Ahmed T. Hafez

Solving Multi-UAV Dynamic Encirclement via Model Predictive Control

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.

Encirclement of multiple targets using model predictive control

Two teams of Unmanned Aerial Vehicles (UAVs) are used in the encirclement of two targets at the same time. Encirclement is defined as the situation in which a target is isolated and surrounded by a group of UAVs. It is a tactic that can be employed by a team of UAVs to neutralize a target by restricting its movement due to a containment motion near the target while maintaining a formation around it. In this paper, the problem of choosing the correct target to create a dynamic circular formation is considered and a Decentralized Model Predictive Control (DMPC) policy is formulated. From simulation results the derived Model Predictive Control (MPC) policy is effective for the case of two teams of UAVs encircling two stationary targets, and two teams of UAVs encircling two moving targets. The contributions of this paper are the application of MPC to the problem of encirclement, the explicit objective of a dynamic circular formation around the target, and the ability of each team to choose its correct target.

Using multiple Quadrotor aircraft and Linear Model Predictive Control for the encirclement of a target

A Multi-Unmanned Aerial Vehicle (UAV) team formed from two or more UAVs is used in the encirclement of a target. Encirclement is defined as the situation in which a target is isolated and surrounded by a UAV team in order to maintain awareness and containment of that target. In this paper, the problem of maintaining a circular path around a target is considered and a Linear Model Predictive Control (LMPC) strategy is implemented on a team of Qball-X4 quadrotor aircraft in order to follow the circular path. The linear plant controlled by the LMPC is a combination of process models found through system identification and a linear cartesian to polar transformation. A collision avoidance system, based on potential fields, is successfully implemented between the Qball-X4 quadrotors. The contribution of this paper lay in the application of LMPC to the problem of encirclement using a team of Qball-X4 quadrotors and the ability of these UAVs to apply a collision avoidance policy.

Using Linear Model Predictive Control via Feedback Linearization for dynamic encirclement

An Unmanned Aerial Vehicle (UAV) team formed from two or more UAVs is used in the autonomous encirclement of a stationary target in simulation. The encirclement tactic is defined as the situation in which a target is surrounded by a UAV team in formation. This tactic can be employed by a team of UAVs to neutralize a target by restricting its movement. A combination of Linear Model Predictive Control (LMPC) and Feedback Linearization (FL) is implemented on a team of UAVs in order to accomplish dynamic encirclement. The linear plant, representing each UAV, is found through System Identification then linearized using an FL technique. The contributions of this paper lay in the application of LMPC and FL to the problem of encirclement using an autonomous team of UAVs in simulation.

UAVs in Formation and Dynamic Encirclement Via Model Predictive Control

Abstract Switching between the formation flight tactic and the dynamic encirclement tactic for a team of Unmanned Aerial Vehicles (UAVs) is done using a decentralized approach. A team formed from N UAVs, accomplishes a line-of-breast formation then dynamic encirclement around a desired target. A high-level Linear Model Predictive Control (LMPC) policy is used to control the UAV team during the execution of the required formation tactic, while a combination of decentralized LMPC and Feedback Linearization (FL) is implemented on the UAV team to accomplish dynamic encirclement. During the simulations, Reynolds rules of flocking are respected. The linear plant, representing each UAV, is found through System Identification. The main contribution of this paper lies in the use of LMPC to implement multiple UAV tactics while ensuring stability and robustness of the system during tactic switching.

Fault-tolerant control for cooperative unmanned aerial vehicles formation via fuzzy logic

In this paper, we investigate the problem of fault tolerant for a group of multiple autonomous cooperative unmanned aerial vehicles (UAVs) during execution their mission in a desired geometrical pattern. A decentralized linear model predictive control (LMPC) is designed and implemented on a team of cooperative UAVs to achieve the desired formation in the absence of faults (normal/fault-free cases). When faults occur in one or more of the UAVs, a fault-tolerant cooperative control (FTCC) strategy is designed via fuzzy logic such that each UAV in the team will take its own decision in a decentralized manner, to reconfigure the formation of the whole team ensuring the execution of the required mission. The proposed controller respects the general formation constraints known as Reynolds rules of flocking during simulations. Our main contribution in this paper lays in the use of fuzzy logic control by the cooperative UAVs in taking the decision to solve the problem of formation reconfiguration for an autonomous team of UAVs in the presences of faults.

Formation reconfiguration of cooperative UAVs via Learning Based Model Predictive Control in an obstacle-loaded environment

Learning Based Model Predictive Control (LBMPC) is a new control policy that combines statistical learning along with control engineering while providing levels of guarantees on safety, robustness and convergence. The designed control policy respects the general rules of flocking such that when static obstacles appear, the UAVs are required to steer around them and also avoid collisions between each other. Also, each UAV in the team match the other team members velocity and stay close to its flockmates during flight. Our main contribution in this paper lays in solving the formation reconfiguration problem for a group of N cooperative UAVs forming a desired formation using LBMPC in the presence of uncertainties and obstacles in simulation.

Multiple cooperative UAVs target tracking using Learning Based Model Predictive Control

In this paper, formation of a group of multiple cooperative unmanned aerial vehicles (UAVs) in a desired geometrical pattern while tracking an aerial target is implemented using decentralized Learning Based Model Predictive Control (LBMPC). The LBMPC is a new control technique that combines statistical learning along with control engineering providing guarantees on safety, robustness and convergence. The controller derived in this paper demonstrates the ability of the vehicles to cooperate, in a decentralized manner, to solve the formation problem in the presence of system uncertainties. The proposed controller respects the general formation constraints known as Reynolds rules of flocking during simulations. Our main contribution in this paper lays in the use of decentralized LBMPC in solving the problem of formation for a group of cooperative UAVs tracking an aerial target in the presence of unmodeled dynamics. A theoretical proof for stability will support our proposed controller.

Applying quadrotor aircraft to dynamic encirclement

A combination of decentralized Linear Model Predictive Control (LMPC) and Feedback Linearization (FL) is implemented on a team of quadrotor aircraft in order to accomplish dynamic encirclement around a stationary target in real-time. Dynamic encirclement is defined as the situation in which a target is isolated and surrounded by an Unmanned-Aerial-Vehicle (UAV) team in order to maintain awareness and containment of it. In this paper, the problem of maintaining a desired radius of encirclement, individual angular velocities and angular separation between team members is considered. The nonlinear plant, representing each vehicles autonomous behaviour, is obtained through system identification and then linearized using an FL technique. Due to complex flight variables and multi-vehicle cooperation, LMPC is the most suited controller for solving the problem of dynamic encirclement on real-world platforms. The contribution of this paper lay in the application of decentralized LMPC and FL to the problem of encirclement using an autonomous team of Qball-X4 quadrotors in real-time.

Task assignment/trajectory planning for unmanned vehicles via HFLC and PSO

This paper investigates the problems of 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 is proposed. Initially, teams of UAVs are moving in a pre-determined 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 targets degree of threat, degree of importance, and the separating distance between each team and each detected target. First, the ground station assigns the teams to the targets based on the gathered information. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Then, each team plans its own path by formulating the path planning problem as an optimization problem, while the objective is to minimize the time to reach their destination considering the UAVs dynamic constraints and the collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve the optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.