In order for teams of unmanned aerial vehicles (UAVs) to collaborate and cooperate to perform challenging group tasks, intelligent and flexible control strategies are required. One of the complex behaviors required of a team of UAVs is dynamic encirclement, which is a tactic that can be employed for persistent surveillance and/or to neutralize a target by restricting its movement. This tactic requires a high level of cooperation such that the UAVs maintain a desired and proper encirclement radius and angular velocity around the target. In this paper, model predictive control (MPC) is used to model and implement controllers for the problem of dynamic encirclement. The linear and nonlinear control policies proposed in this paper are applied as a high-level controller to control multiple UAVs to encircle a desired target in simulations and real-time experiments with quadrotors. The nonlinear solution provides a theoretical analysis of the problem, while the linear control policy is used for real-time operation via a combination of MPC and feedback linearization applied to the nonlinear UAV system. The contributions of this paper lie in the implementation of MPC to solve the problem of dynamic encirclement of a team of UAVs in real time and the application of theoretical stability analysis to the problem.

This paper presents a solution for the formation flight problem for multiple unmanned aerial vehicles (UAVs) cooperating to execute a required mission. Learning Based Model Predictive Control (LBMPC) is implemented on the team of UAVs in order to accomplish the required formation. All flight simulations respect Reynold's rules of flocking to avoid UAV collisions with nearby flockmates, match the team members velocity and stay close to each other during the formation. The main contribution of this paper lies in the application of LBMPC to solve the problem of formation for an autonomous team of UAVs. The proposed solution is theoretically, by the application of analysis to the problem, demonstrated to be stable. Moreover, simulations support the findings of the paper. The main contributions of this paper are the proposed LBMPC formulation for formation of vehicles with uncertainty in their models, and the theoretical analysis of the solution.

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This paper investigates the problems of cooperative task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization (PSO) is proposed. Initially, teams of UAVs are moving in a pre-defined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target’s degree of threat, degree of importance, and the separating distance between each team and each detected target. Based on the gathered information, the ground station assigns the teams to the targets. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Next, each team plans its own path by formulating the path planning problem as an optimization problem. The objective in this case is to minimize the time to reach their destination considering the UAVs dynamic constraints and collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve this optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.

The issue of formation rearrangement for a troop of cooperative unmanned vertical takeoff and landing (VTOL) aircrafts in an obstacle-loaded atmosphere is figured out using a purposed backstepping based proportional-integral-derivative controller (PID). The designed controller is developed to regulate every unmanned quadrotor within the troop in an exceedingly localized manner guaranteeing the reserving of the required geometric formation. The backstepping technique could be a promising control technique for nonlinear and coupled multivariable systems. The essential contribution in this paper concentrates on resolving the formation issue for a troop of cooperative pilotless VTOL airplanes in a decentralized manner via backstepping PID regulator. The designed decentralized controller guarantees the success of the required mission of the swarming troop. The simulation results declare the successes of the proposed controller in guaranteeing the stability of the system and reserving of the desired geometric formation either within the existence or absence of obstacles. I. Introduction In recent decenniums, unmanned aerial vehicles (UAVs) have charged a mature concern with their success among achieving heaps of progress in several applications in each military and civilian scopes [1-3]. The UAV is characterized by its capacity to accomplish its assignments in alleged "D-cube" operations (Dull-Dangerous-Dirty) atmosphere with no risk for manned pilots resources [4, 5], easy to preserve and low worth. Therefore, UAV has attained growing concern from scientists, researchers, and engineers. UAVs may be thought about as a hopeful alternative for numerous pilotless military and civilian exercises [6-8]. The auspicious results of a single UAV in executing varied applications persuade the utilization of multiple UAVs cooperating collectively to meet the required tasks [9-11]. Cooperative UAVs guarantees the success of the desired missions with better performance compared with single UAV [12-15]. Certain strategies are needed for multiple cooperative UAVs to cooperate collectively to fulfill the required goals. These strategies which defined by the cooperative UAVs attributes are known as UAVs tactics. These tactics can be classified into the swarming, mission duty, structure rearrangement, and active blockade [16, 17]. Formation rearrangement is outlined by the power of the multiple cooperative UAVs to preserve a desired geometric structure [12], and reconfigure to a different formation per the surrounding circumstances guaranteeing the success of the required application [16, 18]. Each member in the cooperative UAV troop must respect Reynold's rules of flocking during its formation [19-21]. Each UAV member has to match its velocity and separating distance from its neighbors and avoid colliding with its neighbors or obstacles [22]. There are several varieties of controls in the formation rearrangement of multiple cooperative UAVs domain. The scope of these control techniques steadily growing fast last decade including hybrid ...

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