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The speed control of a Brushless DC Motor Drive in the presence of load disturbance is investigated. Firstly some practical results are presented where a simple proportional-integral speed controller is used in the presence of a large step input speed demand as well as load disturbance. The wind-up problem caused by the saturation of the controller is discussed. In order to improve the performance of the proportional-integral speed controller in the presence of load variation, a load estimator is used with torque feedforward control. The results presented show the speed holding capability in the presence of load variation is significantly improved. A genetic algorithm is used on line to optimise the controller for different conditions such as large and small step input speed demand and load disturbance. The results presented show that a genetic algorithm is capable of finding the tuning of the controller for optimal performance. Single-input single-output and two-input two-output fuzzy speed controllers are also used and the results compared to a proportional-integral controller. Results are presented showing that a single-input single-output fuzzy controller works as a proportional controller with variable gain whereas the two-input two-output fuzzy controller is capable of driving the motor at variable speed and load torque with excellent performance. The robustness of the fuzzy controllers is compared to the proportional-integral controller and the results presented show that the fuzzy one is more robust then the proportional-integral. A genetic algorithm is also used on line for the optimisation of the two-input twooutput fuzzy speed controller and the results show that despite the large number of parameters to be optimised, the tuning for optimal performance is also possible.

Autorotation maneuvers are required to perform a safe landing of a helicopter in cases of engine loss in a single engine vehicle and transmission or tail rotor malfunction. The rise of autonomous helicopter technology, and the pilot skill required to manually perform an autorotation, motivate the need for new autonomous autorotation control laws. Previous approaches to automatic control for this maneuver have relied on control law optimization based on a high-fidelity model of the helicopter, or have attempted to match recorded trajectories flown by an expert human pilot. In this paper, a new expert control system is proposed. The term “expert control system” is used because the system is intended to mimic the actions that a human pilot might take, does not require any iterative learning, model prediction, or optimization at runtime, and is based on an inference system that involves fuzzy logic, PID, and other conventional control techniques. The multi-stage control law drives the helicopter to a near-optimal steady-state descent and uses an estimate of the time to impact to safely flare and land the helicopter in the vast majority of flight conditions. The control law is validated using a full 6-degree-of-freedom simulation of both a full-size attack helicopter and a small hobby-class helicopter. The pro- posed control design is highly flexible and may be used to perform fully autonomous autorotation or to provide guidance to pilots during manual autorotation maneuvers.

This thesis presents a Fuzzy Consensus Building Framework (FCBF), which enables construction project parties to align their teams on their roles and responsibilities early on in their projects. The framework introduces a model that (1) incorporates consensus of construction project teams in aggregating their opinions to decide on the party responsible for every standard task of a construction project; (2) classifies the quality of experts in the decision making process by weighting their responses during aggregation, based on their attributes; and (3) resolves residual conflicts between project teams on their perceived shared tasks, using a consensus reaching process. A template of project and construction management tasks is extracted from relevant standard guidelines and interviews with industry peers. Different extents of the roles and responsibilities of the owner and contractors are described using seven linguistic terms. A modified similarity aggregation method (SAM) aggregates experts opinions in a linguistic framework, using a consensus weight factor for each expert. A fuzzy expert system (FES) determines an importance weight factor for each expert, representing expert quality; opinions are aggregated using this factor and the consensus weight factor. Based on the aggregated opinions of experts, the tasks are classified into three responsibility lists: the owners, the contractors, and the shared responsibility list. The fuzzy preference relations consensus (FPRC) approach is applied to the tasks of shared responsibility, and a linguistic consensus measure is applied to resolve potential conflicts between team members on their perceived shared tasks. Using a case study approach, the FCBF is applied to aid a project owner organization in the field of oil and gas to determine its roles and responsibilities in a customized project delivery system, called owner managing contractor (OMC). The FCBF contributes to the construction industry by solving a fundamental problem for project owners: it helps identify and reduce potential conflicts over the extent of project teams responsibilities prior to the construction stage. It also provides an improvement over previous consensus-based approaches, which rely on a subjective assessment of experts importance weights in aggregating their opinions, and it modifies the SAM to adapt it to a linguistic environment. / Construction Engineering and Management

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Bibliography: p. 321-333. / xii, 336 p. : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Specifically addresses the problem of the robustness and reliability of the SR motor drive operation with sensorless position estimation. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1998

Univ., Habil.-Schr.--Stuttgart, 2005. / Literaturverz. S. [245] - 252.