This paper presents a block oriented nonlinear dynamic model suitable for
online identi cation.The model has the well known Hammerstein architecture
where as a novelty the nonlinear static part is represented by a B-spline
neural network (BSNN), and the linear static one is formalized by an auto
regressive exogenous model (ARX). The model is suitable as a feed-forward
control module in combination with a classical feedback controller to regulate
velocity and position of pneumatic and hydraulic actuation systems
which present non stationary nonlinear dynamics. The adaptation of both
the linear and nonlinear parts is taking place simultaneously on a patterby-
patter basis by applying a combination of error-driven learning rules and
the recursive least squares method. This allows to decrease the amount of
computation needed to identify the model's parameters and therefore makes
the technique suitable for real time applications. The model was tested with
a silver box benchmark and results show that the parameters are converging
to a stable value after 1500 samples, equivalent to 7.5s of running time.
The comparison with a pure ARX and BSNN model indicates a substantial
improvement in terms of the RMS error, while the comparison with alternative
non linear dynamic models like the NNOE and NNARX, having the
same number of parameters but greater computational complexity, shows
comparable performances
