Hybrid Algorithm-Based Approach For Optimized PID Controller for Stability Analysis of DC-DC Interleaved-Buck Converter

Abstract

This thesis is an investigation of the stability analysis of the Interleaved Buck converter by inducing Nature-Inspired Algorithms to create an optimal PID controller. The applicability and compatibility of some algorithms such as PSO, Firefly Algorithm (FA), GWO and Cuckoo Search are used for optimizing the control mechanism of power converters. Improvements in performance parameters are noted, and the results are compared using multiple fitness functions. The thesis focuses on Interleaved converter which has the benefit of lowering ripple currents, simplifying EMI transmission, and faster transient response. The converters are constructed using the State Space Averaging (SSA) technique to provide promising feedback control and to evaluate the transfer functions. The Nature-Inspired Algorithms are non-linear optimization methods based on artificial intelligence. The above algorithms are based on swarm intelligence and operate in accordance with swarm creature customs. Swarm intelligence ensures better data exploitation, which concentrates the search method within the vicinity of optimal solutions while also assisting the procedure to escape the restriction of the local minima, resulting in successful exploration of the search space. With the help of these algorithms, we approached a hybrid algorithm named Hybrid Firefly Particle Swarm Algorithm (HFPSO). We also compared the performance according to the standard. As a result, the algorithms are evaluated for improved system performance using two fitness functions (ITAE, and ITSE) and performance parameters such as percentage of overshoot, rise time, settling time, and peak amplitude are all variables to consider. The procedure is carried using MATLAB. After studying the results, It is discovered that the percentage of HFPSO overshoot (ITSE) offers a lower for each of the error functions than PSO, CSA, FA, GWO, WOA.