Iterative learning control (ILC) is a repetitive control scheme that uses a learning capability
to improve the tracking accuracy of a desired test system output over repeated test trials. ILC is
sometimes used in response reconstruction on complex engineering structures, such as ground vehicles,
for purposes of fatigue testing. The compensator that is employed in ILC in such cases is traditionally
an approximate, linear inverse model of the closed-loop test system.
This research presents advances in ILC, particularly with respect to its application in response
reconstruction for fatigue testing purposes. The contribution of this research focuses on three aspects:
the use of a nonlinear inverse model in the ILC compensator instead of a linear inverse model; the use
of multiple inverse models, each one defined over a different part of the test frequency band, instead
of one model that covers the entire test frequency band; and the development and use of a new
type of ILC algorithm. The contributions are implemented and demonstrated on a quarter vehicle
road simulator, with favorable results for the use of nonlinear inverse models and multiple inverse
models. The new ILC algorithm is shown to be competitive with the conventional inverse modelbased
algorithm, giving comparable to slightly worse results than the conventional ILC algorithm. In
order to invert the nonlinear inverse models this research also presents advances in the stable inversion
method that is used to invert such models.
Keywords: Iterative learning control, response reconstruction, fatigue testing, NARX models, Kolmogorov-
Gabor polynomials, system identification, stable inversion, nonlinear, discrete time, Picard iteration,
Mann iteration, quarter vehicle road simulator.