Study on boost converter control strategy based on particle swarm optimization single neuron PID

Abstract

Aiming at the poor effect of conventional PID control in boost converter with nonlinear and time-varying characteristics, this work suggests a boost converter control technique based on Particle Swarm Optimization Single Neuron PID (PSO-SNPID). First, the ability of self-learning and self-adaptation of single neurons is utilized to adjust the weights online by learning rules, which in turn achieves the purpose of online rectification of PID parameters; Second, the learning rate and gain coefficient of the Single Neuron PID (SNPID) are optimized by the PSO algorithm as a way to improve the accuracy of the model; Finally, simulation experiments were carried out in Matlab/Simulink platform, and the results proved that the proposed method avoids the difficulty of manual parameterization and has better tracking performance and stronger robustness than the conventional PID control and Single Neuron PID control.