published_or_final_version / abstract / toc / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

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This thesis discusses the use of genetic algorithms as a global search technique to solve three optimization problems: a sixth-order polynomial problem, a single-degree-of-freedom spring-mass-damper (SDOF SMD) system problem, and a loading bridge regulator problem. Genetic algorithms are iterative global search techniques based on the principles of natural selection and population genetics. The theory, design and implementation of the algorithm is discussed in detail. The Simple Genetic Algorithm (SGA) is presented to solve a sixth-order polynomial optimization problem. Results from two traditional numerical techniques will be compared with the SGA results as well as the analytical calculus solution. In addition, the effect of different parametric sizes of the genetic operators are investigated. In the second problem, genetic algorithms are used to design a two-state feedback optimal gain set for a SDOF SMD model with a given initial condition. An improved selection scheme called the stochastic remainder selection without replacement is introduced. An improved GA-based (IGA) feedback controller is designed to control the system. Lastly, a regulator control problem is presented using advanced genetic algorithms (AGA). Two-point crossover and inversion operators are employed. A loading bridge is chosen as the control model. An advanced GA-based full-state feedback controller is designed to control the loading bridge with the given reference input voltage. The conclusions show that SGA is more robust than traditional numerical techniques to solve multi-modal functions. Among the three GA approaches considered, AGA is the most robust one for the design of adaptive feedback controllers. / Master of Science

For the implementation of the NASA EOS testbed programmable operations, a control/data acquisition system has been designed, implemented, and tested. The work is focused mainly on the hardware design/build of the custom interface board and software development for real-time control and data acquisition of the testbed. Also, an application program was designed to control, collect, and display the data and parameters of the testbed elements, and troubleshoot the hardware. An algorithm was chosen and applied to the battery simulator control after computer simulations. The final experiment results were obtained successfully, which proved the design to be correct, as the testbed could be operated under computer control. / Master of Science

Household air pollution (HAP) is responsible for almost 4 million premature deaths every year, a burden that is primarily carried by women and children in developing countries. The mortality and morbidity impact of HAP can be significantly alleviated through clean cookstove interventions. However, for these interventions to be effective, the new intervention stove must be a substantially cleaner technology and adoption should be high and sustained over time. Woody biomass is the fuel of choice in many developing communities, and contributes substantially to HAP. Several organizations have launched clean cooking interventions to address this issue. However, the majority of those interventions do not address adoption related challenges, that they often face. This thesis explores previous studies on Human-Centered Design (HCD) and the impact of feedback and data access on behavior change. It details a HCD process and methodology applied during the design process of an air quality feedback system, to improve adoption of liquefied petroleum gas (LPG) cookstoves in Rwanda. The feedback system is intended to provide real-time air quality information to stove users and potentially encourage them to abandon traditional biomass cookstoves in favor of the cleaner LPG stoves.

Multi-level decision feedback equalization (MDFE) is an effective sampled signal processing technique to remove inter-symbol interference (ISI) from disk read-back signals. Parallelism which doubles the symbol rate can be realized by utilizing the characteristic of channel response and decision feedback equalization algorithm. A mixed-signal IC implementation has been chosen for the parallel MDFE. The differential current signals from the feedback equalizer are subtracted from the forward equalizer output at the summing node to cancel the non-causal ISI. A high-speed comparator with 6 bit resolution is used after the cancellation to detect the signal which contains no ISI. In this thesis, a description of the parallel MDFE structure and decision feedback equalization algorithm are presented. The design of a high-speed summing circuitry and a high-speed comparator are discussed. The same comparator design is used for the flash analog-to-digital converter (ADC) which generates error signals for adaptation.The circuits design and layout were carried out in an HP 1.2-��m n-well CMOS process. / Graduation date: 1998

