Browsing by Subject "Real-time control"
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Item A PEMFC hybrid electric vehicle real time control system(Texas Tech University, 2004-12) Sun, HongqiaoIn recent years, environmental friendly technologies and alternative energy solutions have drawn a lot of public attention due to the global energy crisis and pollution issues. Fuel cell (FC), a technology invented about the same time as the internal combustion (IC) engine, is now the focus of the automotive industry again. The fuel cell vehicle (FCV) has zero emission and its efficiency is significantly higher than the conventional IC engine power vehicles. Among a variety of FCV technologies, proton exchange membrane (PEM) FC vehicle appears to be far more attractive and mature. The prototype PEMFC vehicle has been developed and demonstrated to the public by nearly all the major automotive manufacturers in recent years. However, to the interest of the public research, publications and documentations on the PEMFC vehicle technology are rarely available due to its proprietary nature, which essentially makes it a secured technology. This dissertation demonstrates a real world application of a PEMFC hybrid electric vehicle. Through presenting the vehicle design concept, developing the real time control system and generating generic operation principles, this dissertation targets establishing the public knowledge base on this new technology. A complete PEMFC hybrid electric vehicle design, including vehicle components layout, process flow diagram, real time control system architecture, subsystem structures and control algorithms, is presented in order to help understand the whole vehicle system. The design concept is validated through the vehicle demonstration. Generic operating principles are established along with the validation process, which helps populate this emerging technology. Thereafter, further improvements and future research directions are discussedItem Assembly and test operations with multipass requirement in semiconductor manufacturing(2014-05) Gao, Zhufeng; Bard, Jonathan, F.In semiconductor manufacturing, wafers are grouped into lots and sent to a separate facility for assembly and test (AT) before being shipped to the customer. Up to a dozen operations are required during AT. The facility in which these operations are performed is a reentrant flow shop consisting of several dozen to several hundred machines and up to a thousand specialized tools. Each lot follows a specific route through the facility, perhaps returning to the same machine multiple times. Each step in the route is referred to as a "pass." Lots in work in process (WIP) that have more than a single step remaining in their route are referred to as multi-pass lots. The multi-pass scheduling problem is to determine machine setups, lot assignments and lot sequences to achieve optimal output, as measured by four objectives related to key device shortages, throughput, machine utilization, and makespan, prioritized in this order. The two primary goals of this research are to develop a new formulation for the multipass problem and to design a variety of solution algorithms that can be used for both planning and real-time control. To begin, the basic AT model considering only single-pass scheduling and the previously developed greedy randomized adaptive search procedure (GRASP) along with its extensions are introduced. Then two alternative schemes are proposed to solve the multipass scheduling problem. In the final phase of this research, an efficient procedure is presented for prioritizing machine changeovers in an AT facility on a periodic basis that provides real-time support. In daily planning, target machine-tooling combinations are derived based on work in process, due dates, and backlogs. As machines finish their current lots, they need to be reconfigured to match their targets. The proposed algorithm is designed to run in real time.Item Computational modeling and real-time control of patient-specific laser treatment of prostate cancer(2008-05) Fuentes, David Thomas A., 1981-; Oden, J. Tinsley (John Tinsley), 1936-Hyperthermia based cancer treatments delivered under various modalities have the potential to become an effective option to eradicate the disease, maintain functionality of infected organs, and minimize complications and relapse. Moreover, hyperthermia therapies are a form of minimally invasive cancer treatment which are key to improving the quality of life post-treatment. Many modalities are available for delivering the heat source. However, the ability to control the energy deposition to prevent damage to adjacent healthy tissue is a limiting factor in all forms of thermal therapies, including cryotherapy, microwave, radio-frequency, ultrasound, and laser. The application of a laser heat source under the guidance of real-time treatment data has the potential to provide unprecedented control over the temperature field induced within the biological domain. The computational infrastructure developed in this work combines a computational model of bioheat transfer based on a nonlinear version of the Pennes equation for heterogeneous media with the precise timing and orchestration of the real-time solutions to the problems of calibration, optimal control, data transfer, registration, finite element mesh refinement, cellular damage prediction, and laser control; it is an example of Dynamic-Data-Driven Applications System (DDDAS) in which simulation models interact with measurement devices and assimilates data over a computational grid for the purpose of producing high-fidelity predictions of physical events. The tool controls the thermal source, provides a prediction of the entire