Optimization of a neural architecture for the direct control of a Boost converter

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In research related to control of DC/DC converters, artifi cial intelligence techniques are a great improvement  in  the  design  and  performance. However, some of these tools require the use of trial and error strategies in the design, making it diffi cult to obtain an optimal structure. In this pa-per, we propose a direct control based on artifi cial neural network, whose design has been optimized using  bio-inspired  searching  strategies,  with  the idea of    optimizing simultaneously two different but important aspects of the network: architecture and  weights  connections.  The  control  was  successfully  applied  to  a  boost  type  converter. The results obtained allow us to observe the dynamic performance of the scheme, in which the response time  and  variation  in  the  output  voltage  can  be concluded  that  the  criteria  used  for  the  control loop design were appropriate.