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Predictive Control based on Genetic Algorithm for Mobile Robots with Constraints

제한조건을 갖는 이동로봇의 유전알고리즘에 의한 예측제어

  • Choi, Young-Kiu (Department of Electrical Engineering, Pusan National University) ;
  • Park, Jin-Hyun (Dept. of Mechatronics Engineering, Kyeognam National Univ. of Science and Technology)
  • Received : 2017.08.29
  • Accepted : 2017.09.25
  • Published : 2018.01.31

Abstract

Predictive control is a very practical method that obtain the current input that minimizes the future errors of the reference command and state by use of the predictive model of the controlled object, and can also consider the constraints of the state and input. Although there have been studies in which predictive control is applied to mobile robots, performance has not been optimized as various control parameters for determining control performance have been arbitrarily specified. In this paper, we apply the genetic algorithm to the trajectory tracking control of a mobile robot with input constraints in order to minimize the trajectory tracking errors through control parameter tuning, and apply the quadratic programming Hildreth method to reflect the input constraints. Through the computer simulation, the superiority of the proposed method is confirmed by comparing with the existing method.

예측제어는 제어대상의 예측모델을 이용하여 기준명령과 상태의 미래 오차를 예측하고 최소화시키는 현재 입력을 구하며, 상태와 입력의 제한조건도 고려할 수 있는 매우 실용적인 방법이다. 이동로봇에 대해 예측제어가 적용된 연구들이 있었으나 제어성능을 결정하는 여러 제어 파라미터들이 임의로 지정됨에 따라 성능이 최적화되지 못하였다. 본 논문에서 입력제한조건을 갖는 이동로봇의 궤적추종 예측제어에 유전알고리즘을 적용하여 제어 파라미터 튜닝을 통해 궤적추종오차를 최소화하며, 입력의 제한조건을 반영하기 위해서 quadratic programming을 Hildreth 방법을 적용한다. 컴퓨터 시뮬레이션을 통해 본 논문에서 제안한 방법의 우수성을 기존 방법과 비교하여 확인한다.

Keywords

References

  1. K. M. Lynch and F. C. Park, Modern Robotics: Mechanics, Planning and Control, New York, NY: Cambridge University Pressr, 2017.
  2. R. S. Ortigoza and J. R. Sanchez,"Trajectory Tracking Control for a Differential Drive Wheeled Mobile Robot Considering the Dynamics Related to the Actuators and Power Stage," IEEE Latin America Trans, vol. 14, no. 2, pp. 657-664, Feb 2016. https://doi.org/10.1109/TLA.2016.7437207
  3. K. Shojaei and A. M. Shahri, "Output feedback tracking control of uncertain non-holonomic wheeled mobile robots: a dynamic surface control approach," IET Control Theory and Applications, vol. 6, no. 2, pp. 216-228, Jan 2012. https://doi.org/10.1049/iet-cta.2011.0169
  4. Y. Wang, S. Wang, R. Tan, and Y. Jiang, "Motion control of a wheeled mobile robot using digital acceleration control method," International Journal of Innovative Computing, Information and Control, vol. 9, no. 1, pp. 387-396, Jan 2013.
  5. E. F. Camacho and C. Bordons, Model Predictive Control, London, UK: Springer-Verlag, 2007.
  6. D. Gu and H. Hu, "Receding horizon tracking control of wheeled mobile robots," IEEE Trans on Control System Technology, vol. 14, no. 4, pp. 743-749, July 2006. https://doi.org/10.1109/TCST.2006.872512
  7. S. Akiba, T. Zanma, and M. Ishida, "Optimal tracking control of two-wheeled mobile robots based on model predictive control," in Proceeding of the 11th IEEE International Workshop on Advanced Motion Control, Nagaoka, Niigata: Japan, pp. 454-459, May 2010.
  8. G. Klancar and I. skrjanc, "Tracking-error model-based predictive control for mobile robots in real time," Robotics and Autonomous Systems, vol. 55, no. 6, pp. 460-469, June, 2007. https://doi.org/10.1016/j.robot.2007.01.002
  9. H. S. Son, J. H. Park and Y. K. Choi, "Predictive control for mobile robots using genetic algorithms," Journal of the Korea Institute of Information and Communication Engineering, vol. 21, no. 4, pp. 698-707, Apr. 2017. https://doi.org/10.6109/JKIICE.2017.21.4.698
  10. C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems, Upper Saddle River, NJ: Prentice Hall, 1996.
  11. L. Wang, Model Predictive Control System Design and Implementation Using MATLAB, London: UK, Springer Verlag, 2009.
  12. J. H. Park and Y. K. Choi, "Control gain optimization for mobile robots using neural networks and genetic algorithms," Journal of the Korea Institute of Information and Communication Engineering, vol. 20, no. 4, pp. 698-707, Apr. 2016. https://doi.org/10.6109/jkiice.2016.20.4.698

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