In this paper, a survey on distributed control applications for multi Unmanned Aerial Vehicles (UAVs) systems is proposed. The focus is on consensus-based control, and both rotary-wing and fixed-wing UAVs are considered. On one side, the latest experimental configurations for the implementation of formation flight are analysed and compared for multirotor UAVs. On the other hand, the control frameworks taking into account the mobility of the fixed-wing UAVs performing target tracking are considered. This approach can be helpful to assess and compare the solutions for practical applications of consensus in UAV swarms.

The decentralized estimation and tracking of a mobile target performed by a group of unmanned aerial vehicles (UAVs) is studied in this work. A flocking protocol is used for maintaining a collision-free formation, while a decentralized extended Kalman filter in the information form is employed to provide an estimate of the target state. In the prediction step of the filter, we adopt and compare three different models for the target motion with increasing levels of complexity, namely, a constant velocity (CV), a constant turn (CT), and a full-state (FS) model. Software-in-the-loop (SITL) simulations are conducted in ROS/Gazebo to compare the performance of the three models. The coupling between the formation and estimation tasks is evaluated since the tracking task is affected by the outcome of the estimation process.

This paper investigates a leader-follower formation of a group of second-order agents, considering an undirected and connected topology. It is assumed that no velocity information is available, and that a uniform delay affects the processing of the displacement information. To address these issues, a delayed relative displacement feedback is introduced. A necessary and sufficient condition for stability is derived in terms of a delay threshold, given that the delay-free controller is stable. Moreover, a stability region in the complex plane for the eigenvalues of the Laplacian interaction matrix is introduced. The results are illustrated through numerical examples.

Tracking highly maneuvering, non-cooperative UAVs poses significant challenges due to rapid and unpredictable changes in target dynamics. Under such conditions, traditional single-model filters often fail to maintain reliable state estimates, resulting in degraded tracking performance. Multiple-Model Kalman Filter (MMKF) approaches, including the Generalized Pseudo Bayesian (GPB1) and Interacting Multiple-Model (IMM) algorithms, improve robustness by simultaneously considering multiple candidate motion models and weighting them according to the observed target behavior. Adaptive strategies, such as ${\chi }^2$-test-based or t-test-based methods, further enhance performance by dynamically responding to changes in maneuvering patterns. This paper presents a multi-criteriacomparative assessment of four MMKF formulations—GPB1, IMM, ${\chi }^2$-test-based, and t-test-based filters—under a consistent modeling and simulation framework. Particular emphasis is placed on systematically analyzing the role of the transition probability matrix (TPM), investigating how fixed, adaptive, and TPM-free strategies affect estimation accuracy, robustness to noise, and mode-identification performance. Beyond conventional Root Mean Square Error (RMSE) metrics, the filters’ comparison is carried out through confusion matrices and dwell time analysis to highlight performance nuances and trade-offs. This allows to establish which filter formulation is preferable in different operational conditions.