Genetic Fuzzy Controller for a Gas-Turbine Fuel System

Abstract

In this study, a fuel system controller for a gas turbine engine was examined. Controller design in this application is challenging due to nonlinearities in the closed loop system, as well as uncertainties associated with hardware components from part variation or degradation. Current closed loop design methodologies are discussed, as are the limitations or challenges facing these systems. Details on fuzzy logic control and its benefits in this type of application are explored.Information on genetic algorithms is presented, along with a study on how this optimization approach can be utilized to enhance the fuzzy logic controller process. A fuzzy logic controller structure was developed for providing closed loop fuel control in the gas turbine application, using a genetic algorithm to tune the system to provide an accurate and fast response to changing input demands. With a genetic fuzzy controller in place, closed loop analysis was performed, along with a stochastic robustness analysis to assess controller performance in an uncertain environment. Results show that the genetic fuzzy system performed well in this application, resulting in a system with fast rise and settling times to stepping inputs, while also minimizing overshoot and steady state error. Robustness characteristics of the fuzzy controller were also demonstrated, as the stochastic robustness analysis yielded acceptable performance in each simulation of the closed loop system with uncertainties included.ii iii

Fuzzy Logic Attitude Control of a Magnetically Actuated CubeSat

“…One important aspect of the string is the selection of the base into which the parameters are converted. Most often, parameters are expressed as binary numbers, and each parameter is listed in the string one after the other with no spaces 46 . This can lead to a very long binary string representation of the parameters and is relatively undesirable if large numbers of these strings are to be stored.…”

“…Alternatively, base ten could be used to represent the optimization parameters, thus reducing string size. However, base ten representation requires special mutation operations because, unlike binary, a number in base ten does not have a definite "opposite" value 46 .…”

“…Selection is the method by which members of the population are picked to undergo crossover or mutation operations 46 . Several different selection methods exist, but the one used for tuning the attitude control fuzzy gain scheduler is Roulette Wheel Selection.…”

“…Several different selection methods exist, but the one used for tuning the attitude control fuzzy gain scheduler is Roulette Wheel Selection. In Roulette Wheel Selection, the fitness of all population members is normalized, such that the sum of all members' fitness values is one 46 . Each member is then given a percentage of the range between zero and one corresponding to the member's normalized fitness value 46 .…”

“…In Roulette Wheel Selection, the fitness of all population members is normalized, such that the sum of all members' fitness values is one 46 . Each member is then given a percentage of the range between zero and one corresponding to the member's normalized fitness value 46 . A random number is generated on this range, and the member whose part of the range on which the random number falls is selected for further manipulation 46 .…”