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전방향 모바일 로봇에서 유전알고리즘을 이용한 적분 슬라이딩 기반 동적 제어 기법

Integral Sliding-based Dynamic Control Method using Genetic Algorithm on an Omnidirectional Mobile Robot

  • Park, Jin-Hyun (Department of Mechatronics Engineering, Gyeongsang National University) ;
  • Choi, Young-Kiu (Department of Electrical Engineering, Pusan National University)
  • 투고 : 2021.08.18
  • 심사 : 2021.09.27
  • 발행 : 2021.12.31

초록

전방향 모바일 로봇은 로봇의 방향을 바꿀 필요 없이 어떤 방향으로든 움직일 수 있어 여러 응용 분야에서 적용이 쉽고 뛰어난 기동성을 제공한다. 전방향 모바일 로봇은 마찰과 같은 비선형 동적 성분을 가지고 있어 정확히 모델링하기에 어렵다. 본 연구에서는 이러한 비선형 성분을 제거하기 위하여 모바일 로봇의 역 다이내믹과 적분 슬라이딩 모드 제어기법을 사용하여 모바일 로봇 시스템을 선형화하고, 제안된 제어기법의 최적 성능을 구현하기 위하여 유전알고리즘을 사용하여 위치 및 속도 이득을 최적화한다. 성능 평가 결과 유전알고리즘을 적용한 제어기법이 임의의 이득을 갖는 제어기법보다 뛰어난 성능을 나타내었다. 그리고 제안된 역 다이내믹과 적분 슬라이딩 모드 제어기법은 다른 제어기법에서도 적용될 수 있으며, 특히 선형제어시스템 설계에 유용하게 사용될 수 있다.

Omnidirectional mobile robots can be mobile in any direction without changing the robot's direction, making them easy to apply in many applications and providing excellent maneuverability. Omnidirectional mobile robots have non-linear dynamic components such as friction, making them difficult to model accurately. In this paper, we linearize the mobile robot system using the mobile robot's inverse dynamics and integral sliding mode control method to remove these nonlinear components. And the position and velocity gains are optimized using a genetic algorithm to realize the optimal performance of the proposed system control method. As a result of the performance evaluation, the genetic algorithm's control method showed superior performance than the control method with an arbitrary gain. And the proposed inverse dynamic and integral sliding mode control method can be applied to other control methods. It can be beneficial for designing a linear control system.

