In this paper, we investigate the leader–followers Time-Varying Formation Tracking (TVFT) problem of a networked Multi-Agent Systems (MASs) based output feedback. The agents’ behavior is featured by linear dynamics. The interaction topology among the agents is switching over time, directed and only assumed to contain a directed spanning tree. This latter is rooted by a leader-agent whose control input is unknown and bounded. A novel fully distributed TVFT controller is proposed that exhibits a reduced network information exchange property among the agents, thus less communicating-resources are utilized. The proposed controlled design is based on an adaptive observer and disturbance rejection technique, where the unknown leader-input is viewed as an external disturbance. The key feature lies in introducing a local observer in each agent-controller design to observe all the relative neighboring output-measurements and the relative neighboring distributed observer-outputs, all gathered in one signal that we denote the agent’s network information signal. The Lyapunov theory is used to prove that the closed-loop MASs tracking error is stable. An analysis of the effect of the interaction topology structure on the tracking-error convergence rate is further provided, showing the validity of the proposed formation control for switching interaction topologies. Finally, to verify the effectiveness of the obtained results, the proposed controller is extended to a cooperative guidance of a networked quadrotors to track and entrap an uncooperative aerial target that plays the role of a passive leader.

In this paper, the problem of searching and tracking uncooperative and unidentified mobile ground target using a quadcopter unmanned aerial vehicle (QUAV) is investigated. The proposed strategy is an Image-Based Visual Servoing (IBVS) approach, combined with the virtual camera concept and robust control. This strategy makes use of the rough prior information of the target, in contrast with existing strategies, which will alleviate the problem of altitude estimation noise and enhance the overall accuracy. Hence, a new vision-based sliding mode controller (SMC) is designed to control the quadcopter taking into account the flight phase’s heterogeneity, the external disturbances and parameters uncertainties as well as the target maneuverability. In order to get a better insight about the SMC tuning and adjustment, three different reaching laws are evaluated and compared. The proposed controller allows an automatic execution of the flight strategy whilst the searching phase relies on the Camera Coverage Area (CCA) technique. The vision-based technique allows an automatic QUAV altitude tuning for optimal target observation and tracking. Another contribution of this work is the fact that the designed controller validity and stability overspan the entire scenario to reach the universal and to smoothen out surges generated by control switching. Numerical simulations are conducted to compare the proposed SMC controllers and validate the effectiveness of the whole strategy.