Comparative Study on Flyback Converter with PID Controller and Neural Network Controller

A PID controller is extensively used in many fields because of its structure is very simple and can be controlled easily since modern advanced applications and industrial activities or processes demand to solve more complicated problems and nonlinearities. The conventional PID controller cannot meet these requirements. Neural network has a greater capability to cope with the demands of changing environment. Any continuous function can be accurately approximated using neural network so it has acquired a greater attention in the field of process control. This papers analyzes the performance of closed loop control of flyback converter using the traditional PID controller and adaptive neural network for varying load and source condition which could be used in renewable power conversion applications.

[1]  K. Suryanarayana,et al.  Digital peak current mode control of boost converter , 2014, 2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD).

[2]  K. Cheon,et al.  On Replacing PID Controller with Deep Learning Controller for DC Motor System , 2015 .

[3]  Robert W. Erickson,et al.  Fundamentals of Power Electronics , 2001 .

[4]  Muhammad Usman Asad,et al.  Application of neural network based model predictive controller to power switching converters , 2011, The 2011 International Conference and Workshop on Current Trends in Information Technology (CTIT 11).

[5]  Vedavyasa Kamath,et al.  Design and implementation of a universal input flyback converter , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).

[6]  K. Suryanarayana,et al.  Analysis and modeling of digital peak current mode control , 2012, 2012 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES).