Mohamad Iskandarani

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.

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.

Unmanned Aerial Vehicle formation flying using Linear Model Predictive Control

A team of three Unmanned Aerial Vehicles (UAVs) accomplishes a line abreast, triangular and cross formation based on high-level Linear Model Predictive Control (LMPC). All flight tests respect Reynolds rules of flocking, where the UAVs avoid collisions with nearby flockmates, attempt to match velocity of other team members and attempt to stay close to other flockmates. A linear system identification model is at the base of the error dynamics describing the formation control algorithm. The main contribution of this paper lies in the use of LMPC to implement multiple formations on UAVs in simulation and using the Qball-X4 quadrotor.

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.

Linear Model Predictive Control for the encirclement of a target using a quadrotor aircraft

Encirclement is a task accomplished by an Unmanned Aerial Vehicle (UAV) in order to maintain awareness and containment of a given target. The aim of the UAV encircling this target is to maintain close proximity at all times. 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 Qball-X4 quadrotor aircraft in order to follow the circular path. A linear model for the two-dimensional movement of the UAV and its respective MP controller has been designed in MATLAB Simulink, simulated in a X-Plane/MATLAB interface and implemented on the actual vehicle in real-time. The results of the LMPC in simulation are compared to those found while implementing the algorithm on a physical platform. The contributions of this paper lay in the implementation of an autonomous Linear MP controller for the encirclement of a stationary target by a Qball-X4 quadrotor.

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.

Real time tactic switching for multiple cooperative UAVs via Model Predictive Control

Intelligent and flexible control strategies are required to allow a team of cooperative Unmanned Aerial Vehicles (UAVs) to accomplish a multitude of challenging group tasks. In this paper, a real time implementation of a team of multiple cooperative UAVs performing a combination of UAV tactics is introduced. A team of N cooperative UAVs switches from a desired formation into a dynamic encirclement tactic around a desired stationary target in real time using a decentralized high-level Linear Model Predictive Control (LMPC)combined with Feedback Linearization (FL) technique in real time. During the experiments, the cooperative UAVs avoid collisions with nearby flockmates, attempt to match the velocity of other team members and attempt to stay close to other flockmates. The main contribution of this paper lies in the use of a predictive decentralized approach represented by the LMPC to solve the problem of tactic switching for a team of cooperative quadrotors in real time while ensuring the stability of the system during flight.

Encirclement of moving target using linear model predictive control via feedback linearization

A team of Unmanned Aerial Vehicles (UAVs) is used for the dynamic encirclement of a moving target in simulation. The encirclement tactic is defined for the situation in which a target is isolated and surrounded by a group of UAVs. It may be employed by a team of UAVs to neutralize the target and restrict its movement. A combination of decentralized Linear Model Predictive Control (LMPC) and Feedback Linearization (FL) is implemented on the team of UAVs in order to accomplish dynamic encirclement around a moving target. The main contribution of this paper lays in the application of LMPC and FL to solve the problem of encirclement of a moving target using an autonomous team of UAVs in simulation.