This thesis proposes a double-loop on-off temperature control which comprises two loops of compensation. The configuration is simple and the stability and performance can be easily achieved with proper choice of two parameters in the controller. For the performance, the controller switches the current through the heater in order to achieve the goal of power efficiency and temperature regulation; switching noise suppression and small regulation error can be achieved at the same time. The proposed control scheme is applied to a water heater. The experimental results reveal that the regulable range of temperature is from room temperature to 100 ¢J¡]room temperature ¡Ù 30 ¢J¡^, with temperature regulation error within ¡Ó1 ¢J. It conforms the stability and accuracy of the proposed control scheme.

In large controller-datapath systems, the switching activity of datapath is administered by controller. The unnecessary switching activity will cause more power consumption, and therefore the design of controllers (i.e. Finite State Machines, FSMs) will influence the whole power consumption of the systems. The state assignment and output encoding are the two major factors influencing the power of system under the hardware implementation of controllers. In this paper, we present an integer linear programming (ILP) method to solve the state assignment and output encoding problems. The purpose is to reduce switching activity such that the goal of power optimization can be achieved. It has not to reschedule the operations of datapath under timing and resource constraints and has no extra area overhead. In order to verify the effectiveness of our proposed ILP approach, we use this approach to implement several controller-datapath systems. Experimental results show that our proposed approach achieves an average of 30.513% power savings compared to the traditional area optimal synthesis tool, SIS, where power is not considered. Our proposed approach does not cause extra area overhead while achieving a significant power saving of systems.

In this study, two new Multi-Objective Optimization (MOO) techniques are developed. The two new techniques, the Objective-Based Gradient Algorithm (OBGA) and the Principal Component Grid Algorithm (PCGA), were developed with the goals of improving the accuracy and efficiency of the Pareto domain approximation relative to current MOO techniques. Both methods were compared to current MOO techniques using several test problems. It was found that both the OBGA and PCGA systematically produced a more accurate Pareto domain than current MOO techniques used for comparison, for all problems studied. The OBGA requires less computation time than the current MOO methods for relatively simple problems whereas for more complex objective functions, the computation time was larger. On the other hand, the efficiency of the PCGA was higher than the current MOO techniques for all problems tested. The new techniques were also applied to complex chemical engineering problems. The OBGA was applied to an industrial reactor producing ethylene oxide from ethylene. The optimization varied four of the reactor input parameters, and the selectivity, productivity and a safety factor related to the presence of oxygen in the reactor were maximized. From the optimization results, recommendations were made based on the ideal reactor operating conditions, and the control of key reactor parameters. The PCGA was applied to a PI controller model to develop new tuning methods based on the Pareto domain. The developed controller tuning methods were compared to several previously developed controller correlations. It was found that all previously developed controller correlations showed equal or worse performance than that based on the Pareto domain. The tuning methods were applied to a fourth order process and a process with a disturbance, and demonstrated excellent performance.

<p>An on-line identifier using Functional Link Network (FLN) and Pole-shift (PS) controller for power system stabilizer (PSS) application are presented in this thesis. To have the satisfactory performance of the PSS controller, over a wide range of operating conditions, it is desirable to adapt PSS parameters in real time. Artificial Neural Networks (ANNs) transform the inputs in a low-dimensional space to high-dimensional nonlinear hidden unit space and they have the ability to model the nonlinear characteristics of the power system. The ability of ANNs to learn makes them more suitable for use in adaptive control techniques.</p> <p>On-line identification obtains a mathematical model at each sampling period to track the dynamic behavior of the plant. The ANN identifier consisting of a Functional link Network (FLN) is used for identifying the model parameters. A FLN model eliminates the need of hidden layer while retaining the nonlinear mapping capability of the neural network by using enhanced inputs. This network may be conveniently used for function approximation with faster convergence rate and lesser computational load.</p> <p>The most commonly used Pole Assignment (PA) algorithm for adaptive control purposes assign the pole locations to fixed locations within the unit circle in the z-plane. It may not be optimum for different operating conditions. In this thesis, PS type of adaptive control algorithm is used. This algorithm, instead of assigning the closed-loop poles to fixed locations within the unit circle in the z-plane, this algorithm assumes that the pole characteristic polynomial of the closed-loop system has the same form as the pole characteristic of the open-loop system and shifts the open-loop poles radially towards the centre of the unit circle in the z-plane by a shifting factor &alpha; according to some rules. In this control algorithm, no coefficients need to be tuned manually, so manual parameter tuning (which is a drawback in conventional power system stabilizer) is minimized. The PS control algorithm uses the on-line updated ARMA parameters to calculate the new closed-loop poles of the system that are always inside the unit circle in the z-plane.</p> <p>Simulation studies on a single-machine infinite bus and on a multi-machine power system for various operating condition changes, verify the effectiveness of the combined model of FLN identifier and PS control in damping the local and multi-mode oscillations occurring in the system. Simulation studies prove that the APSSs have significant benefits over conventional PSSs: performance improvement and no requirement for parameter tuning.</p>

