Solar Twins and Solar Analogues in Galactic Surveys.

The Sun is a crucial benchmark for how we see the universe. Especially when it comes to the visible range of the spectrum, stars are commonly compared to the Sun, as it is the most thoroughly studied star. In this work I have focussed on two aspects of the Sun and how it is used in modern astronomy. Firstly, I try to answer the question on how similar to the Sun another star can be. Given the limits of observations, we call a solar twin a star that has the same observed parameters as the Sun within its errors. These stars can be used as stand-in suns when doing observations, as normal night-time telescopes are not built to be pointed at the Sun. There have been many searches for these twins and every one of them provided not only information on how close to the Sun another star can be, but also helped us to understand the Sun itself. In my work I have selected _ 300 stars that are both photometrically and spectroscopically close to the Sun and found 22 solar twins, of which 17 were previously unknown and can therefore help the emerging picture on solar twins. In my second research project I have used my full sample of 300 solar analogue stars to check the temperature and metallicity scale of stellar catalogue calibrations. My photometric sample was originally drawn from the Geneva-Copenhagen-Survey (Nordström et al. 2004; Holmberg et al. 2007, 2009) for which two alternative calibrations exist, i.e. GCS-III (Holmberg et al. 2009) and C11 (Casagrande et al. 2011). I used very high resolution spectra of solar analogues, and a new approach to test the two calibrations. I found a zero–point shift of order of +75 K and +0.10 dex in effective temperature and metallicity, respectively, in the GCS-III and therefore favour the C11 calibration, which found similar offsets. I then performed a spectroscopic analysis of the stars to derive effective temperatures and metallicities, and tested that they are well centred around the solar values.

Stiffness based trajectory planning and feedforward based vibration suppression control of parallel robot machines

The dissertation proposes two control strategies, which include the trajectory planning and vibration suppression, for a kinematic redundant serial-parallel robot machine, with the aim of attaining the satisfactory machining performance. For a given prescribed trajectory of the robot's end-effector in the Cartesian space, a set of trajectories in the robot's joint space are generated based on the best stiffness performance of the robot along the prescribed trajectory. To construct the required system-wide analytical stiffness model for the serial-parallel robot machine, a variant of the virtual joint method (VJM) is proposed in the dissertation. The modified method is an evolution of Gosselin's lumped model that can account for the deformations of a flexible link in more directions. The effectiveness of this VJM variant is validated by comparing the computed stiffness results of a flexible link with the those of a matrix structural analysis (MSA) method. The comparison shows that the numerical results from both methods on an individual flexible beam are almost identical, which, in some sense, provides mutual validation. The most prominent advantage of the presented VJM variant compared with the MSA method is that it can be applied in a flexible structure system with complicated kinematics formed in terms of flexible serial links and joints. Moreover, by combining the VJM variant and the virtual work principle, a systemwide analytical stiffness model can be easily obtained for mechanisms with both serial kinematics and parallel kinematics. In the dissertation, a system-wide stiffness model of a kinematic redundant serial-parallel robot machine is constructed based on integration of the VJM variant and the virtual work principle. Numerical results of its stiffness performance are reported. For a kinematic redundant robot, to generate a set of feasible joints' trajectories for a prescribed trajectory of its end-effector, its system-wide stiffness performance is taken as the constraint in the joints trajectory planning in the dissertation. For a prescribed location of the end-effector, the robot permits an infinite number of inverse solutions, which consequently yields infinite kinds of stiffness performance. Therefore, a differential evolution (DE) algorithm in which the positions of redundant joints in the kinematics are taken as input variables was employed to search for the best stiffness performance of the robot. Numerical results of the generated joint trajectories are given for a kinematic redundant serial-parallel robot machine, IWR (Intersector Welding/Cutting Robot), when a particular trajectory of its end-effector has been prescribed. The numerical results show that the joint trajectories generated based on the stiffness optimization are feasible for realization in the control system since they are acceptably smooth. The results imply that the stiffness performance of the robot machine deviates smoothly with respect to the kinematic configuration in the adjacent domain of its best stiffness performance. To suppress the vibration of the robot machine due to varying cutting force during the machining process, this dissertation proposed a feedforward control strategy, which is constructed based on the derived inverse dynamics model of target system. The effectiveness of applying such a feedforward control in the vibration suppression has been validated in a parallel manipulator in the software environment. The experimental study of such a feedforward control has also been included in the dissertation. The difficulties of modelling the actual system due to the unknown components in its dynamics is noticed. As a solution, a back propagation (BP) neural network is proposed for identification of the unknown components of the dynamics model of the target system. To train such a BP neural network, a modified Levenberg-Marquardt algorithm that can utilize an experimental input-output data set of the entire dynamic system is introduced in the dissertation. Validation of the BP neural network and the modified Levenberg- Marquardt algorithm is done, respectively, by a sinusoidal output approximation, a second order system parameters estimation, and a friction model estimation of a parallel manipulator, which represent three different application aspects of this method.

