# Control Methods Comparison for the Real Quadrotor on an Innovative Test Stand
## 1 Introduction  
In the last decades interest in unmanned aerial vehicles (UAVs) has increased. These vehicles consist of various flying platforms such as airships, fixed-wing or vertical take-off and landing (VTOL) vehicles. This article is focused on a quadrotor, which belongs to VTOL UAVs. The main advantage of VTOL UAVs is the ability to hover allowing them to operate in a small and cluttered environment [1]. Comparing a multirotor VTOL platform to a helicopter, several advantages can be identified, such as a greater trust-weight ratio and better maneuverability. The variable number of rotors provide the possibility to use smaller propellers instead of large ones to produce a particular thrust. This leads to less structural and dynamical problems and, in the case of an accident, the resulting injury is usually less heavy [2; 3; 4]. Moreover, the formation of various quadrotors can perform a complex task such as the transport of items heavier than the maximum permissible load weight of one quadrotor. Multirotor platforms are usually controlled by changing an angular speed of rotors, so there is no need of a swashplate. This simplifies not only the mechanics but also the maintenance of the system [2; 3; 5]. Multirotor platforms can also continue in flight after occurrence of an actuator failure as long as they are equipped with a failsafe controller. Although the failsafe controller is more straightforward to design for multirotor drones with six or eight rotors, some controllers were also designed to manage an actuator failure of quadrotor. Examples of failsafe controllers for a quadrotor can be found in [6; 7; 8]. Multirotor platforms have become very popular and their usage has spread over all fields. Some requested tasks can be complicated, and they can require the control algorithms, which are faster, more efficient and reliable also under windy, uncertain and changing conditions. A disturbance observer (DO) is usually designed to compensate such uncertainties. The quadrotor is coupled and underactuated system (i.e., six degrees of freedom are controlled only by four actuators). The translational motion of the quadrotor depends on the attitude of the quadrotor as can be seen from the equations in [9]. By this means, the quadrotor is a cascade system [10]. Therefore, a cascade structure of a controller is usually applied to control position and attitude of the quadrotor. Various controllers based on classic or modern control theory were already designed and verified. The non-linear or linear model of a quadrotor is used depending on the chosen control method [11]. The linear model of a quadrotor is achieved by linearization of the non-linear model around a hovering point. Controllers using the linearized model generally perform well around this hovering point. When the quadrotor goes away from the linearized point, the performance may worsen. Furthermore, the input saturation can cause control failure when large rapid maneuvers are required [3; 5; 12; 13]. The main advantages of linear controllers are simplicity and ease of implementation to a real platform. The disadvantage of this approach is the use of the linearized model of the quadrotor during the process of designing a controller. Commonly used linear controllers are a linear quadratic regulator (LQR) and a proportional derivative (PD) controller [14; 15; 16]. Another group of controllers consists of a wide variety of non-linear controllers. The serious disadvantage of these controllers is the complexity that prevents the wide adoption of non-linear controllers in real applications. Among non-linear methods, the backstepping control technique based on the Lyapunov function is widely adopted due to its systematic design and an intuitive approach [17]. The proposed control law is based on the compensation of non-linear forces or torques depending on whether an attitude or position controller is being designed. Applying Lyapunov stability analysis proves that the closed-loop system is asymptotically stable.