Abstract
The performance of many industrial control systems is determined to a large extent by the quality of both setpoint and disturbance feedforward signals. The quality that is required for a high tracking performance is generally not achieved when the controller parameters are determined on the basis of a detailed model of the plant dynamics or manual tuning. This chapter shows that the optimization of the controller parameters by iterative trials, i.e., data-driven, in both parametric and non-parametric feedforward control structures avoids the need for a detailed model of the plant dynamics, achieves optimal controller parameter values, and allows for the adaptation to possible variations in the plant dynamics. Two industrial applications highlight the large benefits of the data-driven optimization approach. The optimization of the feedforward controller parameters in a wafer scanner application leads to extremely short settling times and higher productivity. The optimization of the current amplifier setpoints in a digital light projection (DLP) application leads to nearly constant color rendering performances of the projection system in spite of large changes in the lamp dynamics over its life span.
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References
Arimoto, S.: Mathematical theory of learning with applications to robot control. In: K.S. Narendra (ed.) Adaptive and Learning Systems: Theory and Applications, pp. 379–388. Plenum Press, New York (1986)
Bien, Z., Xu, J.X.: Iterative Learning Control – Analysis, Design, Integration, and Applications. Kluwer Academic Publishers, Boston (1998)
Boerlage, M., Steinbuch, M., Lambrechts, P., Van de Wal, M.: Model-Based Feedforward for Motion Systems. In: Proceedings of the 2003 IEEE Conference on Control Applications, vol. 2, pp. 1158–1163. Istanbul, Turkey (2003)
Boerlage, M., Tousain, R., Steinbuch, M.: Jerk Derivative Feedforward Control for Motion Systems. In: Proceedings of the 2004 American Control Conference, vol. 5, pp. 4843–4848. Boston, Massachusetts (2004)
Bristow, D.A., Tharayil, M., Alleyne, A.G.: A Survey of Iterative Learning Control – A Learning-Based Method for High-Performance Tracking Control. IEEE Control Systems Magazine 26(3), 96–114 (2006)
De Roover, D.: Motion Control of a Wafer Stage: A Design Approach for Speeding Up IC Production. Ph.D. Thesis, Delft University of Technology, Delft, The Netherlands (1997)
Dennis Jr., J.E., Schnabel, R.B.: Numerical Methods for Unconstrained Optimization and Nonlinear Equations. No. 16 in Classics in Applied Mathematics. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, Pennsylvania (1996)
Devasia, S.: Should Model-Based Inverse Inputs be Used as Feedforward Under Plant Uncertainty. IEEE Transactions on Automatic Control 47(11), 1865–1871 (2002)
Dijkstra, B.G.: Iterative Learning Control, With Applicatins to a Wafer-Stage. Ph.D. Thesis, Delft University of Technology, Delft, The Netherlands (2004)
Frueh, J.A., Phan, M.Q.: Linear Quadratic Optical Learning Control (LQL). Journal of Control 73(10), 832–839 (2000)
Ghosh, J., Paden, B.: A pseudoinverse-based iterative learning control. IEEE Transcations on Automatic Control 47(5), 831–837 (2002)
Hägglund, T., Åström, K.J.: Industrial Adaptive Controllers Based on Frequency Response Techniques. Automatica 27(4), 599–609 (1991)
Hunt, L.R., Meyer, G., Su, R.: Noncausal Inverses for Linear Systems. IEEE Transcations on Automatic Control 41(4), 608–611 (1996)
Kailath, T.: Linear Systems. Prentice-Hall Information and System Sciences Series. Prentice-Hall, Englewood Cliffs, New Jersey (1980)
Lambrechts, P., Boerlage, M., Steinbuch, M.: Trajectory Planning and Feedforward Design for Electromechanical Motion Systems. Control Engineering Practice 13(2), 145–157 (2005)
Longman, R.W.: Iterative Learning Control and Repetitive Control for Engineering Practice. International Journal of Control 73(10), 930–954 (2000)
Van der Meulen, S.H., Tousain, R.L., Bosgra, O.H.: Fixed Strucutre Feedforward Controller Design Exploiting Iterative Trials: Application to a Wafer Stage and a Desktop Printer. Journal of Dynamic Systems, Measurement, and Control 130(051006) (2008)
Moore, K.L.: Iterative learning Control: An expository overview. In: B.N. Datta (ed.) Applied and Computational Control, Signals, and Circuits, vol. 1, chapter 4, pp. 151–214. Birkhäuser, Boston (1999)
Nash, S.G., Sofer, A.: Linear and Nonlinear Programming. McGraw-Hill Series in Industrial Engineering and Management Science. McGraw-Hill, London (1996)
Phan, M., Longman, R.W.: A Mathematical Theory of Learning Control for Linear Discrete Multivariable Sustems. In: Proceedings of the AIAA/AAS Astrodynamics Conference, pp. 740–746. Minneapolis, Minnesota (1988)
Steinbuch, M., Norg, M.L.: Advanced Motion Control: An Industrial Perspective. European Journal of Control 4(4), 278–293 (1998)
Stix, G.: Trends in Semiconductor Manufacturing: Toward “Point One”. Scientific American 272(2), 72–77 (1995)
Tomizuka, M.: Zero Phase Error Tracking Algorithm for Digital Control. Journal of Dynamic Systems, Measurement, and Control 109(1), 65–68 (1987)
Torfs, D.E., Vuerinckx, R., Swevers, J., Schoukens, J.: Comparison of Two Feedforward Design Methods Aiming at Accurate Trajectory Tracking of the End Point of a Flexible Robot Arm. IEEE Transactins on Control Systems Technology 6(1), 2–14 (1998)
Tousain, R., Van der Meché, E., Bosgra, O.: Design Strategy for Iterative Learning Control Based on Optimal Control. In: Proceedings of the 40th IEEE Conference on Decision and Control, vol. 5, pp. 4463–4468. Orlando, Florida (2001)
Tousain, R., Van Casteren, D.: Iterative Learning Control in a Mass Product: Light on Demand in DLP projection systems. In: Proceedings of the 2007 American Control Conference, pp. 5478–5483. New York City, New York (2007)
Tsao, T.C., Tomizuka, M.: Robust Adaptive and Repetitive Digital Tracking Control and Application to a Hydraulic Servo for Nancircular Machining. Journal of Dynamic Systems, Measurement, and Control 116(1), 24–32 (1994)
Van de Wal, M., Van Baars, G., Sperling, F., Bosgra, O.: Multivaribale H ∞/ μ Feedback Control Degign for High-Precision Wafer Stage Motion. Control Engineering Practive 10(7), 739–755 (2002)
Zhao, S., Tan, K.K.: Adaptive Feedforward Compensation of Force Ripples in Linear Motors. Control Engineering Practice 13(9), 1081–1091 (2005)
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Tousain, R., van der Meulen, S. (2009). Advances in Data-driven Optimization of Parametric and Non-parametric Feedforward Control Designs with Industrial Applications. In: Hof, P., Scherer, C., Heuberger, P. (eds) Model-Based Control:. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0895-7_10
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