outcome of the treatment and, using intra-operative data, updates itself to increase the accuracy of the prediction. A precise mathematical framework for the real-time finite element solution of the problems of calibration, optimal heat source control, and goal-oriented error estimation applied to the equations of bioheat transfer is presented. It is demonstrated that current finite element technology, parallel computer architecture, data transfer infrastructure, and thermal imaging modalities are capable of inducing a precise computer controlled temperature field within a biological domain. The project thus addresses a set of problems falling in the intersection of applied mathematics, imaging physics, computational science, computer science and visualizations, biomedical engineering, and medical science. The work involves contributions in the three component areas of the CAM program; A, Applicable Mathematics; B, Numerical Analysis and Scientific Computing; and C, Mathematical modeling and Applications. The ultimate goal of this research is to provide the medical community a minimally invasive clinical tool that uses predictive computational techniques to provide the optimal hyperthermia laser treatment procedure given real-time, patient specific data.Item Intelligent process quality control and tool monitoring in manufacturing systems(Texas Tech University, 1994-05) Chinnam, Ratna BabuThe work presented is best characterized as an investigation of neural networks for effective process quality control and monitoring in automated manufacturing systems. The research addresses two basic questions. The first question is whether neural networks have the potential to "identify" cause-effect relationships associated with advanced manufacturing systems to achieve real-time quality control? The second question is whether it is possible to use neural networks to develop effective reliability based real-time tool condition monitoring models for manufacturing systems? Both multilayer feedforward perceptron networks and radial basis fiinction networks are used in novel configurations to achieve real-time process parameter design. The models developed are capable of monitoring process performance characteristics of interest by building empirical based relationships to relate the process response characteristics with controllable and uncontrollable parameters, simultaneously. Using these empirical models and the levels of the uncontrollable parameters obtained through sensors, the quality controller provides levels for the controllable parameters that will lead to the desired levels ofthe quality characteristics in real-time. In general, the quality controller models were able to provide levels for the controllable variables that resulted in the desired process quality characteristics. Test results are discussed for several simulated production processes. A validity index neural network based approach was developed to automate the toolwear monitoring problem. In contrast to the contemporary approaches that basically deal with a classification problem, classifying a given tool as either fresh or worn, the model derived from radial basis function networks predicts the conditional probability of tool survival in accordance with the traditional reliability theory, given a critical performance plane, using on-line sensory data. In general, the radial basis fimction networks performed extremely well in time-series prediction, when tested on actual data collected from a drilling process. The validity index neural network is extended to arrive at the desired conditional tool reliability.Item Object-oriented analysis and specification for real-time systems(Texas Tech University, 1990-12) Moon, Eunice Yoon-GilIt is important to provide a complete, consistent, and feasible requirements specification, especially for complex real-time systems. To maximize the effectiveness of object-oriented software development, it is ideal to apply the object-oriented approach to every stage of the software life cycle. In this research, an objectoriented analysis methodology which can be applied to real-time systems (OOART) has been developed and applied to an existing system. A message passing technique which employs the priority queue, and embeds the time constraints in each message, has also been developed. To represent the OOART in the resulting requirements specification, a set of graphical notations which not only supports the concepts of the object-oriented approach, but also supports the characteristics of realtime systems, has been utilized. As a case study, the OOART has been applied to a single wafer cleaning system which implements a distributed real-time control of semiconductor process equipment.Item Real time Markov localization for mobile robots using pre-computation of sensor model(Texas Tech University, 2002-05) Kona, SrividyaLocalization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This thesis presents a version of Markov Localization that provides accurate position estimates of the Mobile Robot in Real Time. The key idea of Markov Localization is to maintain a probability density over the space of all locations of a robot in its environment. The approach in this thesis represents this space metrically, using a fine-grained grid to approximate densities. It is able to globally localize the robot from scratch and to recover from localization failures. It is robust to approximate models of the environment (such as occupancy grid maps) and noisy sensors(such as ultrasound sensors). The main extension of the existing algorithm in this thesis is the Pre-Computation of Sensor Model, which takes off a lot of computational burden on the algorithm and makes it work well in Real Time.