Eidgen. Techn. Hochsch., Diss.--Zürich, 2005. / Zsfassung in dt. Sprache.

Chemnitz, Techn. Univ., Diss., 2005.

Thesis (Doctoral)--Rijksuniversiteit te Groningen, 1983.

Bachelor-Arbeit Univ. St. Gallen, 2005.

The topic of this thesis is the theoretical and numerical research of optimal control problems for uncertain nonlinear systems, described by semilinear parabolic differential equations with additive noise, where the state is not completely available. Based on a paper by Kazufumi Ito and Karl Kunisch, which was published in 2006 with the title "Receding Horizon Control with Incomplete Observations", we analyze a Model Predictive Control (MPC) approach where the resulting linear problems on small intervals are solved with a Linear Quadratic Gaussian (LQG) design. Further we define a performance index for the MPC/LQG approach, find estimates for it and present bounds for the solutions of the underlying Riccati equations. Another large part of the thesis is devoted to extensive numerical studies for an 1+1- and 3+1-dimensional problem to show the robustness of the MPC/LQG strategy. The last part is a generalization of the MPC/LQG approach to infinite-dimensional problems.

Diss.--Mathematik--Chemnitz--Technische Universität, 1998. / Bibliogr. p. 103-105.

The numerical treatment of linear-quadratic regulator problems for parabolic partial differential equations (PDEs) on infinite time horizons requires the solution of large scale algebraic Riccati equations (ARE). The Newton-ADI iteration is an efficient numerical method for this task. It includes the solution of a Lyapunov equation by the alternating directions implicit (ADI) algorithm in each iteration step. On finite time intervals the solution of a large scale differential Riccati equation is required. This can be solved by a backward differentiation formula (BDF) method, which needs to solve an ARE in each time step. Here, we study the selection of shift parameters for the ADI method. This leads to a rational min-max-problem which has been considered by many authors. Since knowledge about the complete complex spectrum is crucial for computing the optimal solution, this is infeasible for the large scale systems arising from finite element discretization of PDEs. Therefore several alternatives for computing suboptimal parameters are discussed and compared for numerical examples.

Univ., Diss.--St. Gallen, 2007.

Stuttgart, Univ., Diss., 2006. / Druckausg. beim VDI-Verl., Düsseldorf als: Fortschrittberichte / VDI : Reihe 8 ; Nr. 1125 erschienen.

University, Diss., 2004--Konstanz.