In this study, two new Multi-Objective Optimization (MOO) techniques are developed. The two new techniques, the Objective-Based Gradient Algorithm (OBGA) and the Principal Component Grid Algorithm (PCGA), were developed with the goals of improving the accuracy and efficiency of the Pareto domain approximation relative to current MOO techniques. Both methods were compared to current MOO techniques using several test problems. It was found that both the OBGA and PCGA systematically produced a more accurate Pareto domain than current MOO techniques used for comparison, for all problems studied. The OBGA requires less computation time than the current MOO methods for relatively simple problems whereas for more complex objective functions, the computation time was larger. On the other hand, the efficiency of the PCGA was higher than the current MOO techniques for all problems tested. The new techniques were also applied to complex chemical engineering problems. The OBGA was applied to an industrial reactor producing ethylene oxide from ethylene. The optimization varied four of the reactor input parameters, and the selectivity, productivity and a safety factor related to the presence of oxygen in the reactor were maximized. From the optimization results, recommendations were made based on the ideal reactor operating conditions, and the control of key reactor parameters. The PCGA was applied to a PI controller model to develop new tuning methods based on the Pareto domain. The developed controller tuning methods were compared to several previously developed controller correlations. It was found that all previously developed controller correlations showed equal or worse performance than that based on the Pareto domain. The tuning methods were applied to a fourth order process and a process with a disturbance, and demonstrated excellent performance.

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In order for companies to be competitive they perform different types of development projects.  Due to globalization a greater focus is added on costs, not least on costs in development projects. Project controllers are involved in development projects in order to review the financial aspects. The authors have examined the role that project controllers hold in respect of two Swedish manufacturers, Volvo CE and Scania CV. The investigation has revolved around three main issues where the authors conducted interviews with controllers that do not work in development projects and project controllers in development projects. The goal of the interviews was to gain a greater understanding of the role of project controllers, how this role will differ from those that do not work in development projects and the difficulties faced by project controllers. The investigation has shown that studies on the subject area of project controller are limited. Focus has been on the role of business controller and how this role can contribute to better decisions and management of companies. The analysis of the case companies has shows that project controllers contains properties that business controllers has but has also properties of the more analytical and more accounting oriented role of accounting controller. The authors draw the conclusions that the project controller’s role should be developed and aim of doing it more business oriented. The role of project controller should be more clearly described, divided and the processes should be clarified in order to make the role of project controller at the case companies more similar to the role of business controller.

This master thesis was carried out at the Department of Aerodynamics and Flight Mechanics at Saab Aerosystems, Linköping, Sweden. It makes up the author’s final work prior to graduation in the field Applied Physics and Electrical Engineering at the Department of Electrical Engineering at The Linköping Institute of Technology (LiTH), Linköping, Sweden.   The objective of the paper was to study a non-uniform helicopter rotor downwash model in forward flight for the unmanned helicopter Skeldar, which is under development at Saab. The main task was to compare the mentioned model with today’s uniform downwash model in order to find differences and similarities. This was done both from a modeling and a controlling perspective. To start with, an introduction is given which is followed by a helicopter theory chapter. The following three chapters deal with the theory of induced velocity, the helicopter model and the Linear Quadratic Regulator (LQR). Finally, the results are presented and discussed.   The downwash models were derived using Momentum Theory (MT) and Blade Element Theory (BET). These two theories were combined in order to find a connection between the induced velocity and the rotor thrust coefficient. The non-uniform downwash model that was studied is proposed by Pitt &amp; Peters and describes a linear variation of the induced velocity in the longitudinal plane.   For the control, a LQ-regulator was chosen since it is easily implemented in MATLAB and it stabilizes the plant model by feedback and consequently creates a robust system. Before the controller could be implemented, the models had to be reduced and the states had to be divided into longitudinal and lateral ones.   The comparison between the open systems clearly shows that differences in the inflow models propagate to all states and consequently the helicopter behaves differently in all planes. Great discrepancies are apparent for the angular velocities p and q. For Pitt &amp; Peters’ model those states are believed to be strongly affected by the system’s positive real pole, causing a rather unstable behavior. When the systems were closed by feedback, the differences were reduced dramatically. Pitt &amp; Peters’ model resulted in greater overshoots than the uniform model, but the overall behavior of all states was rather similar for the two models.   It is concluded, that the adaption of Pitt &amp; Peters’ inflow model does not make any substantial difference when a controller is implemented. The differences between the open systems, however, are reason enough to question Pitt &amp; Peters’ model. In order to evaluate the non-uniform model properly, it has to be compared to suitable flight data which is still lacking up to this date.

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