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In this paper, a proposed particle swarm optimization called multi-objective particle swarm optimization (MOPSO) with an accelerated update methodology is employed to tune Proportional-Integral-Derivative (PID) controller for an AR.Drone quadrotor. The proposed approach is to modify the velocity formula of the general PSO systems in order for improving the searching efficiency and actual execution time. Three PID control parameters, i.e., the proportional gain K-p, integral gain K-i and derivative gain K-d are required to form a parameter vector which is considered as a particle of PSO. To derive the optimal PID parameters for the Ar.Drone, the modified update method is employed to move the positions of all particles in the population. In the meanwhile, multi-objective functions defined for PID controller optimization problems are minimized. The results verify that the proposed MOPSO is able to perform appropriately in Ar.Drone control system.

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Chicago
Mac Thi, Thoa, Cosmin Copot, Trung Tran Duc, and Robain De Keyser. 2016. “AR.Drone UAV Control Parameters Tuning Based on Particle Swarm Optimization Algorithm.” In PROCEEDING OF 2016 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, QUALITY AND TESTING, ROBOTICS (AQTR) , 475–480.
APA
Mac Thi, T., Copot, C., Duc, T. T., & De Keyser, R. (2016). AR.Drone UAV control parameters tuning based on particle swarm optimization algorithm. PROCEEDING OF 2016 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, QUALITY AND TESTING, ROBOTICS (AQTR) (pp. 475–480). Presented at the IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR).
Vancouver
1.
Mac Thi T, Copot C, Duc TT, De Keyser R. AR.Drone UAV control parameters tuning based on particle swarm optimization algorithm. PROCEEDING OF 2016 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, QUALITY AND TESTING, ROBOTICS (AQTR) . 2016. p. 475–80.
MLA
Mac Thi, Thoa, Cosmin Copot, Trung Tran Duc, et al. “AR.Drone UAV Control Parameters Tuning Based on Particle Swarm Optimization Algorithm.” PROCEEDING OF 2016 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, QUALITY AND TESTING, ROBOTICS (AQTR) . 2016. 475–480. Print.
@inproceedings{7224888,
  abstract     = {In this paper, a proposed particle swarm optimization called multi-objective particle swarm optimization (MOPSO) with an accelerated update methodology is employed to tune Proportional-Integral-Derivative (PID) controller for an AR.Drone quadrotor. The proposed approach is to modify the velocity formula of the general PSO systems in order for improving the searching efficiency and actual execution time. Three PID control parameters, i.e., the proportional gain K-p, integral gain K-i and derivative gain K-d are required to form a parameter vector which is considered as a particle of PSO. To derive the optimal PID parameters for the Ar.Drone, the modified update method is employed to move the positions of all particles in the population. In the meanwhile, multi-objective functions defined for PID controller optimization problems are minimized. The results verify that the proposed MOPSO is able to perform appropriately in Ar.Drone control system.},
  author       = {Mac Thi, Thoa and Copot, Cosmin and Duc, Trung Tran  and De Keyser, Robain},
  booktitle    = {PROCEEDING OF 2016 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, QUALITY AND TESTING, ROBOTICS (AQTR) },
  isbn         = {978-1-4673-8691-3},
  issn         = {1844-7872 },
  language     = {eng},
  location     = {Cluj-Napoca, Romania},
  pages        = {475--480},
  title        = {AR.Drone UAV control parameters tuning based on particle swarm optimization algorithm},
  year         = {2016},
}