PID Parameter Optimization By Using Ant Colony And Cuckoo Algorithms For Boost Converter

One of the most well-known and easy-to-design converter of DC-DC converters is DC-DC Boost converter. DC-DC boost converters are widely utilized in industrial applications. Therefore, output voltage regulation of the boost converter is a popular issue that researchers focus on. In this study, to improve output voltage of a Boost converter, Proportional-Integral-Derivative (PID) controllers of which the parameters are tuned by meta-heuristic optimization algorithms are designed. The Ant Colony Algorithm (ACO) and Cuckoo Search Algorithm (CSA) are the used optimization algorithms. The optimization algorithm results are compared to each other in addition to traditional trial-error method and simulation results are obtained in MATLAB/Simulink platform. The simulation results show that the optimized PID controller by both ACO and CSA is more effective in improving the transient and steady-state response of the DC-DC converter than the traditional tuning approach. In the optimization process, Integral Time-weighed Absolute Error (ITAE) performance metric is used as objective function. The performances of the controllers designed by ACO, CSA, and trial-error method are also compared to each other using Integral Squared Error (ISE), Integral Absolute Error (IAE) and ITAE performance metrics