## 2 Materials and Methods
### Technical Novelty and State of the Art in VTOL UAV Control Systems  
The current state of the art in drone control systems, especially for vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs), is rapidly being regulated. VTOL aircraft pose additional challenges to control systems because they are capable of operating in a wide range of environments and require switching between position hold and high-speed flight modes. Traditional drone control systems are often split into these modes first, with one set optimized for airplane flight and the other for fixed-wing flight. However, this separation can increase complexity, cost and transition time. For example, the work of [PERSON], already mentioned above, also separates hovering and forward flight. It also uses a nonlinear control strategy, while hovering using a feedback-linearizable input for altitude control and a hierarchical control, as well as a backstepping control law, for the vehicle in forward flight mode. [PERSON] in [5] also compare the use of various controllers of various types for tilted-rotor drones. In his work, he compares the use of PID controllers, Eigenvalue assignment, optimum control, robust control, and model predictive control (MPC). Ultimately, for its experimental TRUAV platform, [PERSON] develops a system using a proportional derivative-double derivative (PD-dD) controller in combination with an MPC controller. At the same time, [PERSON] notes significant problems in modeling the behavior of tilting rotors. In the case of working with FIXAR, there is no such problem due to the absence of this node. This simplifies the model and provides higher system reliability. The research of [PERSON] [6] provides classification and models of the VTOL drones, as well as features of their modeling. The author compares the use of different control systems for hybrid drones, focusing on linear quadratic regulator (LQR) controller, backstepping and gain-scheduling methods. [PERSON] also mentions the nonlinear dynamic inversion (NDI) method as a solution that can be generalized for the MIMO solution. The research presented in this paper introduces a new VTOL UAV control system specifically suited to the FIXAR 007 drone. This drone is a hybrid between a quadcopter \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **Description** & **Unit** & **Value** \\ \hline \(\mathbf{D_{w}}\) & Wingspan & m & 1.6 \\ \hline \(\mathbf{m_{max}}\) & Maximum takeoff weight & kg & 7 \\ \hline \(\mathbf{P_{d}}\) & Propeller diameter & in & 10 \\ \hline \(\mathbf{P_{p}}\) & Propeller pitch & in & 4.5 \\ \hline \(\mathbf{K_{av}}\) & Linear coefficient transferring from throttle command (range [0:1] & rad/s/1V & 109.9 \\  & to motor rate for specified voltage) & & \\ \hline \(\mathbf{K_{fm}}\) & Linear coefficient transferring from motor rate to motor thrust & N/(rad/s) & 0.0181 \\ \hline \(\mathbf{K_{r}}\) & Motor rate-to-yawing torque coefficient & N+m/(rad/s) & 5\(\ast 10^{-6}\) \\ \hline \(\mathbf{J_{m}}\) & Motor inertia & Kg*m\({}^{2}\) & 0.005 \\ \hline \(\mathbf{T_{s}}\) & Motor time constant & s & 0.01 \\ \hline \(\mathbf{V}\) & Battery voltage (constant) & volts & 23 \\ \hline \(\mathbf{\Phi_{1}}=\mathbf{\Phi_{2}}=\mathbf{\Phi_{3}}=\mathbf{\Phi_{4}}\) & Motor inclination angle & degree & 45 \\ \hline \(\mathbf{r_{lx}}=\mathbf{r_{lx}}=\mathbf{r_{lx}}=\mathbf{r_{lx}}=\mathbf{r_{lx}}\) & Motor distance on \(x\) axis & m & 0.45 \\ \hline \(\mathbf{r_{ly}}=\mathbf{r_{zy}}=\mathbf{r_{xy}}=\mathbf{r_{dy}}\) & Motor distance on \(y\) axis & m & 0.25 \\ \hline \hline \end{tabular} Where \(\mathbf{T_{s}}\) is the time constant of the motor aperiodic link, describing the motor dynamics, and \(\mathbf{K_{av}}\) is the coefficient transferring motor command to the motor rate \(\omega\).  
\end{table} 
Table 1: Model parameters. and an airplane. It both uses motor controls and also deals with greater aerodynamic disturbances.