A Direct Adaptive MNN Control Method for Stage Having Paired Reluctance Linear Actuator with Hysteresis
Affiliation(s)
1 Department of Electrical and Electronic Engineering, Chiba University, Chiba, Japan. 2 Department of Microelectronics, Fudan University, Shanghai, China.
1 Department of Electrical and Electronic Engineering, Chiba University, Chiba, Japan. 2 Department of Microelectronics, Fudan University, Shanghai, China.
ABSTRACT
Reluctance linear actuator, which has a unique property of small volume, low current and can produce great force, is a very promising actuator for the fine stage of the next-generation lithographic scanner. But the strong nonlinearities including the hysteresis, between the current and output force limits the reluctance linear actuator applications in nanometer positioning. In this paper, a new nonlinear control method is proposed for the stage having paired reluctance linear actuator with hysteresis using the direct adaptive neural network, which is used as a learning machine of nonlinearity. The feature of this method lies in that the nonlinear compensator in conventional methods, which computed the current reference from that of the input and output force is not used. This naturally overcomes the robustness issue with respect to parameter uncertainty. Simulation results show that the proposed method is effective in overcoming the nonlinearity between the input current and output force and promising in precision stage control.
Reluctance linear actuator, which has a unique property of small volume, low current and can produce great force, is a very promising actuator for the fine stage of the next-generation lithographic scanner. But the strong nonlinearities including the hysteresis, between the current and output force limits the reluctance linear actuator applications in nanometer positioning. In this paper, a new nonlinear control method is proposed for the stage having paired reluctance linear actuator with hysteresis using the direct adaptive neural network, which is used as a learning machine of nonlinearity. The feature of this method lies in that the nonlinear compensator in conventional methods, which computed the current reference from that of the input and output force is not used. This naturally overcomes the robustness issue with respect to parameter uncertainty. Simulation results show that the proposed method is effective in overcoming the nonlinearity between the input current and output force and promising in precision stage control.
Cite this paper
Liu, Y. , Liu, K. and Yang, X. (2014) A Direct Adaptive MNN Control Method for Stage Having Paired Reluctance Linear Actuator with Hysteresis. Intelligent Control and Automation, 5, 213-223. doi: 10.4236/ica.2014.54023.
Liu, Y. , Liu, K. and Yang, X. (2014) A Direct Adaptive MNN Control Method for Stage Having Paired Reluctance Linear Actuator with Hysteresis. Intelligent Control and Automation, 5, 213-223. doi: 10.4236/ica.2014.54023.
References
[1] Butler, H. (2011) Position Control in Lithographic Equipment. IEEE Control Systems Magazine, 31, 28-47.
http://dx.doi.org/10.1109/MCS.2011.941882 [2] Intel Technology-News (2014) Toshiba Starts Mass Production of World’s First 15 nm NAND Flash Memories.
http://phys.org/news/2014-04-toshiba-mass-production-world-15nm.html [3] Vrijsen, N.H. and Jansen, J.W. (2010) Comparison of Linear Voice Coil and Reluctance Actuators for High-Precision Applications. Power Electronics and Motion Control Conference (EPE/PEMC), 2010 14th International, S3-S29. [4] Teng, T.C. and Yuan, B. (2000) Magnetic Actuator Producing Large Acceleration on Fine Stage and Low RMS Power Gain. US Patent No. 6130517. [5] Chang, P.-W., et al. (2007) E/I Core Actuator Commutation Formula and Control Method. US Patent No. 72535767. [6] Bertotti, G. (1998) Hysteresis in Magnetism: For Physicists, Materials Scientists, and Engineers. Academic Press. New York. [7] Iyer, R.V. and Tan, X. (2009) Control of Hysteretic Systems through Inverse Compensation. IEEE Control Systems, 29, 83-99.
http://dx.doi.org/10.1109/MCS.2008.930924 [8] Katalenic, A. and de Boeij, J. (2011) Linearization of the Reluctance Force Actuator Based on the Parametric Hysteresis Inverse and a 2D Spline. Proceedings of the 8th International Symposium on Linear Drives for Industry Applications (LDIA 2011), Eindhoven, 3-6 July 2011. [9] Liu, Y.P., Zhai, L. and Chai, T. (2008) Nonlinear Adaptive PID Control Using Neural Networks and Multiple Models and Its Application. Journal of Chemical Industry and Engineering (China), 59, 1671-1676. [10] Ge, S.S., Hang, C.C., Lee, T.H. and Zhang, T. (2010) Stable Adaptive Neural Network Control. Kluwer, Boston. [11] San, P.P., Ren, B., Ge, S.S., Lee, T.H. and Liu, J.K. (2011) Adaptive Neural Network Control of Hard Disk Drives with Hysteresis Friction Nonlinearity. IEEE Transactions on Control Systems Technology, 19, 351-358.
