Title page for ETD etd-12042009-020333


Type of Document Master's Thesis
Author Fong, Nga Hin Benjamin
URN etd-12042009-020333
Title A comparison study of genetic algorithms in feedback controller design
Degree Master of Science
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Robertshaw, Harry H. Committee Chair
Cudney, Harley H. Committee Member
Wicks, Alfred L. Committee Member
Keywords
  • Genetic algorithms
Date of Defense 1994-12-08
Availability restricted
Abstract

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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