Design of Generalized Hopfield Neural Network based Fractional Order PI controller for Average Current mode control of Synchronous SEPIC Converter

• Main Authors: Sridhar, Sundaramoorthy, Umamaheswari, M.G.
• Format: Conference Proceeding
• Language: English
• Subjects: Average Current Mode (ACM) Control; DC-DC power converters; DC-DC Synchronous Single Ended Primary Inductance Converter (SEPIC); Fractional Order Proportional Integral (FOPI) Controller; Generalized Hopfield Neural Network (GHNN); Genetic algorithms; Hopfield neural networks; Optimization; PI control; Steady-state; Voltage control
• ISSN: 2687-7767
• DOI: 10.1109/UPCON50219.2020.9376499

Synchronous rectified DC-DC Converters are widely used to power devices requiring low voltage and high current, including computer power supplies and PV-systems. This paper proposes Fractional Order Proportional Integral (FOPI) controllers for Average Current Mode (ACM) Control of synchronous SEPIC converter. The parameters of the FOPI controllers used in the inner and outer loop for given specifications are found by means of the Generalized Hopfield Neural Network (GHNN) based optimization technique. The converter's state-space model is derived using the state-space averaging technique by considering the parasitic resistance in each component. The transient and steady-state performances of the closed-loop system are analyzed and compared with conventional Proportional Integral(PI) controllers tuned using other optimization techniques such as Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) for variations in the input, reference voltage, and load variations using Matlab/ Simulink software tool. It is observed that the proposed GHNN based FOPI controller have better performance in terms of indices like less settling time, overshoot, steady-state error than conventional PI controllers tuned using GA and PSO based optimization techniques.