Performance evaluation of a GA-tuned PID controller for a buck-boost converter based on integral error metrics

Abstract

DC-DC buck-boost converters are widely used in photovoltaic (PV) systems to maintain a regulated output voltage under varying source and load conditions. Although genetic algorithms (GA) based proportional-integral-derivative (PID) controllers are commonly used, the influence of different integral error performance indices on controller behavior has not been systematically examined. This paper presents a detailed study of GA-based PID tuning for a buck-boost converter operating in both buck and boost modes. Four integral error metrics-integral absolute error (IAE), integral time absolute error (ITAE), integral square error (ISE), and integral time square error (ITSE)-are employed as fitness functions to obtain optimal controller gains. A detailed MATLAB/Simulink model of the converter working in continuous conduction mode (CCM) is developed to evaluate controller performance under step input, source transients, and load transients. The results demonstrate that the selected error metric significantly affects transient characteristics, including rise time, settling time, overshoot, and steady-state error. Controllers tuned using ITAE and ITSE provide better transient performance compared to IAE and ISE based tuning. The results highlight the critical role of objective function selection in GA based PID optimization and provide practical guidelines for buck-boost converters in PV applications.