Moth Flame Optimisation Algorithm for Control of LUO Converter
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 15 February 2017 | Revised Manuscript received on 22 February 2017 | Manuscript Published on 28 February 2017 | PP: 109-114 | Volume-6 Issue-3, February 2017 | Retrieval Number: C4848026317/17©BEIESP
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© The Authors. 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: Because of the effects of the parasitic elements, the output voltage and power transfer efficiency of all DC-DC converters are restricted. In order to eliminate the limitations caused by parasitic elements, the voltage lift technique is successfully applied to DC-DC converters resulting in a new series called Luo converters. Linear control methods ensure stability and good control only in small vicinity around the operating point. These classical controllers are designed using mathematical models by linearising non-linearities around the nominal operating point. Since these controllers are also sensitive to the operating points and parameters variations, a high degree of accuracy cannot be guaranteed from them. To ensure that the controllers work well in large signal conditions and to enhance their dynamic responses, intelligent method using fuzzy technique is suggested.The performance of a fuzzy logic controller depends on its control rules and membership functions. Hence, it is very important to adjust these parameters to the process to be controlled. A method is presented for tuning fuzzy control rules by Moth Flame Optimization(MFO) algorithm to make the fuzzy logic control systems behave as closely as possible to the operator or expert behavior in a control process. The tuning method fits the membership functions of the fuzzy rules given by the experts with the inference system and the defuzzification strategy selected, obtaining high-performance membership functions by minimizing an error function. Moth-flame Optimization (MFO) algorithm is one of the newest bio inspired optimization techniques in which the main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation.MFO has a fast convergence rate due to use of roulette wheel selection method. Moth-Flame Optimizer (MFO) is used to control the LUO converter. MFO-Fuzzy is used to search the fuzzy rules and membership values to achieve minimum ISE, ITAE, settling time and peak overshoot. The proposed method is compared with fuzzy controller. Simulation results prove that the MFO algorithm is very competitive and achieves a high accuracy.
Keywords: Moth Flame Optimisation Algorithm, Fuzzy Logic Controller, Positive Output Elementary LUO Converter.
Scope of the Article: Fuzzy Logic