Abstract
As the pioneer of model reference adaptive control (MRAC) method, MIT control strategy is still used in various practical applications. In this paper, MIT is applied to the speed control of ultrasonic motor, trying to use a relatively simple control method to obtain good control performance. However, MIT control strategy only adjusts the gain, so it is difficult to achieve a large correction of the system’s dynamic characteristics, which limits the actual performance. To solve this problem, two improved MIT control strategies based on iterative learning are proposed in this paper to enhance the control performance. Both methods adopt the P-type iterative learning control (P-ILC) strategy with simplest structure. One is to connect the P-ILC controller with the MIT controller in series to adjust the given value of the MIT controller in real time. The other is to use the P-ILC controller to adjust the adaptive gain of the MIT controller in real time, so as to enhance its control freedom and adaptive ability to deal with complex objects. The experimental results show that the proposed control strategies have their own advantages and can significantly improve the control performance after finite iterative learning processes.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
M. Suhaib, P. Chen, F. U. Karim, and S. Muhammad, “Leader following speed synchronisation in multiple DC motor system using a hybrid controller,” International Journal of Dynamical Systems and Differential Equations, vol. 9, no. 1, pp. 65–86, February 2019.
M. Eva, P. Tanmay, and P. B. Jaganatha, “Control of a coupled CSTR process using MRAC-MIT rule,” Proc. of the 2019 Innovations in Power and Advanced Computing Technologies, pp. 1–6, March 2019.
G. Harsh and S. Pankaj, “Signal synthesis model reference adaptive controller with artificial intelligent technique for a control of continuous stirred tank reactor,” International Journal of Chemical Reactor Engineering, vol. 17, no. 2, pp. 1–8, February 2019.
K. C. S. Thampatty, “Design of MRAC based TCSC for damping sub-synchronous oscillations in SCIG based wind farm,” Proc. of the IEEE Region 10 Annual International Conference, pp. 1846–1852, October 2019.
J. Ling, F. Zhao, M. Ming, and X. H. Xiao, “Model reference adaptive damping control for a nanopositioning stage with load uncertainties,” Review of Scientific Instruments, vol. 90, no. 4, p. 045101, April 2019.
Z. A. Ali, D. B. Wang, A. Muhammad, and M. Suhaib, “MRAC base robust RST control scheme for the application of UAV,” International Journal of Modelling Identification & Control, vol. 28, no. 3, pp. 232–244, June 2017.
S. Arimoto, S. Kawamura, and F. Miyazaki, “Bettering operation of dynamic systems by learning: A new control theory for servomechanism or mechatronic system,” Proc. of the IEEE Conference on Decision and Control, pp. 1064–1069, April 1984.
K. Wan and X. D. Li, “Iterative learning control for two-dimensional linear discrete systems with Fornasini-Marchesini model,” International Journal of Control, Automation, and Systems, vol. 15, no. 4, pp. 1710–1719, August 2017.
X. Deng, X. Sun, and S. Liu, “Iterative learning control for leader-following consensus of nonlinear multi-agent systems with packet dropout,” International Journal of Control, Automation, and Systems, vol. 17, no. 8, pp. 2135–2144, August 2019.
B. You, “Normalized learning rule for iterative learning control,” International Journal of Control, Automation, and Systems, vol. 16, no. 3, pp. 1379–1389, June 2018.
F. Memon and C. Shao, “An optimal approach to online tuning method for PID type iterative learning control,” International Journal of Control, Automation, and Systems, vol. 18, no. 8, pp.1926–1935, August 2020.
P. Gu and S. Tian, “P-type iterative learning control with initial state learning for one-sided Lipschitz nonlinear systems,” International Journal of Control, Automation, and Systems, vol. 17, no. 9, pp. 2203–2210, September 2019.
M. Hamidaoui, C. Shao, and S. Haouassi, “A PD-type iterative learning control algorithm for one-dimension linear wave equation,” International Journal of Control, Automation, and Systems, vol. 18, no. 4, pp. 1045–1052, April 2020.
S. Hao, T. Liu, and F. Gao, “PI based indirect-type iterative learning control for batch processes with time-varying uncertainties: A 2D FM model based approach,” Journal of Process Control, vol. 78, no. 6, pp. 57–67, June 2019.
J. Shi and W. Huang, “Predictive iterative learning speed control with on-line identification for ultrasonic motor,” IEEE Access, vol. 8, pp. 78202–78212, April 2020.
J. Shi and D. You, “Characteristic model of travelling wave ultrasonic motor,” Ultrasonic, vol. 54, no. 2, pp. 725–730, September 2013.
J. Shi and J. Zhao, “Identification of ultrasonic motor’s nonlinear hammerstein model,” Journal of Control, Automation and Electrical Systems, vol. 25, no. 5, pp. 537–546, October 2014.
J. Shi and W. Huang, “Improved DEA for motor’s model identification,” COMPEL-The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 38, no. 6, pp. 1846–1854, October 2019.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Xiao Song received her B.S. degree in electronic and information engineering from Henan University of Science and Technology in 2003, and an M.S. degree in microelectronics and solid-state electronics from Beijing Institute of Technology in 2006. She is currently pursing a Ph.D. degree in control science and engineering with Henan University of Science and Technology. Her research interests are in area of motor control.
Jingzhuo Shi received his B.E., M.E., and Ph.D. degrees in electrical engineering from Harbin Institute of Technology, in 1995, 1997, and 2001, respectively. He is currently a Professor with the Department of Electrical Engineering, Henan University of Science and Technology. His research interests are in area of motor control.
Rights and permissions
About this article
Cite this article
Song, X., Shi, J. New MIT Control Strategy Combined with Iterative Learning Control. Int. J. Control Autom. Syst. 20, 2413–2424 (2022). https://doi.org/10.1007/s12555-020-0986-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12555-020-0986-4