Die vorliegende Arbeit befasst sich mit der Methode der modalen Superposition als Ordnungsreduktionsverfahren in der Mikrosystemtechnik. Typische Anwendungsgebiete sind Inertialsensoren und dabei im Besonderen Drehratensensoren, für die die Simulation von zeitabhängigen Phänomenen von entscheidender Bedeutung ist. Im Rahmen der Weiterentwicklung der Ordnungsreduktion nach der Methode der modalen Superposition ist es gelungen für typische lineare Kräfte eine auf analytischen Gleichungen basierende Beschreibung im reduzierten Raum zu finden. Für die Beschreibung von nichtlinearen Kräften ist im Rahmen dieser Arbeit ein Verfahren entwickelt worden, das es erlaubt, bestehende Modelle im Finite-Elemente-Raum in der modalen Beschreibung zu nutzen. In dieser Arbeit werden die theoretischen Grundlagen zur Berücksichtigung von Einflüssen der Aufbau- und Verbindungstechnik in ordnungsreduzierten Modellen dargestellt. Neben der Einkopplung äußerer Kräfte und der Veränderung der mechanischen Randbedingungen wird auch der Einfluss der Aufbau- und Verbindungstechnik auf die elektrostatischen Eigenschaften untersucht. Die Parametrisierung des Verfahrens der modalen Superposition über Fit- und Interpolationsverfahren erlaubt es, parametrisierte ordnungsreduzierte Modelle für die zeitabhängige Systemsimulation zu generieren. Damit wird die Durchführung von Designoptimierung und die Berücksichtigung von Fertigungs- und Prozessschwankungen in ordnungsreduzierten Modellen auf Systemebene möglich.

Today in the scientific and technological world, physical and artificial processes are often described by mathematical models which can be used for simulation, optimization or control. As the mathematical models get more detailed and different coupling effects are required to include, usually the dimension of these models become very large. Such large-scale systems lead to large memory requirements and computational complexity. To handle these large models efficiently in simulation, control or optimization model order reduction (MOR) is essential. The fundamental idea of model order reduction is to approximate a large-scale model by a reduced model of lower state space dimension that has the same (to the largest possible extent) input-output behavior as the original system. Recently, the system-theoretic method Balanced Truncation (BT) which was believed to be applicable only to moderately sized problems, has been adapted to really large-scale problems. Moreover, it also has been extended to so-called descriptor systems, i.e., systems whose dynamics obey differential-algebraic equations. In this thesis, a BT algorithm is developed for MOR of index-1 descriptor systems based on several papers from the literature. It is then applied to the setting of a piezo-mechanical system. The algorithm is verified by real-world data describing micro-mechanical piezo-actuators. The whole algorithm works for sparse descriptor form of the system. The piezo-mechanical original system is a second order index-1 descriptor system, where mass, damping, stiffness, input and output matrices are highly sparse. Several techniques are introduced to reduce the system into a first order index-1 descriptor system by preserving the sparsity pattern of the original models. Several numerical experiments are used to illustrate the efficiency of the algorithm.

Computational methods for reducing the complexity of Finite Element (FE) models in structural dynamics are usually based on modal analysis. Classical approaches such as modal truncation, static condensation (Craig-Bampton, Guyan), and component mode synthesis (CMS) are available in many CAE tools such as ANSYS. In other disciplines, different techniques for Model Order Reduction (MOR) have been developed in the previous 2 decades. Krylov subspace methods are one possible choice and often lead to much smaller models than modal truncation methods given the same prescribed tolerance threshold. They have become available to ANSYS users through the tool mor4ansys. A disadvantage is that neither modal truncation nor CMS nor Krylov subspace methods preserve properties important to control design. System-theoretic methods like balanced truncation approximation (BTA), on the other hand, are directed towards reduced-order models for use in closed-loop control. So far, these methods are considered to be too expensive for large-scale structural models. We show that recent algorithmic advantages lead to MOR methods that are applicable to FE models in structural dynamics and that can easily be integrated into CAE software. We will demonstrate the efficiency of the proposed MOR method based on BTA using a control system including as plant the FE model of a machine tool.