Experimental-Based TGPID Motion Control for 2D CNC Machine

Article Preview

Abstract:

2D (two-dimensional) motion is the basic motion for computer numerical controlled (CNC) machine in all industrial applications. In this paper, it is aimed to optimize the multi-performance characteristics, namely roundness error determined by best-fit-circle (REB), actual radius (R_act) and position time (Tt) that is the time needed for making a circular motion. By applying a Taguchi Grey Proportional Integral Derivative (TGPID) control method, the performance of this 2D multi linear motion is improved. The roundness error is closed to zero as time went to infinity which means the actual radius is closed to the reference radius. The position time differences (dTt) of X and Y axis for circling is also zero. This indicated the TGPID approach is robust.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

511-516

Citation:

Online since:

January 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Yang P, Li Q (2007). Design and Realization of Grey PID Control System for High-Precision Simulated Turntable. Proceed. of IEEE Int. Conf. on Grey Systems and Intelligent Services, Nanjing, China, pp.881-886.

DOI: 10.1109/gsis.2007.4443400

Google Scholar

[2] Fischer M, Tomizuka M (1996) Application and Comparison of Alternative Position Sensors in High-Accuracy Control of an X-Y Table. Proceed. of IEEE Int. Workshop on Advanced Motion Control AMC, Mie, Japan, 2: 494-499.

DOI: 10.1109/amc.1996.509298

Google Scholar

[3] Liu ZZ, Luo FL, Rashid MH (2004) Robust high speed and high precision linear motor direct-drive XY-table motion system. Proceed. of IEE Control Theory and Applications, 151(2): 166-173.

DOI: 10.1049/ip-cta:20040105

Google Scholar

[4] Duelger LC, Kirecci A (2007) Motion Control and Implementation for an AC Servomotor System. Modelling and Simulation in Engineering, Hindawi Publishing Corporation, Article ID 50586, Doi: 10. 1155/2007/50586.

Google Scholar

[5] Ping XL, Liu SL, Li JL, Zhou RR (2007) Application of grey prediction for predicting the measuring point for a CMM. Int J Adv Manuf Tech, 32: 288-292.

DOI: 10.1007/s00170-005-0335-z

Google Scholar

[6] Wei LS, Fei MR (2007) A Real-Time Optimization Grey Prediction Method for Delay Estimation in NCS. Proced. of IEEE Int. Conf. on Control and Automation, Guangzhou, China, pp.514-517.

DOI: 10.1109/icca.2007.4376409

Google Scholar

[7] Peng H (2006) A Data Mining Approach Based on Grey Prediction Model in Web Environment. Proceed. of the 2nd Int. Conf. on Semantics, Knowledge, and Grid (SKG'06), Guilin, Guanxi, China, , pp.76-76, doi: 10. 1109/SKG. 2006. 2.

DOI: 10.1109/skg.2006.2

Google Scholar

[8] Wu WY, Chen SP (2005) A prediction method using the grey model GMC(1, n) combined with the grey relational analysis: a case study on Internet access population forecast. Applied Mathematics and Computation, 169: 198-217.

DOI: 10.1016/j.amc.2004.10.087

Google Scholar