AI-Based Optimization of a DC-DC Buck Converter Control Network Across DCM and CCM Operating Region

In this paper we propose an automatic controller design methodology for DC-DC converters that comprehensively addresses both Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM). This methodology leverages on Artificial Intelligence (AI) techniques. Specifically, we resort on the Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) methods. Both GA and PSO permit to optimally tune the component values employed in the compensation network, overcoming the limitations of traditional design methods. The latter focus indeed solely on CCM, leading to significant performance degradation in DCM operation. The proposed methodology can be seamlessly integrated into DC-DC converter design phase, and it is not restricted for specific DC-DC topologies or control architectures. As a case study, we apply the proposed approach to the design of a Type-Iii compensation network in a voltage-mode controlled Buck converter, aiming to improve the load-transient response. The optimization process is carried out in MATLAB. Then, a performance comparison with the conventionally designed controller is conducted via SIMPLIS simulations. An improvement in overall performance is demonstrated.