Lokale Modellnetze zur Identifikation und Versuchsplanung nichtlinearer Systeme

This thesis proposes new approaches for experimental, data-based modeling (identification) and for experimental design of nonlinear systems based on local model networks.

On the one hand, the proposed algorithms are based on a detailed analysis and further development of the linear optimization for locally weighted, polynomial models. This concerns parameter estimation, regularization, validation and subset selection. On the other hand, nonlinear optimization methods are required in order to adapt the validity regions of the local models to the underlying nonlinear process. This is achieved by the development of heuristic partitioning strategies that incrementally subdivide the input space. These methods enable an efficient adaptation of local model networks to the given data. The basic idea of the investigated approaches is the application of flexible axes-oblique partitioning strategies that are well-suited for higher-dimensional problems. In addition, a strategy is proposed that applies a structure trade-off where the model is improved either by performing an axes-oblique split or by increasing the number of regressors to be selected for the local sub-models. The partitioning as well as the local selection of the significant polynomial terms are carried out simultaneously.

Furthermore, the newly developed "hierarchical local model tree" (HILOMOT) algorithm is extended for the application as an experimental design approach. Next to an offline strategy for design of experiments (DoE) the algorithm is enhanced with active learning strategies in order to perform online measurements of processes where no prior knowledge is available. The active learning algorithm is able to interact with the process and can meet the requirements to efficiently place the measurement points.

The proposed algorithms are verified on real system applications where their performance has been demonstrated. That incorporates the modeling of the fuel consumption and emissions of a modern diesel engine as well as the active learning of damage locations in the framework of a structural health monitoring system. Moreover, an adaptive online experimental design is implemented on an engine test bed for the calibration of a diesel engine.