This paper proposes a BPPID control system based on an improved sparrow algorithm to address the problem of getting stuck in local optima when optimizing the initial weights of BPPID using the traditional sparrow algorithm. Improving population diversity by introducing composite chaotic mapping; Utilizing the golden ratio and adaptive Levy flight strategy to balance the algorithm's global search and local development capabilities; Using fuzzy logic adaptive reverse learning strategy to improve the algorithm's global search and adaptability to complex environments. The benchmark functions were tested using standard sparrow algorithm, improved sparrow algorithm, grey wolf optimization algorithm, whale optimization algorithm, improved whale optimization algorithm, particle swarm optimization algorithm, and improved particle swarm optimization algorithm to compare and verify the effectiveness of the improved sparrow algorithm. The experimental results showed that the system efficiency and fairness of the improved sparrow algorithm were superior to other algorithms. Applying the improved sparrow algorithm to solve the initial weights of BPPID in switch mode power supply systems can significantly improve the system's dynamic response and reduce overshoot.