Abstract
The mechanical structure and load changes of nano positioning stage cause a poor accuracy of the control system. For solving the problem, Adaptive PID control algorithm was applied to control the nano positioning stage. The model of nano positioning stage was established on the basis of Controlled Autoregressive Moving Average model (CARMA). The parameters of controller were identified based on Recursive Extended Least Squares algorithm (RELS). The control system of nano positioning stage was steady through 4ms after parameters identification, which static error was less than 5nm. The experimental results demonstrated that adaptive PID control algorithm was able to identify the parameters of controlled object and calculate the parameters of controller. The accuracy of control system can be at nanometer resolution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Xie, X.H., Du, R.X., Qiang, S.: Design and simulation of a nano-scale micro positioning stage. International Journal of Modelling, Identification and Control (2015). doi:10.1504/IJMIC.2009.027019
Jung, S.B., Kim, S.W.: Improvement of scanning accuracy of PZT piezoelectric actuators by feed-forward model-reference control. Precis Eng., 49–55 (1994)
Hanz, R., Eduaroo, A.M., Lucca, A.: Characterization of nonlinearities in a piezoelectric positioning device. In: IEEE International Conference on Control Applications, pp. 717–720 (1997)
Hu, X.D., Hu, X.T., Yong, Y.: Study on the performance of 3D monolithic ultra-micropositioning system. Journal of China Mechanical Engineering 11, 1201–1203 (1999)
Lu, Z.Q., Chen, D.J., Kong, H.F. et al.: Neuron net control and it’s application in precision feed mechanism of machine tool driven by PZT. In: IEEE International Conference on Industrial Technology, pp. 80–82 (1994)
Ma, B., Meng, Q.B., Feng, Y. et al.: The controller design for dc motor speed system based on improved fuzzy PID. In: International Conference on Intelligent System and Applied Material, pp. 466–467, 1246–1250 (2012)
Wang, Y.G., Tian, Z.P.: Adaptive PID controllers with robustness specifications. International Journal of Modelling, Identification and Control 20, 148–155 (2013)
Dai, J., Jing, S., Meng, S., et al.: Design and implementation of filter in the control system of the high accuracy micro-displacement platform. International Journal of Modelling, Identification and Control 21, 82–92 (2014)
Feng, D.: System identification Part D: Auxiliary model identification idea and methods. Journal of Nanjing University of Information Science and Technology: Natural Science Edition 4, 289–318 (2011)
Feng, Z., Lu, Y.H., Feng, Q., et al.: Self-tuning fuzzy adaptive PID pitch control of wind power systems. International Conference on Mechatronics, Applied Mechanics and Energy Engineering 394, 404–409 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dai, J., Wang, T., Shao, M., Qu, P. (2016). Research on Adaptive Control Algorithm of Nano Positioning Stage. In: Kim, J., Geem, Z. (eds) Harmony Search Algorithm. Advances in Intelligent Systems and Computing, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47926-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-662-47926-1_9
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-47925-4
Online ISBN: 978-3-662-47926-1
eBook Packages: EngineeringEngineering (R0)