Real-time hardware ANN-QFT robust controller for reconfigurable micro-machine tool

The International Journal of Advanced Manufacturing Technology, January 2015. Pedro Ponce1 , Arturo Molina1, Hector Bastida 1, Brian MacCleery 2

  1. Graduate School of Engineering, Tecnológico de Monterrey, Campus Ciudad de México, 14380, Mexico City, Mexico.
  2. National Instruments, Austin, TX, USA.


This paper shows a reconfigurable micro-machine tool (RmMT) controlled by an artificial neural network based on a robust controller with quantitative feedback theory (QFT). In order to improve the performance of the controller, a field programmable gate array (FPGA) was applied. Since micro-machines present parametric uncertainties under different points of operation, linear controllers cannot deal with those uncertainties. The parametric uncertainties of a micro-machine could be described by a set of linear transfer functions in frequency domain to generate a complete model of the micro-machine; this family of transfer functions can be used for designing a robust controller based on QFT. Although robust control based on QFT is an attractive control methodology for dealing with parametric uncertainties in CNC micro-machines, the real-time FPGA implementation is difficult because robust controllers require a complex discrete representation. In contrast, artificial neural networks (ANNs) work with basic elements (neurons) and run using a parallel topology. Moreover, they are described by simple representation, so the real-time FPGA implementation of ANN controller is a better alternative than the QFT controller.  On the other hand, artificial  neural networks can generalize , so they can cover several work conditions. This paper shows the complete design and the implementation   in LabVIEW FPGA. The proposed ANN-QFT controller gives excellent results for controlling the CNC micro-machine tool during the transitory response.

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