Browsing by Author "Fong, Nga Hin Benjamin"

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    A comparison study of genetic algorithms in feedback controller design
    Fong, Nga Hin Benjamin (Virginia Tech, 1994-12-08)
    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.
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    Modeling, Analysis,and Design of Responsive Manufacturing Systems Using Classical Control Theory
    Fong, Nga Hin Benjamin (Virginia Tech, 2005-04-15)
    The manufacturing systems operating within today's global enterprises are invariably dynamic and complicated. Lean manufacturing works well where demand is relatively stable and predictable where product diversity is low. However, we need a much higher agility where customer demand is volatile with high product variety. Frequent changes of product designs need quicker response times in ramp-up to volume. To stay competitive in this 21st century global industrialization, companies must posses a new operation design strategy for responsive manufacturing systems that react to unpredictable market changes as well as to launch new products in a cost-effective and efficient way. The objective of this research is to develop an alternative method to model, analyze, and design responsive manufacturing systems using classical control theory. This new approach permits industrial engineers to study and better predict the transient behavior of responsive manufacturing systems in terms of production lead time, WIP overshoot, system responsiveness, and lean finished inventory. We provide a one-to-one correspondence to translate manufacturing terminologies from the System Dynamics (SD) models into the block diagram representation and transfer functions. We can analytically determine the transient characteristics of responsive manufacturing systems. This analytical formulation is not offered in discrete event simulation or system dynamics approach. We further introduce the Root Locus design technique that investigates the sensitivity of the closed-loop poles location as they relate to the manufacturing world on a complex s-plane. This subsequent complex plane analysis offers new management strategies to better predict and control the dynamic responses of responsive manufacturing systems in terms of inventory build-up (i.e., leanness) and lead time. We define classical control theory terms and interpret their meanings according to the closed-loop poles locations to assist production management in utilizing the Root Locus design tool. Again, by applying this completely graphic view approach, we give a new design approach that determine the responsive manufacturing parametric set of values without iterative trial-and-error simulation replications as found in discrete event simulation or system dynamics approach.