DOI QR코드

DOI QR Code

Formation Control of Mobile Robots using PID Controller with Neural Networks

신경회로망 PID 제어기를 이용한 이동로봇의 군집제어

  • Kim, Yong-Baek (Department of Electrical Engineering, Pusan National University, Automation Research Dept., Industrial Technology Institute, Hyundai Heavy Industries) ;
  • Park, Jin-Hyun (Dept. of Mechatronics Eng., Kyeognam National University of Science and Technology) ;
  • Choi, Young-Kiu (Department of Electrical Engineering, Pusan National University)
  • Received : 2014.05.13
  • Accepted : 2014.06.26
  • Published : 2014.08.31

Abstract

In this paper, a PID controller with interpolated gains by use of neural networks is proposed for the formation control problem that following robots track a leading robot with constant distances and angles when there are changes in the mass of the following robot. The whole control system is composed of a kinematic controller and a dynamic controller considering the robot dynamics. The dynamic controller is the PID controller with varying gains, and the proper gains are obtained for some representative masses of the follower robot by the genetic algorithm. Neural networks is trained using the genetic algorithm with the gain data obtained in the previous step. The trained neural network determines optimal PID gains for a random mass of following robot. Simulation studies show that for arbitrary masses of the tracking robot, the PID controller with interpolated gains by the trained neural network has better tracking performance than that of the PID controller with fixed gains.

본 논문은 선도 로봇을 추종 로봇이 일정거리와 각도를 두고 추종하는 군집제어에서, 추종 로봇의 질량이 변할 경우, 신경회로망을 통해 보간된 이득을 갖는 PID제어기를 제안한다. 전체 제어시스템은 기구학 제어기와 동역학을 고려한 동적제어기로 구성하였다. 동적제어기는 가변 이득을 가지는 PID 제어기로 구성하여, 추종 로봇의 대표적 질량에 따라 적절한 PID 이득을 유전 알고리즘으로 구하였다. 유전 알고리즘으로 구한 데이터를 기초로 신경회로망을 학습하여 추종 로봇이 임의의 질량을 갖더라도 최적의 PID 이득을 선정할 수 있었다. 모의실험에서 추종 로봇의 질량이 임의의 값으로 변화하는 경우, 신경회로망을 통해 보간된 이득을 갖는 PID 제어기가 고정된 이득을 가지는 PID 제어기에 비해 군집제어에서 추종 성능을 향상시키는 것을 확인하였다.

Keywords

References

  1. Y. Q. Chen and Z. Wang, "Formation control: a review and a new consideration," Proc. IEEE International Conference on Intelligent Robots and systems, pp. 3181-3186, Aug 2005.
  2. Aveek K. Das et. al., "A vision-based formation control framework," IEEE Trans on Robotics and Automaton, vol. 18, no. 5, Oct 2002.
  3. J. Shao, G. Xie and L. Wang, "Leader-following formation control of multiple mobile vehicles," IET Control Theory and Applications, vol. 1, no. 2, pp. 545-552, Mar 2007. https://doi.org/10.1049/iet-cta:20050371
  4. R. Fierro and F. L. Lewis, "Control of a nonholonomic mobile robot: Backstepping kinematics into dynamics," Proc. IEEE Conference on Decision and Control, pp. 3805-3810, 1995.
  5. A. M. Bloch, M. Reyhanoglu and N. H. McClamroch, "Control and stabilization of nonholonomic dynamic systems," IEEE Trans. Automatic Control, vol. 37, pp. 1746-1757, 1992. https://doi.org/10.1109/9.173144
  6. N. Sarkar, X. Yun and V. Kumar, "Control of mechanical system with rolling constraints: Application to dynamical control of mobile robots," Int. J. Robot. Res., vol. 13, no. 1, pp. 55-69, 1994. https://doi.org/10.1177/027836499401300104
  7. E. R. Fierro and F. L. Lewis, "Control of a nonholonomic mobile robot using neural networks," IEEE Trans on Neural Networks, vol. 9, no. 4, July 1998.
  8. Y. B. Kim, J. H. Park and Y. K. Choi, "Design of PID controller with adaptive neural network compensator for formation control of mobile robots," Journal of the Korea Insttute of Information and Communication Engineering, vol. 18, no. 3, Mar 2014. https://doi.org/10.6109/jkiice.2014.18.3.503