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Control Gain Optimization for Mobile Robots Using Neural Networks and Genetic Algorithms

신경회로망과 유전알고리즘에 기초한 이동로봇의 제어 이득 최적화

  • Received : 2016.01.08
  • Accepted : 2016.02.14
  • Published : 2016.04.30

Abstract

In order to move mobile robots to desired locations in a minimum time, optimal control problems have to be solved; however, their analytic solutions are almost impossible to obtain due to robot nonlinear equations. This paper presents a method to get optimal control gains of mobile robots using genetic algorithms. Since the optimal control gains of mobile robots depend on the initial conditions, the initial condition range is discretized to form some grid points, and genetic algorithms are applied to provide the optimal control gains for the corresponding grid points. The optimal control gains for general initial conditions may be obtained by use of neural networks. So the optimal control gains and the corresponding grid points are used to train neural networks. The trained neural networks can supply pseudo-optimal control gains. Finally simulation studies have been conducted to verify the effectiveness of the method presented in this paper.

Keywords

Neural Network;Genetic Algorithm;Mobile Robot;Minimum-time;Optimal Control Gain

References

  1. F. Fahimi, Autonomous Robots: Modeling, Path Planning, and Control, New York, NY:Springer, 2010.
  2. R. Siegwart, I.R. Nourbakhsh, and D. Scaramuzza, Introduction to Autonomous Mobile Robots, 2nd ED, Cambridge, MA: The MIT Press, 2011.
  3. D.E. Kirk, Optimal Control Theory: An Introduction, Englewood Cliffs, NJ: Prentice-Hall, Inc., 2004.
  4. F.L. Lewis, D. Vrabie and V.L. Syrmos, Optimal Control: 3rd ED, Hoboken, NJ: John Wiley & Sons Inc., 2012.
  5. Y.K. Choi and J.H. Park, "Minimum-time trajectory control of ships using neural networks," Journal of the Korea Institute of Information and Communication Engineering, vol. 17, no. 1, pp 117-126, Jan. 2013. https://doi.org/10.6109/jkiice.2013.17.1.117
  6. M. Mitchell, An Introduction to Genetic Algorithms, Cambridge, MA: The MIT Press, 1998.
  7. Z. Michalewicz, Genetic Algorithms+Data Structures=Evolution Programs, Berlin, Heidelberg: Springer, 1996.
  8. V. Kecman, Learning and Soft Computing, Cambridge, MA: The MIT Press, 2001.
  9. C.T. Lin and C.S.G. Lee, Neural Fuzzy Systems, Upper Saddle River, NJ: Prentice Hall, 1996.
  10. 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.
  11. Y.K. Choi, M.J. Lee and S.S. Kim, "Design and Implementation of an adaptive neural network compensator for control systems," IEEE Trans. on Industrial Electronics, vol. 48, no. 2, April 2001.

Acknowledgement

Supported by : Pusan National University