System analysis

This thesis discusses the coupling of a mechanical and electrical oscillator, an arrangement that is often encountered in mechatronics actuators and sensors. The dynamics of this coupled system is mathematically modeled and a low pass equivalent model is presented. Numerical simulations are then performed, for various input signals to characterize the nonlinear relationship between the electrical current and the displacement of the mass. Lastly a framework is proposed to estimate the mass position without the use of a position sensor, enabling the sensorless control of the coupled system and additionally providing the ability for the system to act as an actuator or a sensor. This is of value for health monitoring, diagnostics and prognostics, actuation and power transfer of a number of interconnected machines that have more than one electrical system, driving corresponding mechanical subsystems while being driven by the same voltage source and at the same time being spectrally separated and independent.

Due to the relative youth of the computer-aided software engineering (CASE) market and the lack of standards, evaluation of CASE tools is a difficult problem. This problem is made more difficult by the fact that no single CASE tool is able to satisfy the needs of all potential users. In addition, an incorrect choice is expensive in terms of money and time invested. In this thesis, the literature is surveyed and synthesized to produce procedures and criteria to be used in the evaluation and selection of CASE tools intended for the analysis and design phases of the software development life cycle.

System identification methods are frequently used to obtain appropriate models for the purpose of control, fault detection, pattern recognition, prediction, adaptive filtering and other purposes. A number of techniques exist for the identification of linear systems. However, real-world and complex systems are often nonlinear and there exists no generic methodology for the identification of nonlinear systems with unknown structure. A recent approach makes use of highly interconnected networks of simple processing elements, which can be programmed to approximate nonlinear functions to identify nonlinear dynamic systems. This thesis takes a detailed look at identification of nonlinear systems with neural networks. Important questions in the application of neural networks for nonlinear systems are identified; concerning the excitation properties of input signals, selection of an appropriate neural network structure, estimation of the neural network weights, and the validation of the identified model. These questions are subsequently answered. This investigation leads to a systematic procedure for identification using neural networks and this procedure is clearly illustrated by modeling a complex nonlinear system; the components of the space shuttle main engine. Additionally, the neural network weights are determined by using a general purpose optimization technique known as evolutionary programming which is based on the concept of simulated evolution. The evolutionary programming algorithm is modified to include self-adapting step sizes. The effectiveness of the evolutionary programming algorithm as a general purpose optimization algorithm is illustrated on a test suite of problems including function optimization, neural network weight optimization, optimal control system synthesis and reinforcement learning control.

Model reduction of large-scale systems over a specified frequency range of operation is studied in this research and reported in this dissertation. Frequency-domain balanced structures with integration of singular perturbation are proposed for model reduction of large-scale continuous-time as well as discrete-time systems. This method is applied to both open-loop as well as closed-loop systems. It is shown that the response of reduced systems closely resemble that of full order systems within a specified frequency range of operation. Simulation experiments for the model reduction of several large-scale, continuous and discrete-time systems demonstrate the superiority of the proposed technique over the previously available methods.

The demand on transportation infrastructure is dramatically increasing due to population growth causing the transportation systems to be pushed to their limits. With the projected population growth, not only for the U.S. but especially for the higher education field, university campuses are of great importance for transportation engineers. Urban univeristy campuses are considered major trip generators and with the population forecast many challenges are bound to arise. The implementation of an improved transit system provides a lower-cost solution to the continuously increasing congestion problems in university campus road networks and surrounding areas. This paper presents a methodology focused on the development of a hybrid system concentrated in three main aspects of transit functionality : access to bus stop location, reasonable travel time and low cost. Two methods for bus stop locations assessment are presented for two levels of analysis : microscopic and mesoscopic. The resulting travel time from the improved bus stop locations is analyzed and compared to the initial conditions by using a microsimulation platform. The development of a mathematical model targets the overall system's cost minimization, including user and operator cost, while maximizing the service coverage. The results demonstrate the benefits of the bus stop assessment by the two applied methods, as well as, the benefits of the route and headway selection based on the mathematical model. Moreover, the results indicate that the generation of routes using travel time as the impedance factor generates the optimal possible routes to obtain the minimum system's overall cost.

The dominant definitions of sustainability are too various and neglect essential elements necessary for effective sustainability discourse. This project considers what current understandings of sustainable development mean to those who subscribe to them and how those understandings affect public policy for sustainable development. I begin by presenting a timeline on the evolution of the term 'sustainability'. Then, I offer narrative policy analysis as a methodological tool for investigating communities of meaning with contending views on sustainability. This provides a foundation for the analysis of case studies using Harrisonian Sustainability Narratives-efficiency, equity, and ethics-as lenses through which three corresponding U.S. case studies are explored, each representing different levels of analysis-corporate, state, and individual. First, the Business Roundtable, a lobbying organization comprised of the CEOs of top U.S. companies exemplifying the efficiency narrative, claims that the problem of sustainable development can be addressed through free markets, which continually increase eco-efficiency and encourage technological advancement. Next, the Environmental Protection Agency, a state organization mandated to protect water and air and to manage toxic and solid wastes and representing the equity narrative, sees the problem of sustainable development as ensuring the just distribution of natural limits so as to reduce the impact of those limits on individuals within communities. Lastly, the ethical anthropology of Anna Peterson, philosopher of religion, points to the power of ethical narratives in creating wide-scale changes to our ideas about humanness and human nature as they relate to our relationship with our environment for sustainability.