# Control and Position Tracking for UAVs
## 2 An Overview of the Published Articles
To verify the effectiveness of the novel APPC (Adaptive Prescribed Performance Control), numerical simulations were conducted, including scenarios of external disturbances responsible for inducing chattering in the desired control input, as well as comparison of the results to standard PID control loops and the P-PPC scheme (hybrid Proportional and Prescribed Performance Control). The proposed robust adaptive control scheme deals with the conflicting nature between the output specifications, inputs, and state constraints of the system, which is crucial for guaranteeing the safety and the stability of UAV navigation during landing. This is possible due to the exploitation of the adaptive performance boundaries and adaptive performance trajectory tracking with predefined characteristics. The article by [PERSON] and others focuses on distributed airborne radar, which, in contrast to distributed ground-based radar, has stronger anti-damage ability, more degrees of freedom, and a better detection view of targets. A UAV formation is used as a carrier of airborne radars; thus, stable wireless communication in the formation is essential to maximize the radar detection performance. Applying multi-relay methods directly in the case of long spacings between UAVs can result in wireless network overload and a long time delay. In the article, the measurement of the angles of arrival (AOA) made by each UAV in the swarm is considered to create a passive localization model of an active target. To maximize the accuracy and to achieve the optimal performance of radar localization under inter-UAV communication constraints, i.e., communication coverage, and topology, the authors propose applying a joint radar-communication optimization algorithm (JRCO). The algorithm is fully distributed, and this enables it to simultaneously sense the information of a UAV in the neighboring area and to co-decide in the same time slot. Based on two sets of parameters for four different control groups and one experimental group, numerical simulations of the JRCO algorithm were performed, each changing the communication coverage range, as well as different approaches to optimize UAV locations. Plots of the MSE (Mean Square Error) of the AOA measurements confirm the effectiveness of the JRCO for the experimental group. The next article by [PERSON] and others (contribution 6) considers 2D dynamic visual servoing, where a UAV tracks a ground vehicle, using a single camera facing down toward the ground. To track a ground vehicle like a car, there is no need to use data about orientation angles since the camera is mounted with a gimbal framework, as well as knowing the flight altitude. Control is based on the knowledge of the x and y axes' displacement and difference in heading angle, and the concept of differential flatness is applied to extract and demonstrate that it is possible to control a UAV using this knowledge. The proposed dynamic 2D visual servoing method (Dynamic IBVS) allows generating movements for the quadcopter in real time and solves the problem related to the fact that the quadcopter is an under-actuated system. The effectiveness of the proposed approach in tracking ground vehicles is proved in numerical simulations. The processing of images from the camera, the related delays, the disturbances in control of the gimbal with the camera, and the motion of a ground vehicle on an uneven surface are not considered by the authors. These issues would have a significant impact on the effectiveness of the approach in real-world applications. The final article (contribution 7) by [PERSON] and others concerns a method of linear matrix inequalities (LMIs)-based linear parameter varying (LPV) control for quadrotors with time-varying payload. The novelty of the article is provided by a new 6 DoF LPV model of the quadrotor, which considers the mass, inertia, and mass flow rate as variables, and it is applicable for large tilt angles and scenarios with wind disturbance.