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2-Input 2-Output ANFIS Controller for Trajectory Tracking of Mobile Robot

이동로봇의 경로추적을 위한 2-입력 2-출력 ANFIS제어기

  • Received : 2012.06.28
  • Accepted : 2012.08.30
  • Published : 2012.08.31

Abstract

One approach of the control of a nonlinear system that has gained some success employs a fuzzy structure in cooperation with a neural network(ANFIS). The traditional ANFIS can only model and control the process in single-dimensional output nature in spite of multi-dimensional input. The membership function parameters are tuned using a combination of least squares estimation and back-propagation algorithm. In the case of a mobile robot, we need to drive left and right wheel respectively. In this paper, we proposed the control system architecture for a mobile robotic system that employs the 2-input 2-output ANFIS controller for trajectory tracking. Simulation results and preliminary evaluation show that the proposed architecture is a feasible one for mobile robotic systems.

비선형 시스템을 제어하는 효과적인 방법으로 신경망과 연동된 퍼지구조를 적용한 ANFIS 제어기를 이용되고 있다. 전통적인 ANFIS에서는 다차원의 입력에도 불구하고 단일출력에 대한 공정을 모델링하고 제어 하는데 사용된다. 멤버쉽 함수의 파라미터는 최소자승예측과 역전파 알고리즘을 이용하여 조정된다. 이동로봇의 경우에는 좌측과 우측의 바퀴를 각각 구동할 필요가 있다. 본 논문에서는 이동로봇의 궤적을 추적하기 위하여 2-입력 2-출력을 가진 ANFIS제어기를 적용한 제어시스템 구조를 제안하였다. 시뮬레이션을 통하여 제안된 구조가 이동로봇에 대한 가능한 제어기임을 확인할 수 있었다.

Keywords

References

  1. J.S.R. Jang, "ANFIS: Adaptive-Network Based Fuzzy Inference System", IEEE Trans. on Systems, Man and Cybernetics, vol. 23, no. 3, pp. 665-685, 1993. https://doi.org/10.1109/21.256541
  2. J.S.R. Jang and C.T. Sun, "Neuro-Fuzzy Modelling and Control", Proceeding of IEEE, vol. 83, no. 3, pp. 378-406, 1995. https://doi.org/10.1109/5.364486
  3. T. Takagi and M. Sugeno, "Fuzzy Identifica-tion of Systems and its Applications to odeling and Control", IEEE Trans. on Systems, Man and Cybernetics, vol. 15, no. 1, Feb. 1985, pp. 116-132.
  4. R. Fierro and F.L. Lewis, "Control of a Nonholonomic Mobile Robot using Neural Networks", IEEE Transactions on Neural Networks, vol. 9, no. 4, pp. 389-400, 1998. https://doi.org/10.1109/72.668882
  5. J. Velagic, N. Osmic and B. Lacevic, "Neural Network Controller for Mobile Robot Motion Control", International Journal of Electrical and Computer Engineering, vol. 3, no. 7, pp.427-432, 2008.
  6. B. Allaoua, A. Laoufi, B. Gasbaoui and A. Abderrahmani, "Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization", Leonardo Electronic Journal of Practices and Technologies, issue 15, pp. 1-18, Dec. 2009.
  7. N.M. Thanh and M.S. Chen, "Image Denoising Using Adaptive Neuro-Fuzzy System", International Journal of Applied Mathematics, vol. 36, no. 1, pp. 11-15, 2007.
  8. M. Hayati, M. Seifi and A. Rezaei, "Double Gate MOSFET Modeling Based on Adaptive Neuro-Fuzzy Inference System for Nanoscale Circuit Simulation", ETRI Journal, vol. 32, no. 4, pp. 520-539, Aug. 2010. https://doi.org/10.4218/etrij.10.0109.0707
  9. K.S. Narenda and K. Pathasarathy, "Identification and Control of DynamicSystems using Neural Network", IEEE Trans. on Neural Networks, vol. 1, no. 1, pp.4-27, 1990. https://doi.org/10.1109/72.80202
  10. D.H. Nguyen, and B. Widrow, "Neural Networks for Self-Learning Control Systems", IEEE Control Systems Magazine, vol. 10, no.3, pp. 18-23, 1990. https://doi.org/10.1109/37.55119
  11. H. X. Li, "Three-dimensional Fuzzy Logic System for Modeling and Control", Systems, Man and Cybernetics Society eNewsletter, issue 27, June 2009.