http://dx.doi.org/10.1109/TCST.2010.2041233 [12] Lin, F.J., Shieh, H.J. and Huang, P.K. (2006) Adaptive Wavelet Neural Network Control with Hysteresis Estimation for Piezo-Positioning Mechanism. IEEE Transactions on Neural Networks, 17, 432-444. [13] Liu, Y.P., Liu, K.Z. and Yang, X.F. (2013) Hysteresis Compensation Control for Reluctance Actuator Force Using Neural Network. The 32nd Chinese Control Conference, Xian, 26-28 July 2013, 3354-3359. [14] Corless, R.M., Gonnet, G.H., Hare, D.E.G., Jeffrey, D.J. and Knuth, D.E. (1996) On the Lambert W Function. Advances in Computational Mathematics, 5, 329-359.
http://dx.doi.org/10.1007/BF02124750 [15] Furlani, E.P. (2001) Permanent Magnet and Electromechanical Devices: Materials, Analysis, and Applications. Academic Press, Waltham. [16] Butler, H. (2013) Adaptive Feedforward for a Wafer Stage in a Lithographic Tool. IEEE Transactions on Control Systems Technology, 21, 875-881. [17] Gong, J.Q. and Yao, B. (1999) Adaptive Robust Control without Knowing Bounds of Parameter Variations. Proceedings of the 38th IEEE Conference on Decision and Control, 4, 3334-3339. [18] Roover, D. (1997) Motion Control of a Wafer Stage. Delft University Press, Delft.
[1] Butler, H. (2011) Position Control in Lithographic Equipment. IEEE Control Systems Magazine, 31, 28-47.
http://dx.doi.org/10.1109/MCS.2011.941882 [2] Intel Technology-News (2014) Toshiba Starts Mass Production of World’s First 15 nm NAND Flash Memories.
http://phys.org/news/2014-04-toshiba-mass-production-world-15nm.html [3] Vrijsen, N.H. and Jansen, J.W. (2010) Comparison of Linear Voice Coil and Reluctance Actuators for High-Precision Applications. Power Electronics and Motion Control Conference (EPE/PEMC), 2010 14th International, S3-S29. [4] Teng, T.C. and Yuan, B. (2000) Magnetic Actuator Producing Large Acceleration on Fine Stage and Low RMS Power Gain. US Patent No. 6130517. [5] Chang, P.-W., et al. (2007) E/I Core Actuator Commutation Formula and Control Method. US Patent No. 72535767. [6] Bertotti, G. (1998) Hysteresis in Magnetism: For Physicists, Materials Scientists, and Engineers. Academic Press. New York. [7] Iyer, R.V. and Tan, X. (2009) Control of Hysteretic Systems through Inverse Compensation. IEEE Control Systems, 29, 83-99.
http://dx.doi.org/10.1109/MCS.2008.930924 [8] Katalenic, A. and de Boeij, J. (2011) Linearization of the Reluctance Force Actuator Based on the Parametric Hysteresis Inverse and a 2D Spline. Proceedings of the 8th International Symposium on Linear Drives for Industry Applications (LDIA 2011), Eindhoven, 3-6 July 2011. [9] Liu, Y.P., Zhai, L. and Chai, T. (2008) Nonlinear Adaptive PID Control Using Neural Networks and Multiple Models and Its Application. Journal of Chemical Industry and Engineering (China), 59, 1671-1676. [10] Ge, S.S., Hang, C.C., Lee, T.H. and Zhang, T. (2010) Stable Adaptive Neural Network Control. Kluwer, Boston. [11] San, P.P., Ren, B., Ge, S.S., Lee, T.H. and Liu, J.K. (2011) Adaptive Neural Network Control of Hard Disk Drives with Hysteresis Friction Nonlinearity. IEEE Transactions on Control Systems Technology, 19, 351-358.
http://dx.doi.org/10.1109/TCST.2010.2041233 [12] Lin, F.J., Shieh, H.J. and Huang, P.K. (2006) Adaptive Wavelet Neural Network Control with Hysteresis Estimation for Piezo-Positioning Mechanism. IEEE Transactions on Neural Networks, 17, 432-444. [13] Liu, Y.P., Liu, K.Z. and Yang, X.F. (2013) Hysteresis Compensation Control for Reluctance Actuator Force Using Neural Network. The 32nd Chinese Control Conference, Xian, 26-28 July 2013, 3354-3359. [14] Corless, R.M., Gonnet, G.H., Hare, D.E.G., Jeffrey, D.J. and Knuth, D.E. (1996) On the Lambert W Function. Advances in Computational Mathematics, 5, 329-359.
http://dx.doi.org/10.1007/BF02124750 [15] Furlani, E.P. (2001) Permanent Magnet and Electromechanical Devices: Materials, Analysis, and Applications. Academic Press, Waltham. [16] Butler, H. (2013) Adaptive Feedforward for a Wafer Stage in a Lithographic Tool. IEEE Transactions on Control Systems Technology, 21, 875-881. [17] Gong, J.Q. and Yao, B. (1999) Adaptive Robust Control without Knowing Bounds of Parameter Variations. Proceedings of the 38th IEEE Conference on Decision and Control, 4, 3334-3339. [18] Roover, D. (1997) Motion Control of a Wafer Stage. Delft University Press, Delft.