En el presente trabajo de investigación se busca demostrar la efectividad de la herramienta de planeamiento Look Ahead, de la Programación Ultimo Planificador (Last Planner), cuando es aplicada correctamente en un proyecto inmobiliario multifamiliar de 10 pisos. La necesidad de cumplir con fechas pactadas de entrega de los productos terminados y validados a los clientes, es la razón de los proyectos inmobiliarios, por un tema de cumplimiento, imagen y como motivo principal, evitar los sobrecostos; por tal razón, se hace indispensable buscar ayuda en los diferentes sistema de gestión de proyectos, para usarlos como herramientas de planificación y programación que ayuden a tomar el control efectivo de los proyectos para así garantizar el cumplimiento de los tiempos de ejecución, en las diferentes fases que la componen.

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In this master thesis the possibilities of combining an adaptive cruise control with information about the road ahead has been studied. The focus has been to investigate the possibility to save fuel by using information about road topology, Look-Ahead. An adaptive cruise control, AiCC, is used when there are preceding vehicles and when the driver in addition to choosing a desired travel speed for the vehicle also chooses a desired time gap that is to be kept to preceding vehicles travelling slower than the own vehicle. Using information about the road ahead and information of preceding vehicles a controller with the function to adapt the speed to the preceding vehicle, target, and at the same time reduce the fuel usage has been constructed. The controller considers the topography on the road and the distance to the target to be able to reduce the utilization of the brakes in steep downhills and to reduce fuel by slowing down before the downhill and then gain speed due to the gravitational force. The controller uses the assumption that the target travels with constant velocity at all time. The work has included simulations with two different test roads, one in Sweden with shorter and not so steep downhills. The other road is placed in Germany and has long and steep downhills. In the simulations three various time gaps, 1, 2 and 3 seconds, has been used and three different weights of the preceding vehicle, 20, 40 and 50 tonnes. The vehicle with the controller using adaptive cruise control and Look-Ahead has a weight of 40 tonnes. The results shows that fuel can be saved, using information about the road ahead in combination with an adaptive cruise control. The best result is obtained when the road contains steep and long downills, where the vehicle will gain speed due to the gravitational force. For the long and steep downhills the result is best when the target weight is 40 and 50 tonnes. When the downhills are smaller and not so steep the best result is obtained when the target weight is 20 tonnes. For these downhills the assumption that the target travels at constant speed makes the vehicle reduce the speed too much before the downhill, not considering that a heavier vehicle will accelerate in the downhill as well. The time gaps that gives the best result is time gap 1 second. This is due to that the aerodynamic force acting upon the vehicle is reduced when there is a preceding vehicle at a not too far distance. The smaller the distance to the preceding vehicle the more the aerodynamic force is reduced.

Look ahead cruise control deals with the concept of using recorded topographic road data combined with a GPS to control vehicle speed. The purpose of this is to save fuel without a change in travel time for a given road. This thesis explores the sensitivity of different disturbances for look ahead systems. Two different systems are investigated, one using a simple precalculated speed trajectory without feedback and the second based upon a model predictive control scheme with dynamic programming as optimizing algorithm. Defect input data like bad positioning, disturbed angle data, faults in mass estimation and wrong wheel radius are discussed in this thesis. Also some investigations of errors in the environmental model for the systems are done. Simulations over real road profiles with two different types of quantization of the road slope data are done. Results from quantization of the angle data in the system are important since quantization will be unavoidable in an implementation of a topographic road map. The results from the simulations shows that disturbance of the fictive road profiles used results in quite large deviations from the optimal case. For the recorded real road sections however the differences are close to zero. Finally conclusions of how large deviations from real world data a look ahead system can tolerate are drawn.

<p>Look ahead cruise control deals with the concept of using recorded topographic road data combined with a GPS to control vehicle speed. The purpose of this is to save fuel without a change in travel time for a given road. This thesis explores the sensitivity of different disturbances for look ahead systems. Two different systems are investigated, one using a simple precalculated speed trajectory without feedback and the second based upon a model predictive control scheme with dynamic programming as optimizing algorithm.</p><p>Defect input data like bad positioning, disturbed angle data, faults in mass estimation and wrong wheel radius are discussed in this thesis. Also some investigations of errors in the environmental model for the systems are done. Simulations over real road profiles with two different types of quantization of the road slope data are done. Results from quantization of the angle data in the system are important since quantization will be unavoidable in an implementation of a topographic road map.</p><p>The results from the simulations shows that disturbance of the fictive road profiles used results in quite large deviations from the optimal case. For the recorded real road sections however the differences are close to zero. Finally conclusions of how large deviations from real world data a look ahead system can tolerate are drawn.</p>

Increasing penetration of stochastic photovoltaic (PV) generation into the electric power system poses significant challenges to system operators. In the thesis, we evaluate the spatial and temporal correlations of stochastic PV generation at multiple sites. Given the unique spatial and temporal correlation of PV generation, an optimal data-driven forecast model for short-term PV power is proposed. This model leverages both spatial and temporal correlations among neighboring solar sites, and is shown to have improved performance compared with conventional persistent model. The tradeoff between communication cost and improved forecast quality is studied using realistic data sets collected from California and Colorado. n IEEE 14 bus system test case is used to quantify the value of improved forecast quality through the reduction of system dispatch cost. The Modified spatio-temporal forecast model which has the least forecast PV overestimate percentage shows the best performance in the dispatch cost reduction.

