Traditional scheduling tools like Gantt Charts and CPM while useful in planning and execution of complex construction projects with multiple interdependent activities haven?t been of much help in implementing effective control systems for the same projects in case of deviation from their desired or assumed behavior. Further, in case of such deviations project managers in most cases make decisions which might be guided either by the prospects of short term gains or the intension of forcing the project to follow the original schedule or plan, inadvertently increasing the overall project cost. Many deterministic project control methods have been proposed by various researchers for calculating optimal resource schedules considering the time-cost as well as the time-cost-quality trade-off analysis. But the need is for a project control system which optimizes the effort or cost required for controlling the project by incorporating the stochastic dynamic nature of the construction-production process. Further, such a system must include a method for updating and revising the beliefs or models used for representing the dynamics of the project using the actual progress data of the project. This research develops such an optimal project control method using Kalman Filter forecasting method for updating and using the assumed project dynamics model for forecasting the Estimated Cost at Completion (EAC) and the Estimated Duration at Completion (EDAC) taking into account the inherent uncertainties in the project progress and progress measurements. The controller is then formulated for iteratively calculating the optimal resource allocation schedule that minimizes either the EAC or both the EAC and EDAC together using the evolutionary optimization algorithm Covariance Matrix Adaption Evolution Strategy (CMA-ES). The implementation of the developed framework is used with a hypothetical project and tested for its robustness in updating the assumed initial project dynamics model and yielding the optimal control policy considering some hypothetical cases of uncertainties in the project progress and progress measurements. Based on the tests and demonstrations firstly it is concluded that a project dynamics model based on the project Gantt chart for spatial interdependencies of sub-tasks with triangular progress rates is a good representation of a typical construction project; and secondly, it is shown that the use of CMA-ES in conjunction with the Kalman Filter estimation and forecasting method provides a robust framework that can be implemented for any kind of complex construction process for yielding the optimal control policies.

To reduce large motion responses of moored platforms in a harsh environment in deep waters, a thruster-assisted position mooring system can be applied. By applying the system, global dynamic responses can be improved in terms of the mooring line/riser top tensions, operational radii, and the top and bottom angle of the production risers. Kalman filtering as an optimum observer and estimator for stochastic disturbances is implemented in the developed control algorithm to filter out wave frequency responses. Investigation of the performance of thruster-assisted moored offshore platforms was conducted in terms of six-degree-of-freedom motions and mooring line/riser top tensions by means of a fully coupled hull/mooring/riser dynamic analysis program in the time domain and a spectral analysis. The two cases, motion analyses of a platform with thrusters and without thrusters, are extensively compared. The numerical examples illustrate that for deepwater position-keeping of platforms a thruster-assisted moored platform can be an effective solution compared to a conventionally moored platform.

Propulsion systems like Turboprop engines are generally designed to operate at a narrow range of optimum steady state performance conditions. However, these conditions are likely to vary in an unpredictable manner according to factors such as components aging, structural damages or even the operating environment. Over time, inefficiencies could add up and can lead to expensive fuel consumption or faster component aging. This thesis presents a self-optimizing control scheme, referred as Peak-seeking control, applied to propulsion systems similar to Turboprop engines. Using an extended Kalman filter, the Peak-seeking method is able drive the system to an optimal condition based only on measurements. No prior knowledge of the engine dynamics is required which make the Peak-seeking technique easy to implement and also allow for modularity in the engine design. This study is performed on both a turboprop and a DC motor driving a variable pitch propeller and considers several performance functions to optimize.

Thesis (M.S.)--California Polytechnic State University, 2009. / Mode of access: Internet. Title from PDF title page; viewed on Jan. 19, 2010. Major professor: John Saghri, Associate Professor. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Electrical Engineering." "December 2009." Includes bibliographical references (p. 67-68).

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published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

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