The study on animal models to investigate different human diseases has made a remarkable contribution to the field of biomedical science. Many studies demonstrating the behavior activities of Drosophila have been studied with a motion tracking system. Most of these studies have been performed by the conventional paradigm where the Drosophila is held fixed by tethering or within the limited space. In this paper, we demonstrate the Active Omnidirectional Treadmill System (AOT) which enables the physically unrestricted Drosophila to navigate infinitely in two-dimensional space with the privilege of real-time tracking. The system maintains the position of the fly at a specific position by compensating the motion of the fly by counter direction motion of the sphere. The system is capable of maintaining the position error < 1000 µm for 89.7% of the time and the angular error < 5o 80.0% of the time. The study evaluates the performance of the different feedback control system through the experimental and simulation results. The successful phototaxis experiment was conducted to validate the usability of the system. The integration of the treadmill system with other peripherals for olfactory, vision and thermal stimuli can be a powerful tool to study the longitudinal behavior of the Drosophila. Further, the integration of the system with the optical microscopic device can be used to perform the brain imaging of the walking Drosophila.

Metabolic cycles are a fundamental element of cellular and organismal function. Among the most critical in higher organisms is the Cori Cycle, the systemic cycling between lactate and glucose. Here, skeletal muscle-specific Mitochondrial Pyruvate Carrier (MPC) deletion in mice increased muscle glucose uptake but diverted pyruvate into the circulation as lactate, driving increased Cori Cycling and energy expenditure. Loss of muscle MPC activity evoked adaptive glutaminolysis, increased fatty acid oxidation, and resulted in a striking resistance to gains in fat mass with age with perfect sparing of muscle mass and strength. Furthermore, chronic and acute muscle MPC deletion accelerated fat mass loss on a normal diet after high fat diet-induced obesity. Our results illuminate the role of the skeletal muscle MPC as a central node for whole-body carbohydrate, fat, and amino acid metabolism. They highlight the potential utility of decreasing muscle pyruvate oxidation to ameliorate obesity and type 2 diabetes.

<p>New and exciting possibilities in vehicle control are revealed by the consideration of topography through the combination GPS and three dimensional road maps. This thesis explores how information about future road slopes can be utilized in a heavy truck with the aim at reducing the fuel consumption over a route without increasing the total travel time. </p><p>A model predictive control (MPC) scheme is used to control the longitudinal behavior of the vehicle, which entails determining accelerator and brake levels and also which gear to engage. The optimization is accomplished through discrete dynamic programming. A cost function is used to define the optimization criterion. Through the function parameters the user is enabled to decide how fuel use, negative deviations from the reference velocity, velocity changes, gear shifts and brake use are weighed. </p><p>Computer simulations with a load of 40 metric tons shows that the fuel consumption can be reduced with 2.5% with a negligible change in travel time, going from Link¨oping to J¨onk¨oping and back. The road slopes are calculated by differentiation of authentic altitude measurements along this route. The complexity of the algorithm when achieving these results allows the simulations to run two to four times faster than real time on a standard PC, depending on the desired update frequency of the control signals.</p>

In the future autonomous vehicles are expected to navigate independently and manage complex traffic situations. This thesis is one of two theses initiated with the aim of researching which methods could be used within the field of autonomous vehicles. The purpose of this thesis was to investigate how Model Predictive Control could be used in the field of autonomous vehicles. The tasks to generate a safe and economic path, to re-plan to avoid collisions with moving obstacles and to operate the vehicle have been studied. The algorithm created is set up as a hierarchical framework defined by a high and a low level planner. The objective of the high level planner is to generate the global route while the objectives of the low level planner are to operate the vehicle and to re-plan to avoid collisions. Optimal Control problems have been formulated in the high level planner for the use of path planning. Different objectives of the planning have been investigated e.g. the minimization of the traveled length between the start and the end point. Approximations of the static obstacles' forbidden areas have been made with circles. A Quadratic Programming framework has been set up in the low level planner to operate the vehicle to follow the high level pre-computed path and to locally re-plan the route to avoid collisions with moving obstacles. Four different strategies of collision avoidance have been implemented and investigated in a simulation environment.

The focus of this thesis was on the development of a dynamic modeling capability for a vapor compression system along with the implementation of advanced multivariable control techniques on the resulting model. Individual component models for a typical vapor compression system were developed based on most recent and acknowledged publications within the field of thermodynamics. Parameter properties such as pressure, temperature, enthalpy etc. for each component were connected to detailed thermodynamic tables by algorithms programmed in MATLAB, thus creating a fully dynamic environment. The separate component models were then interconnected and an overall model for the complete system was implemented in SIMULINK. An advanced control technique known as Model Predictive Control (MPC) along with an open-source QP solver was then applied on the system. The MPC-controller requires the complete state information to be available for feedback and since this is often either very expensive (requires a great number of sensors) or at times even impossible (difficult to measure), a full-state observer was implemented. The MPC-controller was designed to keep certain system temperatures within tight bands while still being able to respond to varying cooling set-points. The control architecture was successful in achieving the control objective, i.e. it was shown to be adaptable in order to reflect changes in environmental conditions. Cooling demands were met and the temperatures were successfully kept within given boundaries.