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Most commercial vehicles currently on the market are still equipped with a passive suspension system, while some luxury brands may already use an adaptive suspension. Active suspension systems on the other hand are rarely found, however, they offer great opportunities to close the gap of the well-known trade-off between ride comfort and handling. Besides that, they can also be used to mitigate single event disturbances, an objective of the USA army as announced in a solicitation which initiated and motivated this research. In addition to that, several studies were found stating the impact and danger of potholes and their impact on the vehicle and passenger. Reviewing the literature, several control strategies for controlling active suspension systems were found. However, most of these approaches used feedback control and did not try to mitigate single event disturbances. Since literature also suggested making use of look ahead preview, research at the Performance Engineering Research Lab at Virginia Tech was started in 2015 combining look ahead preview and an adaptive system to generate optimal force profiles. This introductory research succeeded and proved the used approach to be very promising. However, the used adaptive system was not designed to operate in real-time and did not show any correlation between different road profiles. Therefore, the main objective of this research project is to evaluate and analyze each of the adaptive systems by searching for correlations in their solutions. The results then should be used in order to design a control law which emulates the adaptive system and can be used in a real-time environment. First, an overall research methodology was derived. According to this a software application was developed which extracts ideal force profiles from single event disturbance signals in order to mitigate their impact to the vehicle. The application uses a quarter car model with a partially loaded active suspension system, a set of predefined road profiles, a road profile preprocessor, and an adaptive algorithm. The preprocessing includes geometric filtering using a Tandem-Cam Model and the adaptive processor used an iterative version of the Filtered-X Last-Mean-Square algorithm. During evaluation and analysis of several generated data sets, high correlations in the generated and adjusted adaptive systems were discovered. From these an empirical and theoretical universal filter model was derived, which was then used to design an open-loop control law named Optimal Force Control. The original control law and an adjusted version designed for a real-time environment were tested for all predefined road profiles over all considered vehicle velocities and prove to perform much better than the offline solution using the adaptive system. In summary, a control law named Optimal Force Control was designed which can be used and implemented in a vehicle to extract an analytical and ideal force profile given a road profile input. Implementing an active suspension system with tracking controller, this approach can be used in order to mitigate single event disturbance signals by reducing the vertical vehicle acceleration. / Master of Science / Most commercial vehicles currently on the market are still equipped with a suspension system consisting of springs and shock absorbers (passive suspensions), while some luxury brands already use suspension systems including parts which can change their behavior based on the driving situation (active suspensions). While these active suspension systems are still rarely found, they offer great opportunities to make the vehicle stable and at the same time easy to handle. Also, they have the potential to reduce the risk of an accident while driving over a pothole or disturbance in the road, an objective of the USA Army as announced in a solicitation which initiated and motivated this research. Reviewing the literature, several control strategies for controlling active suspension systems were found. However, most of these approaches required measuring the current state of the suspension system. Research at the Performance Engineering Research Lab at Virginia Tech was started in 2015 in order to control active suspension systems by using data of the road profile ahead of the vehicle. This introductory research succeeded and proved the approach used to be very promising. However, the used system was designed to work in a laboratory environment only. Therefore, the main objective of this research project was to evaluate and analyze the used control strategy by searching for intersections and similarities in the different solutions. The results were then used to design a control strategy which can be applied in a real-world vehicle environment. First, an overall research methodology was derived. According to this methodology a software application was developed that generates the ideal control signal for the active suspension system in order to reduce the impact of a disturbance in the road profile. To that end a set of predefined road profiles were used, and a computer algorithm called Filtered-X Last-Mean-Square algorithm calculated the ideal control signal for the active suspension system. During the evaluation and analysis of several generated data sets, a lot of intersections and similarities were discovered. Based on these findings a new control strategy was designed in order to be implemented into a real-world vehicle environment. The new control strategy for the real-world vehicle environment was tested for all predefined road profiles over all considered vehicle velocities and proved to outperform the control strategy for the laboratory environment. In summary, a new control strategy named Optimal Force Control was designed, which can be used and implemented in a vehicle. The implementation of an active suspension system can be used to mitigate disturbances in the road by reducing the vertical vehicle acceleration.

With the emergence of quantum physics and computer science in the 20th century, a new era was born which can solve very difficult problems in a much faster rate or problems that classical computing just can't solve. In the 21st century, quantum computing needs to be used to solve tough problems in engineering, business, medical, and other fields that required results not today but yesterday. To make this dream come true, engineers in the semiconductor industry need to make the quantum circuits a reality. To realize quantum circuits and make them scalable, they need to be fault tolerant, therefore Clifford+T gates need to be implemented into those circuits. But the main issue is that in the Clifford+T gate set, T gates are expensive to implement. Carry Look-Ahead addition circuits have caught the interest of researchers because the number of gate layers encountered by a given qubit in the circuit (or the circuit's depth) is logarithmic in terms of the input size n. Therefore, this thesis focuses on optimizing previous designs of out-of-place and in-place Carry Look-Ahead Adders to decrease the T-count, sum of all T and T Hermitian transpose gates in a quantum circuit.

<p>This thesis is a study of how cooling fan control can be improved by using road topography information. Two such controllers are presented, one that uses information available in vehicles produced today, and one that combines GPS-information with digital topographic maps to use information about the road ahead of the vehicle.</p><p>Simulations show that significant energy savings can be obtained, especially during warm conditions and hilly roads. Compared to conventional fan controllers, energy consumption can be cut by up to three quarters. Moreover, this is possible without any hardware redesign.</p>