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Machine tool control with additional measurement for increasing the control system dynamics

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Języki publikacji
EN
Abstrakty
EN
As a typical type of controller in the area of machine tools the classical cascade controller is used. It consists of several PI control loops and allows the position control of the machine tool. This type of controller is easy to be implemented and gives satisfactory results but only in the case that the sufficiently stiff machine tool is being controlled. If adverse is true the performance of the controller deteriorates. This is due to the fact, that the controller is limited by the structural properties of the machine tool. The bandwidth of the controller is restricted by the position of the first anti-resonant frequency of the machine tool. The control techniques overcoming this limitation have been extensively researched. As a result the control technique employing the additional measurement of TCP, the model-based predictive control and the Kalman filter is used and delivers the increased control system dynamics. The paper deals with the description of the proposed control concept and the practical methods for additional measurement together with the Kalman filter tuning are described. The evaluation of the proposed control concept is based on the experimentally measured data on the machine tool axis with significant flexibility.
Rocznik
Strony
5--16
Opis fizyczny
Bibliogr. 13 poz., rys.
Twórcy
autor
  • Czech Technical University in Prague, Faculty of Mechanical Engineering, Department of Mechanics, Biomechanics and Mechatronics, Karlovonám. 13, Praha 2, Czech Republic
autor
  • Czech Technical University in Prague, Faculty of Mechanical Engineering, Department of Mechanics, Biomechanics and Mechatronics, Karlovonám. 13, Praha 2, Czech Republic
Bibliografia
  • 1. ALSPACH D. L., 1974, A parallel filtering algorithm for linear systems with unknown time-varying noise statistics, IEEE Transactions on Automatic Control, 19/5/552–556.
  • 2. CAMACHO E. F., BORDONS C., 2004, Model Predictive Control, Springer-Verlag.
  • 3. KASHYAP R. L., 1970, Maximum likelihood identification of stochastic linear systems, IEEE Transactions on Automatic Control, 15/1/25–34.
  • 4. MACIEJOWSKI J. M., 2002, Predictive Control with Constraints, Prentice Hall, Englewood Cliffs.
  • 5. MUSKE K. R., BADGWELL T. A., 2002, Disturbance modelling for offset-free linear model predictive control, Journal of Process Control, 12/617-632.
  • 6. MYERS K. A., TAPLEY B. D., 1976, Adaptive sequential estimation with unknown noise statistics, IEEE Transactions on Automatic Control, 21/520–523.
  • 7. ODELSON B. J., RAJAMANI M. R., RAWLINGS J. B., 2006, A new autocovariance least-squares method for estimating noise covariances, Automatica, 42/303-308.
  • 8. PRETT D. M., GARCIA C. E., 1988, Fundamental process control, Butterworths, Boston.
  • 9. QIN S. J., BADGWELL T. A., 1997, An overview of industrial model predictive control technology, Fifth International Conference on Chemical Process Control, AIChE Symposium Series, 93/316/232–256.
  • 10. ROSSITER J. A., 2003, Model-based Predictive Control: A Practical Approach, CRC Press.
  • 11. SOETERBOEK R., 1992, Predictive Control: A Unified Approach, Prentice Hall, Englewood Cliffs.
  • 12. SOUCEK P., 2004, Servomechanisms in production machines, Lecture Notes, Publishing House of CTU, Prague.
  • 13. STRAKOS P., VALASEK M., 2010, Machine tool control with additional measurements for overcoming the anti-resonant frequencies, Applied and Computational Mechanics, 4/1.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1e546c96-dcc5-440e-ae47-ec90a5939639
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