Artificial intelligence–assisted modeling and adaptive control strategies are developed for nonlinear power electronics systems. Through the integrated use of machine learning, parameter estimation, and real-time data acquisition infrastructures, digital twin models with learning capability are created, enabling predictive fault diagnosis and performance optimization.

This approach combines classical circuit theory with intelligent system behavior, enabling the development of converter and drive systems capable of adapting to changing operating conditions.