Background:
The PWM Boost converter is a strongly nonlinear discrete system, especially when the input
voltage or load varies widely, therefore, tuning the control parameters of which is a challenge work.
Objective:
In order to overcome the issues, particle swarm optimization (PSO) is employed for tuning the parameters of a
sliding mode controller of a boost converter.
Methods:
Based on the analysis of the Boost converter model and its non-linear characteristics, a mathematic model of a
boost converter with a sliding mode controller is built firstly. Then, the parameters of the Boost controller are adjusted
based on the integrated time and absolute error (ITAE), integral square error (ISE) and integrated absolute error (IAE)
indexes by PSO.
Results:
Simulation verification was performed, and the results show that the controllers tuned by the three indexes all
have excellent robust stability.
Conclusion:
The controllers tuned by ITAE and ISE indexes have excellent steady-state performance, but the overshoot is
large during the startup. The controller tuned by IAE index has better startup performance and slightly worse steady-state
performance.
Genetic algorithm assisted fixed frequency sliding mode controller for quadratic boost converter in fuel cell vehicle
