Distributed fault detection and isolation in time‐varying formation tracking UAV multi‐agent systems

Abstract

The problem of distributed fault detection and isolation (DFDI) in conjunction with time‐varying formation control of unmanned aerial vehicle (UAV) multi‐agent systems with multiple‐leader leader–follower structure is studied, in this paper. It is assumed that the communication data of the agents are noisy, and faults may occur in either leaders or followers. The followers are not aware of the main trajectory and just follow the leaders. The first step in this paper is to reduce the communication noise. Besides, if a fault occurs in one of the leaders causing to exit the leader from the main trajectory, the followers should be able to detect the fault, isolate the leader, use the other leaders' data, and keep the main trajectory. Tracking the trajectory by followers in such a case is the second step of this paper. Preserving the time‐varying formation despite a fault occurring in one of the followers is the next step. To reach these goals, a distributed array of Kalman filters is used for noise cancellation and data extraction. Next, the combination of state vector data fusion and some type of χ2 test are employed for DFDI, in agents. Besides, a controller is implemented in each agent for formation tracking. In this controller, estimated relative position and velocity vectors of the agents are employed to keep the time varying formation while tracking the leaders. The closed‐loop stability is studied via Lyapunov stability theorem, and various simulations are carried out to demonstrate the capability of the proposed strategy.

Leader–follower synchronization of networked multi‐agent systems via hybrid protocols

“…When the secure communication is applied to the agents in a network, synchronization of all the nodes will guarantee that all the agents can obtain the same original message. As a specific kind of DNs, networked multi-agent systems (NMASs) have recently been widely studied due to their applications in unmanned aerial vehicles [8], sensor networks [9], smart grids [10], and so on. Subsequently, many scholars have devoted to investigating the dynamics of NMASs, and many interesting consensus or synchronization results on NMASs have been reported [11][12][13][14][15][16].…”

Input‐constrained optimal output synchronization of heterogeneous multiagent systems via observer‐based model‐free reinforcement learning

“…Distributed cooperative control of multiagent systems (MASs) has attracted compelling attention due to its considerable application prospect in various fields such as physics, engineering, biology, and social sciences [1][2][3][4][5]. The main objective is to develop a control protocol that uses only available local information such that all agents can reach an agreement or track the leader's trajectory.…”

Distributed adaptive time‐varying formation of multi‐UAV systems under undirected graph

“…However, in [26][27][28][29][30][31], the proposed control protocol relies on the eigenvalues of the global topology and has a large amount of computation. The designed control protocol cannot be called fully distributed as it cannot solely rely on the state information of agents and adjacent agents.…”

“…[26] utilized sliding mode control theory to design a distributed time‐varying formation control protocol. Considering multi‐UAV systems with faults and multiple leaders, [27] designed a time‐varying formation control protocol based on distributed Kalman filters. [28–30] studied the time‐varying formation control problem of multi‐UAV systems, and verified the effectiveness of the designed distributed control protocol through a self‐built UAV experimental platform.…”