On Control-specific Derivation of Affine Takagi-Sugeno Models from Physical Models: Assessment Criteria and Modeling Procedure
A. Kroll, and A. Dürrbaum. CICA 2011 IEEE Symposium on Computational Intelligence in Control and Automation, page 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.