Precise machining of bearing rings is integral to finished bearing assembly quality. The output accuracy of center-based machining systems such as lathes or magnetic chuck grinders relates directly to the accuracy of part centering before machining. Traditional tooling and methods for centering on such machines are subject to wear, dimensional inaccuracy, setup time (hard tooling) and human error (manual centering).A flexible system for initial part centering is developed based on a single measurement system and actuator, whereby the part is placed by hand onto the machine table, rotated and measured to identify center of geometry offset from center of rotation, then moved by a series of controlled impacts or pushes to align the centers. The prototype centering system is developed as a demonstration platform for research in a number of mechanical engineering areas, particularly: Characterization of optimal state estimators through analysis of accuracy and computational efficiency; Distributed communication and control, efficient transfer of information in a real-time environment, and information sharing between processes; Modeling of sliding dynamics and the interaction of friction with compliant body dynamic models; Motion path planning through both deterministic geometric transforms and through frequency domain command manipulation.A vision is created for future work not only in the described areas, but also in the areas of advanced controller design incorporating multiple variables, derived machine diagnostic information, and application of the distributed communication architecture to information flow throughout the manufacturing organization. The guiding motivation for this research is reduction of manufacturing processing costs in the face of global competition. The technologies researched, developments made, and directions prescribed for future research aid in enabling this goal.

Thesis (Ph. D.)--Georgia Institute of Technology, 1989. / Shipping list no.: 90-0587-M. "April, 1989." "NASA grant NAG1-623"--P. [iii]. Includes bibliographical references (p. 162-166). Also available via Internet from the NASA Technical Report Server web site. Address as of 4/26/06: http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19900004467%5F1990004467.pdf; current access available via PURL.

Iterative Learning Control (ILC) considers systems which perform the given desired trajectory repetitively. The command for the upcoming iteration is updated after every iteration based on the previous recorded error, aiming to converge to zero error in the real-world. Iterative Learning Control can be considered as an inverse problem, solving for the needed input that produces the desired output. However, digital control systems need to convert differential equations to digital form. For a majority of real world systems this introduces one or more zeros of the system z-transfer function outside the unit circle making the inverse system unstable. The resulting control input that produces zero error at the sample times following the desired trajectory is unstable, growing exponentially in magnitude each time step. The tracking error between time steps is also growing exponentially defeating the intended objective of zero tracking error. One way to address the instability in the inverse of non-minimum phase systems is to use basis functions. Besides addressing the unstable inverse issue, using basis functions also has several other advantages. First, it significantly reduces the computation burden in solving for the input command, as the number of basis functions chosen is usually much smaller than the number of time steps in one iteration. Second, it allows the designer to choose the frequency to cut off the learning process, which provides stability robustness to unmodelled high frequency dynamics eliminating the need to otherwise include a low-pass filter. In addition, choosing basis functions intelligently can lead to fast convergence of the learning process. All these benefits come at the expense of no longer asking for zero tracking error, but only aiming to correct the tracking error in the span of the chosen basis functions. Two kinds of matched basis functions are presented in this dissertation, frequency-response based basis functions and singular vector basis functions, respectively. In addition, basis functions are developed to directly capture the system transients that result from initial conditions and hence are not associated with forcing functions. The newly developed transient basis functions are particularly helpful in reducing the level of tracking error and constraining the magnitude of input control when the desired trajectory does not have a smooth start-up, corresponding to a smooth transition from the system state before the initial time, and the system state immediately after time zero on the desired trajectory. Another topic that has been investigated is the error accumulation in the unaddressed part of the output space, the part not covered by the span of the output basis functions, under different model conditions. It has been both proved mathematically and validated by numerical experiments that the error in the unaddressed space will remain constant when using an error-free model, and the unaddressed error will demonstrate a process of accumulation and finally converge to a constant level in the presence of model error. The same phenomenon is shown to apply when using unmatched basis functions. There will be unaddressed error accumulation even in the absence of model error, suggesting that matched basis functions should be used whenever possible. Another way to address the often unstable nature of the inverse of non-minimum phase systems is to use the in-house developed stable inverse theory Longman JiLLL, which can also be incorporated into other control algorithms including One-Step Ahead Control and Indirect Adaptive Control in addition to Iterative Learning Control. Using this stable inverse theory, One-Step Ahead Control has been generalized to apply to systems whose discrete-time inverses are unstable. The generalized one-step ahead control can be viewed as a Model Predictive Control that achieves zero tracking error with a control input bounded by the actuator constraints. In situations where one feels not confident about the system model, adaptive control can be applied to update the model parameters while achieving zero tracking error.