Numerical Studies of Dark Energy Models and Observations

The cosmological standard view is based on the assumptions of homogeneity, isotropy and general relativistic gravitational interaction. These alone are not sufficient for describing the current cosmological observations of accelerated expansion of space. Although general relativity is extremely accurately tested to describe the local gravitational phenomena, there is a strong demand for modifying either the energy content of the universe or the gravitational interaction itself to account for the accelerated expansion. By adding a non-luminous matter component and a constant energy component with negative pressure, the observations can be explained with general relativity. Gravitation, cosmological models and their observational phenomenology are discussed in this thesis. Several classes of dark energy models that are motivated by theories outside the standard formulation of physics were studied with emphasis on the observational interpretation. All the cosmological models that seek to explain the cosmological observations, must also conform to the local phenomena. This poses stringent conditions for the physically viable cosmological models. Predictions from a supergravity quintessence model was compared to Supernova 1a data and several metric gravity models were studied with local experimental results. Polytropic stellar configurations of solar, white dwarf and neutron stars were numerically studied with modified gravity models. The main interest was to study the spacetime around the stars. The results shed light on the viability of the studied cosmological models.

Control of Robot by Means of Human Thought

Brain computer interface (BCI) is a kind of human machine interface, which provides a new interaction method between human and computer or other equipment. The most significant characteristic of BCI system is that its control input is brain electrical activities acquired from the brain instead of traditional input such as hands or eyes. BCI technique has rapidly developed during last two decades and it has mainly worked as an auxiliary technique to help the disable people improve their life qualities. With the appearance of low cost novel electrical devices such as EMOTIV, BCI technique has been applied to the general public through many useful applications including video gaming, virtual reality and virtual keyboard. The purpose of this research is to be familiar with EMOTIV EPOC system and make use of it to build an EEG based BCI system for controlling an industrial manipulator by means of human thought. To build a BCI system, an acquisition program based on EMOTIV EPOC system is designed and a MFC based dialog that works as an operation panel is presented. Furthermore, the inverse kinematics of RV-3SB industrial robot was solved. In the last part of this research, the designed BCI system with human thought input is examined and the results indicate that the system is running smoothly and displays clearly the motion type and the incremental displacement of the motion.

Design of traction transmission and suspension systems for an omni-directional mobile robot

This project aims to design and manufacture a mobile robot with two Universal Robot UR10 mainly used indoors. In order to obtain omni-directional maneuverability, the mobile robot is constructed with Mecanum wheels. The Mecanum wheel can move in any direction with a series of rollers attached to itself. These rollers are angled at 45º about the hub’s circumference. This type of wheels can be used in both driving and steering with their any-direction property. This paper is focused on the design of traction system and suspension system, and the velocity control of Mecanum wheels in the close-loop control system. The mechanical design includes selection of bearing housing, couplers which are act as connection between shafts, motor parts, and other needed components. The 3D design software SolidWorks is utilized to assemble all the components in order to get correct tolerance. The driving shaft is designed based on assembled structure via the software as well. The design of suspension system is to compensate the assembly error of Mecanum wheels to guarantee the stability of the robot. The control system of motor drivers is realized through the Robot Operating System (ROS) on Ubuntu Linux. The purpose of inverse kinematics is to obtain the relationship among the movements of all Mecanum wheels. Via programming and interacting with the computer, the robot could move with required speed and direction.