Advanced control systems require accurate process models, while processes are often both nonlinear and time variant. After introducing the identification of nonlinear processes with grid-based look-up tables, a new learning algorithm for on-line adaptation of look-up tables is proposed. Using a linear regression approach, this new adaptation algorithm considerably reduces the convergence time in relation to conventional gradient-based adaptation algorithms. An application example and experimental results are shown for the learning feedforward control of the ignition angle of a spark ignition engine.
On-Line Adaptation of Grid-Based Look-up Tables Using a Fast Linear Regression Technique
Contributed by the Dynamic Systems, Measurement, and Control Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 21, 2004; final revision, December 21, 2004. Review conducted by: F. Ghorbel.
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Vogt, M., Mu¨ller, N., and Isermann, R. (March 11, 2005). "On-Line Adaptation of Grid-Based Look-up Tables Using a Fast Linear Regression Technique ." ASME. J. Dyn. Sys., Meas., Control. December 2004; 126(4): 732–739. https://doi.org/10.1115/1.1849241
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