\mathbf{e}_{p,1}^{\diamond}$ [m]	1.7216e-03	1.7089e-03	1.2671e-05
$\mathbf{e}_{p,2}^{\diamond}$ [m]	3.4434e-03	3.4200e-03	2.3341e-05
$\mathbf{e}_{p,3}^{\diamond}$ [m]	1.5840e-05	5.667e-05	4.0830e-05
$\mathbf{e}_{R,1}^{\diamond}$ [rad]	8.5025e-18	3.3220e-04	3.3220e-04
$\mathbf{e}_{R,2}^{\diamond}$ [rad]	7.0063e-18	1.0891e-04	1.0891e-04
$\mathbf{e}_{R,3}^{\diamond}$ [rad]	7.1449e-18	1.6628e-07	1.6228e-07

TABLE IV : Mean Absolute Tracking Errors

Fig. 8 : Analytical VS. physics-based simulation tracking performance: propellers spinning rate difference

\Delta\omega_{i}

Aerial Robotics

Aerial robotics studies the design, perception, control, and coordination of flying robots for navigation, inspection, transport, and physical interaction.

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