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.
Keywords:
table lookup,
learning systems,
nonlinear control systems,
adaptive control,
time-varying systems,
regression analysis,
identification,
internal combustion engines,
feedforward,
gradient methods,
convergence,
Look-up Tables,
On-Line Adaptation,
Recursive Least Squares,
Spark Ignition Engine
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