Learning Speed Enhancement of Iterative Learning Control with Advanced Output Data based on Parameter Estimation

Authors

  • Gu-Min Jeong Kookmin University
  • Sang-Hoon Ji Convergent Technology R&D Division, KITECH

Keywords:

iterative learning control, speed enhancement, parameter estimation, learning gain estimation

Abstract

Learning speed enhancement is one of the most important issues in learning control. If we can improve both learning speed and tracking performance, it will be helpful to the applicability of learning control. Considering these facts, in this paper, we propose a learning speed enhancement scheme for iterative learning control with advanced output data (ADILC) based on parameter estimation. We consider linear discrete-time non-minimum phase (NMP) systems, whose model is unknown, except for the relative degree and the number of NMP zeros. In each iteration, estimates of the impulse response are obtained from input-output relationship. Then, learning gain matrix is calculated from the estimates, and by using new learning gain matrix, learning speed can be enhanced. Simulation results show that the learning speed has been enhanced by applying the proposed method.

References

Arimoto S., Kawamura S., Miyazaki F. (1984); Bettering operation of robots by learning, Journal of Robotic Systems, 1(2), 123-140, 1984. https://doi.org/10.1002/rob.4620010203

Bien Z., Xu J.-X. (1998); Iterative learning control analysis, design, integration and applications, Kluwer Academic Publishers, 1998.

Jang T.-J., Ahn H.-S., Choi C.-H. (1994); Iterative learning control for discrete-time nonlinear systems, International Journal of Systems Science, 25(7): 1179-1189. https://doi.org/10.1080/00207729408949269

Jeong G.-M., Choi C.-H. (2002); Iterative learning control for linear discrete time nonminimum phase systems, Automatica, 38(2), 287-291, 2002. https://doi.org/10.1016/S0005-1098(01)00197-2

Jeong G.-M., Ji S.-H. (2013); Iterative learning control with advanced output data using an estimation of the impulse response, IEICE Transactions on Fundamentals, E96-A (6), 1488-1491, 2013. https://doi.org/10.1587/transfun.E96.A.1488

Ngo T., Wang Y., Mai T.L., Ge J., Nguyen M.H., Wei S. N. (2012); An adaptive iterative learning control for robot manipulator in task space, International Journal of Computers Communications & Control, 7(3), 518-529, 2012.

Uchiyama M. (1978); Formulation of high-speed motion pattern of mechanical arm by trial, Transactions of the Society of Instituteument and Control Engineers (in Japanese), 14(6), 706-712, 1978. https://doi.org/10.9746/sicetr1965.14.706

Xia C., Deong W., Shi T., Yan Y. (2016); Torque ripple minimization of PMSM using parameter optimization based iterative learning control, Journal of Electrical Engineering and Technology, 11(2), 709-718, 2016.

Xu J.-X. (1997); Direct learning of control efforts for trajectories with different magnitude scales, Automatica, 33(12), 2191-2195, 1997. https://doi.org/10.1016/S0005-1098(97)00140-4

Yamada M., Riadh Z., Funahashi Y. (1999); Design of discrete-time repetitive control system for pole placement and application,IEEE/ASME Transactions on Mechatronics, 4(2), 110- 118, 1999. https://doi.org/10.1109/3516.769538

Published

2017-04-23

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