키워드

참고문헌

  1. S. Lee and J. Seul, "Design of a fuzzy compensator for balancing control of a one-wheel robot," International Journal of Fuzzy Logic and Intelligent Systems, vol. 16, no. 4, pp. 188-196, Apr. 2016. https://doi.org/10.5391/IJFIS.2016.16.3.188
  2. M. Mende, M. L. Scott, J. van Doorn, D. Grewal, and I. Shanks, "Service robots rising: How humanoid robots influence service experiences and elicit compensatory consumer responses," Journal of Marketing Research, vol. 56, no. 4, pp. 535-556, 2019. https://doi.org/10.1177/0022243718822827
  3. T. Takemori, T. Motoyasu, and F. Matsuno, "Gait design for a snake robot by connecting curve segments and experimental demonstration," IEEE Transactions on Robotics, vol. 34, no. 5, pp. 1384-1391, 2018. https://doi.org/10.1109/tro.2018.2830346
  4. Y. Wang, R. Wang, S. Wang, M. Tan, and J. Yu, "Underwater bioinspired propulsion: From inspection to manipulation," IEEE Transactions on Industrial Electronics, vol. 67, no. 9, pp. 7629-7638, 2019. https://doi.org/10.1109/tie.2019.2944082
  5. A. Saenz, V. Santibanez, E. Bugarin, A. Dzul, H. Rios, and J. Villalobos-Chin, "Velocity Control of an Omnidirectional Wheeled Mobile Robot Using Computed Voltage Control with Visual Feedback: Experimental Results," International Journal of Control, Automation and Systems, vol. 19, no. 2, pp. 1089-1102, 2021. https://doi.org/10.1007/s12555-019-1057-6
  6. H. Kim and B. K. Kim, "Minimum-energy cornering trajectory planning with self-rotation for three-wheeled omni-directional mobile robots," International Journal of Control, Automation and Systems, vol. 15, no. 4, pp. 1857-1866, 2017. https://doi.org/10.1007/s12555-016-0111-x
  7. B. Siciliano and O. Khatib, and T. Kroger, The Handbook of Robotics. Berlin,Germany: Springer-Verlag, 2008.
  8. B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo, Robotics-Modelling,Planning and Control, 3rd ed. Berlin, Germany: Springer-Verlag, pp. 248-302, 2009.
  9. J. B. Rawlings and D. Q. Mayne, Model Predictive Control: Theory and Design, Madison, WI, USA: Nob Hill, 2009.
  10. P. Karamanakos, E. Liegmann, T. Geyer, and R. Kennel, "Model predictive control of power electronic systems: Methods, results, and challenges," IEEE Open Journal of Industry Applications, vol. 1, pp. 95-114, 2020. https://doi.org/10.1109/ojia.2020.3020184
  11. J. Yang, W. X. Zheng, S. Li, B. Wu, and M. Cheng, "Design of a prediction accuracy-enhanced continuous-time MPC for disturbed systems via a disturbance observer," IEEE Trans. Ind. Electron., vol. 62, no. 9, pp. 5807-5816, Sep. 2015. https://doi.org/10.1109/TIE.2015.2450736
  12. E. Kayacan, H. Ramon, and W. Saeys, "Robust trajectory tracking error model-based predictive control for unmanned ground vehicles," IEEE/ASME Trans. Mechatronics, vol. 21, no. 2, pp. 806-814, Apr. 2016. https://doi.org/10.1109/TMECH.2015.2492984
  13. A. J. Spiers, B. Calli, and A. M. Dollar, "Variable-friction finger surfaces to enable within-hand manipulation via gripping and sliding," IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 4116-4123, 2018. https://doi.org/10.1109/lra.2018.2856398
  14. L. Mostefai, M. Denai, and Y. Hori, "Robust tracking controller design with uncertain friction compensation based on a local modeling approach," IEEE/ASME Trans. Mechatronics, vol. 15, no. 5, pp. 746-756, Oct. 2010. https://doi.org/10.1109/TMECH.2009.2033315
  15. B. Xu, C. Pradalier, A. Krebs, R. Siegwart, and F. Sun, "Composite con trol based on optimal torque control and adaptive Kriging control for the CRAB rover," in Proc. IEEE Int. Conf. Robot. Autom., pp. 1752-1757, May. 2011.
  16. G. P. Incremona, A. Ferrara, and L. Magni, "MPC for robot manipulators with integral sliding modes generation," IEEE/ASME Transactions on Mechatronics, vol. 22, no. 3, pp. 1299-1307, Jun. 2017. https://doi.org/10.1109/TMECH.2017.2674701
  17. J. C. L. Barreto S, A. G. S. Conceicao, C.E.T. Dorea, L. Martinea, and E. R. de Pieri, "Design and implementation of model-predictive control with friction compensation on an omnidirectional mobile robot," IEEE/ASME Transactions On Mechatronics, vol.19, no. 2, pp. 467-476, Apr. 2014. https://doi.org/10.1109/TMECH.2013.2243161
  18. M. Rubagotti, D. M. Raimondo, A. Ferrara, and L. Magni, "Robust model predictive control with integral sliding mode in continuous-time sampled data nonlinear systems," IEEE Trans. Autom. Control, vol. 56, no. 3, pp. 556-570, Mar. 2011. https://doi.org/10.1109/TAC.2010.2074590
  19. H. Olsson, K. J. Astrom, C. Canudas de Wit, M. Gafvert, and P. Lischinsky, "Friction models and friction compensation," Eur. J. Contr., vol. 4, pp. 176-195, 1998. https://doi.org/10.1016/S0947-3580(98)70113-X
  20. V. I. Utkin and J. Shi, "Integral sliding mode in systems operating under uncertainty conditions," in Proc. 35th IEEE Conf. Decis. Control, vol. 4, pp. 4591-4596, Dec. 1996.
  21. 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
  22. J. H Park and Y. K. Choi, "Design of Mobel Predictive Controllers with Velocity and Acceleration Constraints," Journal of Korean Society of Mechanical Technology, vol. 20, no. 6, pp. 809-817, 2018. https://doi.org/10.17958/ksmt.20.6.201812.809