Zur Identifikation mechatronischer Stellglieder mit Reibung bei ...

Abstract
ABSTRACT
This work investigates modeling of mechatronic systems with friction. Proper dynamic mo-
dels are fundamental for various tasks. Once dynamic process models are available, model-
based analysis, design and testing methods can be applied. However, in practice the effort
for modeling restricts spreading of model-based applications. An automation of the modeling
process is therefore of great interest. In this paper, three kinds of modeling methods for the
class of mechatronic systems with friction will be proposed: semi-physical modeling, sliding-
mode-observer based modeling and empirical modeling using piecewise-affine (PWA) mo-
dels. Firstly, two novel semi-physical modeling methods are presented. Compared with other
approaches for modeling systems with friction, which often require extensive knowledge and
expensive experiments, the proposed methods require little prior knowledge, minimal effort,
few experiments and can be largely automated. Secondly, a novel friction-identification me-
thod using a sliding-mode-observer based friction estimation approach is proposed. Using
a sliding-mode-observer, which is developed based on a simple linear state space model,
the friction can be reconstructed from simple open-loop experiments. Unlike other methods,
which often demand detailed knowledge of physics and complex measurements, the pro-
posed methods have the advantage that modeling of mechatronic systems with friction can
be carried out with little effort. Thirdly, a novel clustering-based identification method for
PWA-models for systems with friction is presented. Particular feature vectors are chosen for
clustering, in order to capture the friction effects. Furthermore, the classical c-means method
is used, which is easy to use, more efficient and works well also for large data sets. Compa-
red with other PWA modeling methods, this method requires fewer design parameters and
can be more efficiently applied to real systems with friction. Another novelty of the proposed
method is that the prediction error of the model and several cluster validity measures are
combined for choosing the number of submodels. In addition, the proposed method optimi-
zes the parallel model evaluation to improve the model quality. The proposed methods will
be applied to throttles, swirl flaps and EGR-valves of diesel vehicles for the hardware-in-the-
loop (HiL) simulation. Due to their efficiency and effectiveness, the proposed methods are
directly applicable to practice in the automotive industry.
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