Control of ultrahigh-precision magnetic leadscrew using recurrent neural networks

Timothy N. Chang; Tony Wong; Danni Bhaskar; Zhiming Ji; Michael Shimanovich; Reggie Caudill

doi:10.1117/12.332796

17 December 1998 Control of ultrahigh-precision magnetic leadscrew using recurrent neural networks

Timothy N. Chang, Tony Wong, Danni Bhaskar, Zhiming Ji, Michael Shimanovich, Reggie Caudill

Proceedings Volume 3518, Sensors and Controls for Intelligent Machining, Agile Manufacturing, and Mechatronics; (1998) https://doi.org/10.1117/12.332796
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States

Abstract

In this work, the problem of vibration control for a contactless magnetic leadscrew system is considered. A contactless drive system is a magnetic nut/leadscrew and air bearing assembly that operates on the principle of magnetic/aerodynamic suspension to position a load with high accuracy. However, the dynamics of such system is lightly damped, load dependent, and generally difficult to stabilize by conventional linear controllers. Therefore, the technique of recurrent neural network is applied to separate the oscillatory signals so that passband shaping can be carried out to regulate plant dynamics and to reject disturbances. This controller possesses a modular structure and is easy to implement. Experimental results also confirm the vibration suppression capabilities of this controller.

Citation Download Citation

Timothy N. Chang, Tony Wong, Danni Bhaskar, Zhiming Ji, Michael Shimanovich, and Reggie Caudill "Control of ultrahigh-precision magnetic leadscrew using recurrent neural networks", Proc. SPIE 3518, Sensors and Controls for Intelligent Machining, Agile Manufacturing, and Mechatronics, (17 December 1998); https://doi.org/10.1117/12.332796

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