Analytic Hierarchy Process-based Control System Optimization for Buck Circuit using Genetic Algorithm

Many control schemes are employed in the buck-boost converter. Among them, non-singular terminal sliding mode control exhibits strong robustness and resolves singularity issues in terminal sliding mode control. However, the selection of controller parameters poses a significant challenge as it affects system response time and electronic switch losses, among others. The weight factor allocation in the fitness function optimized by traditional genetic algorithms is in need of improvement. Therefore, from the perspective of theoretical design, this paper proposes a method to design fitness function weights using the analytic hierarchy process, and performs multi-parameter optimization through genetic algorithms. Firstly, the AHP is utilized to analyze the weight coefficients of different indicators in the fitness function. Then, these coefficients are incorporated into the genetic algorithm to optimize the controller parameters. Finally, the effectiveness of the Analytic Hierarchy Process design for fitness function weights was validated through a genetic algorithm.