The recent advent of next generation ground-based telescopes, code-named Extremely Large Telescopes (ELT), highlights the beginning of a forced march toward an era of deploying instruments capable of exploiting starlight captured by mirrors at an unprecedented scale. This confronts the astronomy community with both a daunting challenge and a unique opportunity. The challenge arises from the mismatch between the complexity of current instruments and their expected scaling with the square of the future telescope diameters, on which astronomy applications have relied to produce better science. To deliver on the promise of tomorrow’s ELT, astronomers must design new technologies that can effectively enhance the performance of the instrument at scale, while compensating for the atmospheric turbulence in real-time. This is an unsolved problem. This problem presents an opportunity because the astronomy community is now compelled to rethink essential components of the optical systems and their traditional hardware/software ecosystems in order to achieve high optical performance with a near real-time computational response. In order to realize the full potential of such instruments, we investigate a technique supporting Adaptive Optics (AO), i.e., a dedicated concept relying on turbulence tomography. In particular, a critical part of AO systems is the supervisor module, which is responsible for providing the system with a Tomographic Reconstructor (ToR) at a regular pace, as the atmospheric turbulence evolves over an observation window. In this thesis, we implement an optimized supervisor module and assess it under real configurations of the future European ELT (E-ELT) with a 39m diameter, the largest and most complex optical telescope ever conceived. This necessitates manipulating large matrix sizes (i.e., up to 100k × 100k) that contain measurements captured by multiple wavefront sensors. To address the complexity bottleneck, we employ high performance computing software solutions based on cutting-edge numerical algorithms using asynchronous, fine-grained computations as well as approximations techniques that leverage the resulting matrix data structure. Furthermore, GPU-based hardware accelerators are used in conjunction with the software solutions to ensure reasonable time-to-solution to cope with rapidly evolving atmospheric turbulence. The proposed software/hardware solution permits to reconstruct an image with high accuracy. We demonstrate the validity of the AO systems with a third-party testbed simulating at the E-ELT scale, which is intended to pave the way for a first prototype installed on-site

In this research work, the control system of 16 MVAr, 13.8 kV TCR will be designed and digitally implemented. A Real-Time Control System (NI CompactRIOTM Reconfigurable I/O) and a Digital Platform (NI LabVIEWTM Gcode) are used in the digital implementation of TCR control system. The digital control system is composed of reactive power calculation, firing angle determination and triggering pulse generation blocks. The performance of control system will be tested in the field. The simulation results will also be compared with test data.

This thesis uses a modern control approach to address the Internet traffic control issues in the Transport Layer. Through literature review, we are interested in using the H2/H∞ formulation to obtain the good transient performance of an H2 controller and the good robust property from an H∞ controller while avoiding their deficiencies. The H2/H∞ controller is designed by formulating an optimization problem using the H2-norm and the H∞-norm of the system, which can be solved by an LMI approach using MATLAB. Our design starts with the modeling of a router and the control system by augmenting the network plant function with the Sensitivity function S, the Complementary Sensitivity function T and the Input Sensitivity function U. These sensitivity functions along with their weight functions are used to monitor the closed-loop dynamics of the traffic control. By choosing different combinations of the sensitivity functions, we can obtain the SU, the ST and the STU controllers. Both the window-based and rate-based version of these different types of H2/H∞ controllers have been designed and investigated. We have also proved that these controllers are stable using Lyapunov’s First Method. Next, we verify the performance of the controllers by OPNET simulation using different performance measures of queue length, throughput, queueing delay, packet loss rate and goodput. Our performance evaluation via simulation has demonstrated the robustness and the better transient response such as the rise/fall time and the peak queue value. We have also investigated the controller performances subject to network dynamics as well as through comparison with other controllers. Finally, we have improved these controllers for real-time application. They are capable to update/renew the controller in a short time whenever new network parameter values are detected so that the optimum performance can be maintained.