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Christine Barrera,Ju-Jang Lee 제어로봇시스템학회 2011 제어로봇시스템학회 각 지부별 자료집 Vol.2011 No.12
In the past, non-rigid airship/blimp control was commonly implemented using standard fuzzy logic controllers also known as type-1 fuzzy logic controllers. For better performance, the membership function of the type-1 fuzzy controller was further optimized using different optimization methods with Genetic Algorithm (GA) being the most common. These days, however, a lot of attention is being focused on type-2 fuzzy logic controllers due to its better performance on environments with uncertainties. New optimization methods for the membership function were also being discovered. Among these methods is the Clonal Selection Algorithm (CLONALG) which was inspired from clonal selection principle based on the basic features of an adaptive immune response to an antigenic stimulus. It was proven to have superior performance than Genetic Algorithm because of its faster convergence speed and better fitness values. This method, however, still needs to be tested on an actual control application. This project developed both type-l and type-2 fuzzy logic controllers optimized by using both GA and CLONALG for a blimp control problem. The control system was divided into three parts: velocity, heading and elevation. This research provides a comparison and validates the performance benefits of type-2 over type-1 fuzzy control and CLONALG over GA. A better control with shorter rise time and settling time, less error and less sensitivity to uncertainties was also achieved by using type-2 fuzzy control combined with CLONALG optimization.
The Effect of Galaxy Interactions on Molecular Gas Properties
Pan, Hsi-An,Lin, Lihwai,Hsieh, Bau-Ching,Xiao, Ting,Gao, Yang,Ellison, Sara L.,Scudder, Jillian M.,Barrera-Ballesteros, Jorge,Yuan, Fangting,Saintonge, Amé,lie,Wilson, Christine D.,Hwang, Ho Seo American Astronomical Society 2018 The Astrophysical journal Vol.868 No.2