JRM Vol.28 No.5 pp. 752-758
doi: 10.20965/jrm.2016.p0752


Data-Driven Torque Controller for a Hydraulic Excavator

Yasuhito Oshima*1, Takuya Kinoshita*1, Kazushige Koiwai*2, Toru Yamamoto*3, Takao Nanjo*4, Yoichiro Yamazaki*4, and Yoshiaki Fujimoto*4

*1Graduate School of Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-hiroshima city, Hiroshima 739-8527, Japan

*2Collaborative Research Division, Institute of Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-hiroshima city, Hiroshima 739-8527, Japan

*3Division of Electrical, Systems and Mathematical Engineering, Institute of Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-hiroshima city, Hiroshima 739-8527, Japan

*4Global Engineering Center, Kobelco Construction Machinery Co., Ltd.
2-2-1 Itsukaichikou, Saeki-ku, Hiroshima city, Hiroshima 731-5161, Japan

April 5, 2016
June 12, 2016
October 20, 2016
data-driven, FRIT, nonlinear system, derivative system, hydraulic excavator
Proportional-integral-derivative (PID) control has been widely used in industrial equipment. However, the transient response obtained is poor when the fixed PID controller is used for nonlinear systems. Moreover, a hydraulic excavator consists of a nonlinear system with a derivative element. In this case, the system output does not track the reference signal because the integral element in the controller is canceled out by the derivative element in the system. In order to improve the transient response and the tracking performance, a data-driven PID control for tuning and updating PID gains has been proposed. However, the data-driven PID control method could not improve the tracking performance. In this paper, the controller design scheme based on a data-driven approach for the hydraulic excavator is proposed. Moreover, the control parameters of the proposed scheme are updated in an off-line manner by using fictitious reference iterative tuning. The effectiveness of this controller is verified by simulating the control behavior of a hydraulic excavator.
Hydraulic system in the excavator

Hydraulic system in the excavator

Cite this article as:
Y. Oshima, T. Kinoshita, K. Koiwai, T. Yamamoto, T. Nanjo, Y. Yamazaki, and Y. Fujimoto, “Data-Driven Torque Controller for a Hydraulic Excavator,” J. Robot. Mechatron., Vol.28 No.5, pp. 752-758, 2016.
Data files:
  1. [1] S. Okabe, “A Complete Work in Hydraulic Excavator,” Japan Industrial Publishing Co., Ltd., 2007 (in Japanese).
  2. [2] H. Inoue and H. Yoshida, “Development of Hybrid Hydraulic Excavators,” Int. J. of Automation Technology, Vol.6, No.4, pp. 516-520, 2012.
  3. [3] S. Ashizawa, T. Watanabe, Y. Kamiya, H. Aoki, and T. Oomichi, “Development of the Energy Simulator for the Water Hydraulic System Under Flow Condition Changes,” J. of Robotics and Mechatronics, Vol.23, No.3, pp. 416-425, 2011.
  4. [4] H. Yamada, K. Takeichi, and T. Mato, “Sliding Mode Control of Hydraulic Power Shovel,” J. of Robotics and Mechatronics, Vol.15, No.1, pp. 47-53, 2002.
  5. [5] T. Ohgi and Y. Yokokohji, “Control of Hydraulic Actuator Systems Using Feedback Modulator,” J. of Robotics and Mechatronics, Vol.20, No.5, pp. 47-53, 2008.
  6. [6] M. Ping, Z. Qian, and L. Nannan, “Study of a Dynamic Predictive PID Control Algorithm,” 2015 Fifth Int. Conf. on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), pp. 1418-1423, 2015.
  7. [7] J. Guo, G. Wu, and S. Guo, “Fuzzy PID algorithm-based motion control for the spherical amphibious robot,” 2015 IEEE Int. Conf. on Mechatronics and Automation (ICMA), pp. 1583-1588, 2015.
  8. [8] T. Tateno and H. Nakazawa, “Advanced motion control: From classical PID to nonlinear adaptive robust control,” 2010 11th IEEE Int. Workshop on Advanced Motion Control, pp. 815-829, 2010.
  9. [9] T. Yamamoto, K. Takao, and T. Yamada, “Design of a Data-Driven PID Controller,” IEEE Trans. on control system Technology, Vol.17, No.1, pp. 29-39, 2009.
  10. [10] S. Hou and S. Jin, “A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems,” IEEE Trans. on control system, Vol.19, No.6, pp. 1549-1558, 2011.
  11. [11] R. Thawonmas, M. Iwata, and S. Fukunaga, “A Novel Parallel Model for Self-Organizing Map and its Efficient Implementation on a Data-Driven Multiprocessor,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.7, No.3, pp. 355-361, 2003.
  12. [12] M. Miska and M. Kuwahara, “Sustainable management of data driven projects,” 2010 13th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), pp. 689-693, 2010.
  13. [13] M. Dikmen, D. Hoisen, and T. S. Huang, “A data driven method for feature transformation,” 2012 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 3314-3321, 2012.
  14. [14] N. N. Nandola and D. E. Rivera, “Model-on-Demand predictive control for nonlinear hybrid systems with application to adaptive behavioral interventions,” 2010 49th IEEE Conf. on Decision and Control (CDC), pp. 6113-6118, 2010.
  15. [15] S. Wakitani, T. Nawachi, G. R. Martins, and T. Yamamoto, “Design and Implementation of a Data-Oriented Nonlinear PID Controller,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.17, No.5, pp. 690-698, 2013.
  16. [16] S. Masuda, M. Kano, and Y. Yasuda, “A fictitious reference iterative tuning method with simultaneous delay parameter tuning of the reference model,” Int. Conf. on Networking, Sensing and Control 2009 (ICNSC ’09), pp. 422-427, 2009.
  17. [17] M. Kano, K. Tasaka, M. Ogawa, and A. Takinami, “Extended fictitious reference iterative tuning and its application to chemical processes,” 2011 Int. Symposium on Advanced Control of Industrial Processes (ADCONIP), pp. 379-384, 2011.
  18. [18] O. Kaneko, S. Souma, and T. Fujii, “A fictitious reference iterative tuning (frit) in the two-degree of freedom control scheme and its application to closed loop system identification,” Proc. of 16th IFAC World congress (CD-ROM), 2005.
  19. [19] O. Kaneko, “Fictitious reference iterative tuning of internal model controllers for a class of nonlinear systems,” 2015 IEEE Conf. on Control Applications (CCA), pp. 88-94, 2005.
  20. [20] S. Wakitani, K. Nishida, M. Nakamoto, and T. Yamamoto, “Design of a Data-Driven PID Controller Using Operating Data,” 11th IFAC Int. Workshop on Adaptation and Learning in Control and Signal Processing, pp. 587-592, 2013.
  21. [21] S. Wakitani and T. Yamamoto, “Design and application of a data-driven PID controller,” IEEE Conf. on Control Applications (CCA), pp. 1443-1448, 2014.
  22. [22] S. Masuda, “Data-driven PID gain tuning for liquid level control of a single tank based on disturbance attenuation fictitious reference iterative tuning,” 2015 15th Int. Conf. on Control, Automation and Systems (ICCAS), pp. 16-20, 2014.
  23. [23] H. T. Nguyen, O. Kaneko, and S. Yamamoto, “Data-driven parameter tuning of IMC for unstable plants,” 2012 2nd Australian Control Conf. (AUCC), pp. 92-97, 2012.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 19, 2024