Fuzzy Iterative Learning Control of Servo System

Abstract:

According to the repeatability characteristics of servo system, a fuzzy iterative learning controller is proposed which combining advantages of fuzzy control and iterative learning control. Fuzzy control has better robustness, and does not require accurate model of the system, only need the previous experience, the design method is simple. Fuzzy controller is used in the position-loop, but the static error is difficult to eliminate, iterative learning controller use the control error to adjust the previous control input to reduce the error of next time. The combination of these two intelligent control algorithms can improve the control performance of servo system effectively. Simulation results show that the fuzzy control combined with iterative learning control can achieve better control performance in servo system.

• Periodical: Advanced Materials Research (Volumes 217-218)
• Pages: 917-923
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.217-218.917
• Online since: March 2011
• Authors: Hong Li Liu, Qi Xin Zhu, Xiang Wen Chen
• Keywords: Fuzzy Iterative Learning Control, Permanent Magnet Synchronous Motor (PMSM), Servo-System, Simulink Simulation

References

[1] Ying Luo, YangQuan Chen, and YouGuo Pi: Authentic simulation studies of periodic adaptive learning compensation of cogging effect in PMSM position servo system. Proceeding of the Chinese Conference on Decision and Control, Yantai, Shandong, China, pp.4760-4765, (2008).

DOI: 10.1109/ccdc.2008.4598233

[2] Bin Zhang, Danwei Wang, Yongqiang Ye: Stability and robustness analysis of cyclic pseudo-downsampled iterative learning control. International journal of control, Vol. 83(3), pp.651-659, (2010).

DOI: 10.1080/00207170903373753

[3] Freeman C T, Lewin P L: Rogers E, et al. Iterative learning control applied to a gantry robot and conveyor system. Transactions of the Institute of Measurement & Control, Vol. 32(3), pp.251-264, (2010).

DOI: 10.1177/0142331209104155

[4] Visioli Antonio, Ziliani Giacomo, Legnani Giovanni: Iterative Learning Hybrid Force/Velocity Control for Contour Tracking. IEEE Transactions on Robotics, Vol. 26(2), pp.388-393, (2010).

DOI: 10.1109/tro.2010.2041265

[5] Liu Tao, Gao Furong: Robust two-dimensional iterative learning control for batch processes with state delay and time-varying uncertainties. Chemical Engineering Science, Vol. 65(23), pp.6134-6144, (2010).

DOI: 10.1016/j.ces.2010.08.031

[6] Bing Chu, Owens, David H: Iterative learning control for constrained linear systems. International Journal of Control, Vol. 83(7), pp.1397-1413, (2010).

DOI: 10.1080/00207171003758752

[7] Jia Zhenyuan, Lu Xiaohong, Yang Jiangyuan: Self-learning fuzzy control of scan tracking measurement in copying manufacture. Transactions of the Institute of Measurement & Control, Vol. 32(3), pp.307-318, (2010).

DOI: 10.1177/0142331209342907

[8] Feipeng Da: Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction. Neural Computing & Applications, Vol. 17(5/6) , pp.531-539, (2008).

DOI: 10.1007/s00521-007-0130-x

[9] Yordanova S: Robust performance design of singleinput fuzzy control system for plant with time delay. Transactions of the Institute of Measurement & Control, Vol. 31(5), pp.381-399, (2009).

DOI: 10.1177/0142331208095563

[10] Precup R E, Preitl S, Tar J K: Fuzzy Control System Performance Enhancement by Iterative Learning Control. IEEE Transactions on Industrial Electronics, Vol. 55(9), pp.3461-3475, (2008).

DOI: 10.1109/tie.2008.925322