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

Iskandarani, Mohamad; Hafez, Ahmed T.; Givigi, Sidney N.; Beaulieu, Alain; Rabbath, C.A.

doi:10.1109/syscon.2013.6549947

Cited by 15 publications

(16 citation statements)

References 17 publications

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“…A linear second order process model was found from collected flight data with small steady-state error [40] . Each UAV in the team will be characterized by the following state-space representation [40] : …”

Section: Bsystem Identificationmentioning

confidence: 99%

“…For instance, Nonlinear Model Predictive Control (NMPC) can be used to control the formation of a fleet of UAVs in the presence of obstacles and collision avoidance [35] , and to develop the formation guidance for a team of UAVs, where the controllers predict the behavior of the system over the receding horizon and generate the optimal control commands over the horizon for flight formation and inter-collision avoidance in the presence of control input and state constraints [38,39] . In [40] , a combination of decentralized linear MPC and Taylor series linearization was successfully applied to a team of UAVs to solve the problem of the dynamic encirclement around a stationary target. Also, the problem of dynamic encirclement for a team of cooperative UAVs surrounding a stationary and movable target was solved using decentralized NPMC in [28] and [41] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-UAV Tactic Switching via Model Predictive Control and Fuzzy Q-Learning

Hafez

Givigi

Yousefi³

et al. 2017

Eng. Sci. and Milit. Techno.

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Teams of cooperative Unmanned Aerial Vehicles (UAVs) require intelligent and flexible control strategies to allow the accomplishment of a multitude of challenging group tasks. In this paper, we introduce a solution for the problem of tactic switching between the formation flight tactic and the dynamic encirclement tactic for a team of cooperative UAVs using a predictive decentralize control approach. Decentralized Model Predictive Control (MPC) is used to generate tactics for a team of UAVs in simulation and real-world validation. A high-level Linear Model Predictive Control (LMPC) policy is used to control the UAV team during the execution of a desired formation, while a combination of decentralized LMPC and Feedback Linearization (FL) is applied to the UAV team to accomplish dynamic encirclement. The decision of switching from one tactic to the other is derived by a fuzzy logic controller, which, in its turn, is derived by a Reinforcement Learning (RL) approach. The main contributions of this paper are: (i) solution of the problem of tactic switching for a team of cooperative UAVs using LMPC and a fuzzy controller derived via RL; (ii) simulations demonstrating the efficiency of the method; and (iii) implementation of the solution to on-board real-time controllers on Qball-X4 quadrotors.

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Section: Bsystem Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-UAV Tactic Switching via Model Predictive Control and Fuzzy Q-Learning

Hafez

Givigi

Yousefi³

et al. 2017

Eng. Sci. and Milit. Techno.

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show abstract

“…The nonlinear system in (40) will be linearized using a suitable linearization technique, the linearized system will be controlled using LMPC to solve the problem of dynamic encirclement. Taylor series linearization was used to linearize our system according to eight positions around a circular path of radius 1 m and angular velocity 0.1 rad/s in [24], while in this paper an FL technique will be used to linearize the dynamics of the UAV team.…”

Section: B Linearization Of the Uav Systemmentioning

confidence: 99%

“…Moreover, a decentralized NMPC was applied to two UAV teams to solve the problem of dynamic encirclement of two stationary and movable targets at the same time [23], while in [24], a linear MPC (LMPC) combined with Taylor series linearization to control a team of Qball-X4 quadrotor aircraft for dynamic encirclement of a stationary target in real-time implementation.…”

mentioning

confidence: 99%

Solving Multi-UAV Dynamic Encirclement via Model Predictive Control

Hafez

Marasco

Givigi

et al. 2015

IEEE Trans. Contr. Syst. Technol.

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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.

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“…Adaptive control strategies are built in Sato and Maeda (2010) and Guo, Yan, and Lin (2010) to achieve an enclosing formation for a single target when there exists uncertainty or unknown information of the systems. In addition, model predictive control methods are applied in Iskandarani, Hafez, Givigi, Beaulieu, and Rabbath (2013), Marasco, Givigi, Rabbath, and Beaulieu (2013) and to achieve an enclosing formation, where a group of agents enclosing a single stationary or moving target are, respectively, considered.…”

Section: Introductionmentioning

confidence: 99%

Cooperative enclosing control for multiple moving targets by a group of agents

Shi

Teo

2014

International Journal of Control

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In this paper, the enclosing control problem of second-order multi-agent systems is considered, where the targets can be either stationary or moving. The objective is to achieve an equidistant circular formation for a group of agents to enclose a team of targets. In order to do so, we first introduce a formal definition explaining certain basic properties of the exploring relation between the agents and the targets. We then construct the estimator of the centre of the targets, which is used to build the control protocol to achieve equidistant circular enclosing. Using a Lyapunov function and Lasalle's Invariance Principle, the convergency of the estimator and control protocol are, respectively, established. We then construct a smooth function to approximate the discontinuous term in the estimator. Finally, the simulations for stationary targets and moving targets are given to verify the validity of the results obtained.

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

Cited by 15 publications

References 17 publications

Multi-UAV Tactic Switching via Model Predictive Control and Fuzzy Q-Learning

Multi-UAV Tactic Switching via Model Predictive Control and Fuzzy Q-Learning

Solving Multi-UAV Dynamic Encirclement via Model Predictive Control

Cooperative enclosing control for multiple moving targets by a group of agents

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