Development of Genetic Algorithm based Fuzzy Controls for LUO Converters
N. Nachammai1, R. Kayalvizhi2
1N. Nachammai,, Associate Professor, Department of Electronics and Instrumentation Engineering, Annamalai University, Chidambaram (Tamil Nadu), India.
2Dr. R. Kayalvizhi, Professor, Department of Electronics and Instrumentation Engineering Annamalai University, Chidambaram (Tamil Nadu) . India.
Manuscript received on October 21, 2017. | Revised Manuscript received on October 28, 2017. | Manuscript published on November 05, 2017. | PP: 1-6 | Volume-7 Issue-5, November 2017. | Retrieval Number: E3060117517/2017©BEIESP
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©The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Positive output elementary Luo converters perform the conversion from positive DC input voltage to positive DC output voltage. Since Luo converters are non-linear and time-variant systems, the design of high performance controllers for such converters is a challenging issue. The controllers should ensure system stability in any operating condition and good static and dynamic performances in terms of rejection of supply disturbances and load changes. To ensure that the controllers work well in large signal conditions and to enhance their dynamic responses, soft computing techniques such as Fuzzy Logic Controller (FLC) and Genetic Algorithm based FLC (GA-FLC) are suggested. Fuzzy logic is expressed by means of the human language. A fuzzy controller converts a linguistic control strategy into an automatic control strategy and fuzzy rules are constructed by expert experience or knowledge database. Genetic Algorithm is a powerful optimizing tool that is based on the mechanism of natural selection and natural genetics. Since fuzzy parameters are obtained by trial and error method, Genetic Algorithm can be used to optimize the fuzzy rules, membership functions and scaling gains thereby improving the performance of the Luo converter. In order to test the robustness of the designed GA-Fuzzy and Fuzzy based Luo converter, the controllers and converter have been modeled using Matlab – Simulink software. From the simulation results, it is seen that GA- FLC gives fast response, good transient performance and robustness to variations in line and load disturbances. Performance comparison show improvement of transient responses in terms of settling time, peak overshoot and ISE in the GA-Fuzzy than FLC for Luo converter.
Keywords: Fuzzy Logic controller, Genetic Algorithm, Luo Converter, Membership function.