Robust Adaptive Neural Network Control for XY Table

Affiliation(s)

Department of Intelligent System Engineering, Graduate School of Dong-eui University, Busan, South Korea.

Department of Mechatronics Engineering, Dong-eui University, Busan, South Korea.

Department of Intelligent System Engineering, Graduate School of Dong-eui University, Busan, South Korea.

Department of Mechatronics Engineering, Dong-eui University, Busan, South Korea.

ABSTRACT

This paper proposed a robust adaptive neural network control for an XY table. The XY table composes of two AC servo drives controlled independently. The neural network with radial basis function is employed for velocity and position tracking control of AC servo drives to improve the system’s dynamic performance and precision. A robust adaptive term is applied to overcome the external disturbances. The stability and the convergence of the system are proved by Lyapunov theory. The proposed controller is implemented in a DSP-based motion board. The validity and robustness of the controller are verified through experimental results.

Cite this paper

N. Giap, J. Shin and W. Kim, "Robust Adaptive Neural Network Control for XY Table,"*Intelligent Control and Automation*, Vol. 4 No. 3, 2013, pp. 293-300. doi: 10.4236/ica.2013.43034.

N. Giap, J. Shin and W. Kim, "Robust Adaptive Neural Network Control for XY Table,"

References

[1] H. Lim, J. W. Seo and C. H. Choi, “Position Control of XY Table in CNC Machining Center with Non-Rigid Ballscrew,” Proceedings of the American Control Con ference, Chicago, 2000, pp. 1542-1546.

[2] E. C. Park, H. Lim and C. H. Choi, “Position Control of X-Y Table at Velocity Reversal Using Presliding Friction Characteristics,” IEEE Transactions on Control Systems Technology, Vol. 11, No. 1, 2003, pp. 24-31. doi:10.1109/TCST.2002.806436

[3] F. J. Lin and P. H. Shen, “Robust Fuzzy Neural Network Sliding-Mode Control for Two-Axis Motion Control Sys tem,” IEEE Transactions on Industrial Electronics, Vol. 53, No. 4, 2006, pp. 1209-1225. doi:10.1109/TIE.2006.878312

[4] Z. Jamaludin, H. Van Brussel and J. Swevers, “Friction Compensation of an XY Feed Table Using Friction-Mo del-Based Feedforward and an Inverse-Model-Based Dis turbance Observer,” IEEE Transactions on Industrial Electronics, Vol. 56, No. 10, 2009, pp. 3848-3853. doi:10.1109/TIE.2009.2017560

[5] Y. T. Kim, “Adaptive Fuzzy Backstepping Control of AC Servo System in the Presence of Nonlinear Dynamic Ef fect and Mechanical Uncertainties,” Automation Congress, WAC, Hawaii, 28 September-2 October 2008, pp. 1-8.

[6] J. Chang, Y. Tan and J. T. Yu, “Backstepping Approach of Adaptive Control, Gain Selection and DSP Implemen tation for AC Servo System,” IEEE Power Electronics Specialists Conference, Orlando, 17-21 June 2007, pp. 535-541.

[7] Y. S. Xiao, Q. D. Wu and G. X. Zhou, “Neural Network Based Parameters Identification and Adaptive Speed Con trol of AC Drive System,” Proceedings of the IEEE In ternational Conference on in Industrial Technology, Shang hai, 2-6 December 1996, pp. 118-121.

[8] Y. X. Su, B. Y. Duan and Y. F. Zhang, “Robust Precision Motion Control for AC Servo System,” Proceedings of the 4th World Congress on Intelligent Control and Auto mation, Vol. 4, 2002, pp. 3319-3323.

[9] P. H. Kim, S. H. Sin, H. L. Baek and G. B. Cho, “Speed Control of AC Servo Motor Using Neural Networks,” Proceedings of the 5th International Conference on Elec trical Machines and Systems, Shenyang, August 2001, pp. 691-694.

[10] K. Hornik, M. Stinchcombe and H. White, “Multilayer Feed-Forward Networks Are Universal Approximator,” Neu ral Networks, Vol. 2, No. 5, 1989, pp. 359-366. doi:10.1016/0893-6080(89)90020-8

[1] H. Lim, J. W. Seo and C. H. Choi, “Position Control of XY Table in CNC Machining Center with Non-Rigid Ballscrew,” Proceedings of the American Control Con ference, Chicago, 2000, pp. 1542-1546.

[2] E. C. Park, H. Lim and C. H. Choi, “Position Control of X-Y Table at Velocity Reversal Using Presliding Friction Characteristics,” IEEE Transactions on Control Systems Technology, Vol. 11, No. 1, 2003, pp. 24-31. doi:10.1109/TCST.2002.806436

[3] F. J. Lin and P. H. Shen, “Robust Fuzzy Neural Network Sliding-Mode Control for Two-Axis Motion Control Sys tem,” IEEE Transactions on Industrial Electronics, Vol. 53, No. 4, 2006, pp. 1209-1225. doi:10.1109/TIE.2006.878312

[4] Z. Jamaludin, H. Van Brussel and J. Swevers, “Friction Compensation of an XY Feed Table Using Friction-Mo del-Based Feedforward and an Inverse-Model-Based Dis turbance Observer,” IEEE Transactions on Industrial Electronics, Vol. 56, No. 10, 2009, pp. 3848-3853. doi:10.1109/TIE.2009.2017560

[5] Y. T. Kim, “Adaptive Fuzzy Backstepping Control of AC Servo System in the Presence of Nonlinear Dynamic Ef fect and Mechanical Uncertainties,” Automation Congress, WAC, Hawaii, 28 September-2 October 2008, pp. 1-8.

[6] J. Chang, Y. Tan and J. T. Yu, “Backstepping Approach of Adaptive Control, Gain Selection and DSP Implemen tation for AC Servo System,” IEEE Power Electronics Specialists Conference, Orlando, 17-21 June 2007, pp. 535-541.

[7] Y. S. Xiao, Q. D. Wu and G. X. Zhou, “Neural Network Based Parameters Identification and Adaptive Speed Con trol of AC Drive System,” Proceedings of the IEEE In ternational Conference on in Industrial Technology, Shang hai, 2-6 December 1996, pp. 118-121.

[8] Y. X. Su, B. Y. Duan and Y. F. Zhang, “Robust Precision Motion Control for AC Servo System,” Proceedings of the 4th World Congress on Intelligent Control and Auto mation, Vol. 4, 2002, pp. 3319-3323.

[9] P. H. Kim, S. H. Sin, H. L. Baek and G. B. Cho, “Speed Control of AC Servo Motor Using Neural Networks,” Proceedings of the 5th International Conference on Elec trical Machines and Systems, Shenyang, August 2001, pp. 691-694.

[10] K. Hornik, M. Stinchcombe and H. White, “Multilayer Feed-Forward Networks Are Universal Approximator,” Neu ral Networks, Vol. 2, No. 5, 1989, pp. 359-366. doi:10.1016/0893-6080(89)90020-8