On Control-specific Derivation of Affine Takagi-Sugeno Models from
Physical Models: Assessment Criteria and Modeling Procedure
A. Kroll, und A. Dürrbaum. CICA 2011 IEEE Symposium on Computational Intelligence in Control
and Automation, Seite 23--30. Paris, France, IEEE Symposium Series on Computational Intelligence, (April 2011)
Models are commonly derived and their performance is assessed wrt.
minimal prediction error on a closed data set. However, if no perfect
model can be used, the degrees of freedom in modeling should be used
to adjust the model to application-specific metrics. For model-based
controller design, control-oriented performance metrics (e.g. performance
wrt. to control-critical properties) are important, but not primarily
prediction (i.e. prognosis- and simulation-oriented) ones. This motivates
the derivation of control-specific models. The contribution introduces
structured and quantitative measures on "model suitability for control"
for the class of affine dynamic Takagi-Sugeno models. A method is
suggested that derives control-specific dynamic models from a physical
model given as a set of nonlinear differential equations. Within
a case study, the proposed method demonstrates its significance:
Using control-specific models improves control performance metrics
such as set-point tracking quality, stability region and energy efficiency.