Study of Parallel Interleaved Control Buck Converter Algorithm Based on Particle Hybrid Swarm-Genetic

To enhance the transient regulation capabilities of step-down switching converters in power management applications, robustness and anti-interference capability, this study introduces an enhanced swarm intelligence-based control methodology for power converter optimization. By analyzing the working principle of the Buck converter, a new type of parallel interleaved control structure is designed, which employs two-phase multi-path complementary conduction, a shared freewheeling diode, and an LC filter circuit. This design significantly reduces the output ripple while decreasing the overall size and improving the efficiency of the system. An optimized transfer function is constructed to optimize the key indicator of dynamic response speed, achieving the goals of fast system dynamic response, good stability, and high steady-state accuracy. Subsequently, the particle swarm-genetic hybrid algorithm is used for global optimization, further enhancing the performance of the control strategy. Simulation results show that the optimized Buck converter has significant advantages in dynamic response, robustness, and anti-interference capability, verifying the effectiveness of the proposed method.