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.

The aim of platooning is to enable several vehicles to drive in a convoy while each vehicle is controlled autonomously in longitudinal direction. All vehicles in the platoon are equipped with WiFi and can therefore apply Vehicle-to-Vehicle (V2V) communication. As a result, a short intermediate distance between the vehicles can be maintained. Reduction of the aerodynamic drag is the result of the short distance, which in turn, reduces the consumed fuel. This thesis is a part of a larger project, consisting of two other theses that investigate estimation of the sensor data. Other scenarios that may arise with the platooning concept, e.g. packet losses and time synchronization of the different sensors are also analyzed. The purpose of this master thesis is to develop and evaluate a Model Predictive Control (MPC) in the concept of platooning. The main focus lies on implementation of two types of MPC, centralized and distributed, and later on integration with the other two subsystems is performed. Results from the MPC itself are evaluated, principally in terms of fuel con- sumption and computational demand. The major part of the results are based on the complete system as one unit and covers different test scenarios such as WiFi loss and non-transmitting vehicle entering the platoon. A comparison of how much energy that is consumed by the engine between an HDV driving with its cruise control and an HDV driving in a platoon has been performed. With an intermediate distance of 10 meters, driving with varying velocity and ideal signals the energy consumption got reduced with an average of 11%. / Syftet med platooning är att flera tunga fordon kör tätt efter varandra i ett fordonståg. Varje fordon regleras autonomt i longitudinell riktning och är utrustad med WiFi. Detta bidrar till att fordonen kan kommunicera med varandra och denna kommunikation, även kallad Vehicle-to-Vehicle (V2V) - communication, leder till att det relativa avståndet mellan fordonen kan minskas, vilket i sin tur leder till minskat luftmotstånd och därmed minskad bränsleförbrukning. Detta examensarbete är en del av ett större projekt som består av ytterligare två examensarbeten. De andra två hanterar estimeringen av sensordata samt behandlar förlorat sensordata och tidssynkronisering av de olika sensorerna som används. Syftet med detta examensarbete är att utveckla och utvärdera en MPC regu- lator i platooning sammanhang. Huvudfokuset ligger på implementeringen, både centraliserad och distribuerad MPC, och integreringen med de två andra delsystemen. Resultaten från enbart MPC utvärderas i termer av bränsleförbrukning och även beräkningskapactiet, då MPC är känt för att vara väldigt beräkningskrävan- de och är ofta en begränsning för hårdvaran. Den största delen av resultaten är baserade på hela systemet och täcker olika scenarion som exempelvis dålig WiFi uppkoppling och att icke−sändande fordon intar platoonen. En jämförelse av hur mycket energi motorn förbrukade har gjorts mellan ett tungt fordon som kör med farthållaren påslagen och ett tungt fordon som kör i en platoon. Med ett relativt avstånd på 10 meter, varierande hastighet och icke brusiga signaler kan bränsleförbrukning minskas med ett medel på approximativt 11%.

New and exciting possibilities in vehicle control are revealed by the consideration of topography through the combination GPS and three dimensional road maps. This thesis explores how information about future road slopes can be utilized in a heavy truck with the aim at reducing the fuel consumption over a route without increasing the total travel time. A model predictive control (MPC) scheme is used to control the longitudinal behavior of the vehicle, which entails determining accelerator and brake levels and also which gear to engage. The optimization is accomplished through discrete dynamic programming. A cost function is used to define the optimization criterion. Through the function parameters the user is enabled to decide how fuel use, negative deviations from the reference velocity, velocity changes, gear shifts and brake use are weighed. Computer simulations with a load of 40 metric tons shows that the fuel consumption can be reduced with 2.5% with a negligible change in travel time, going from Link¨oping to J¨onk¨oping and back. The road slopes are calculated by differentiation of authentic altitude measurements along this route. The complexity of the algorithm when achieving these results allows the simulations to run two to four times faster than real time on a standard PC, depending on the desired update frequency of the control signals.

<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>

To control the speed of an overactuated vehicle there may be many possible ways to use the actuators of the car achieving the same outcome. The actuators in an ordinary car is a combustion engine and a friction brake. In some cases it is trivial how to coordinate actuators for the optimal result, but in many cases it is not. The goal with the thesis is to investigate if it is possible to achieve the same or improved performance with a more sophisticated control structure than today's, using a model predictive controller. A model predictive controller combines the possibility to predict the outcome through an open-loop controller with the stability of a closed loop controller and gives the optimal solution for a finite horizon optimization problem. A simple model of the longitudinal dynamics of a car is developed and used in the model predictive controller framework. This is then used in simulations and in a real car. It is shown that it is possible to replace the current controller structure with a model predictive controller, but there are advantages and disadvantages with the new control structure.