A robust nonlinear observer, utilizing the sliding mode concept, is developed for the dynamic positioning of ships. The observer provides the estimates of linear velocities of the ship and bias from the slowly varying environmental loads. It also filters out wave frequency motion to avoid wear of actuators and excessive fuel consumption. Especially, the observer structure with a saturation function makes the proposed observer robust against neglected nonlinearties, disturbances and uncertainties. A direct adaptive neural network controller is developed for a model of an underwater vehicle. Radial basis neural network and multilayer neural network are used in the closed-loop to approximate the nonlinear vehicle dynamics. No prior off-line training phase and no explicit knowledge of the structure of the plant are required, and this scheme exploits the advantages of both neural network control and adaptive control. A control law and a stable on-line adaptive law are derived using the Lyapunov theory, and the convergence of the tracking error to zero and the boundedness of signals are guaranteed. Comparison of the results with different neural network architectures is made, and performance of the controller is demonstrated by computer simulations. The sliding mode observer is used to eliminate observation spillovers in the vibration control of flexible structures. It is common to build a state feedback controller and a state estimator based on the mathematical model of the system with a finite number of vibration modes, but this may cause control and observation spillover due to the residual (uncontrolled) modes. The performance of a sliding mode observer is compared with that of a conventional Kalman filter in order to demonstrate robustness and disturbance decoupling characteristics. Simulation and experimental results using the sliding mode observer are presented for the active vibration control of a cantilever beam using smart materials. / Ph. D.

Materialized views are a well-known optimization strategy with the potential for massive improvements in query processing time, especially for aggregation queries over large tables. To realize this potential, the query optimizer has to know how and when to exploit materialized views. Reporting functions represent a novel technique to formulate sequence-oriented queries in SQL. They provide a column-wise ordering, partitioning, and windowing mechanism for aggregation functions and therefore extend the well-known way of grouping and applying simple aggregation functions. Up to now, current work has not considered the frequently used reporting functions in data warehouse environments. In this paper, we introduce materialized reporting function views and show how to rewrite queries with reporting functions as well as aggregation queries to this new kind of materialized views. We demonstrate the efficiency of our approach with a large number of experiments.