Model Predictive Control and Parameter Identification for Aviation Vienna Rectifiers

Finite Control Set Model Predictive Control (FCSMPC) has gained widespread attention in power electronics for its fast dynamic response and multi-objective control capability. However, conventional FCS-MPC suffers from computational complexity and limited control accuracy at low frequencies. Moreover, its performance heavily depends on precise mathematical models, as parameter mismatches can degrade control stability, which is particularly critical in aviation power systems that demand stringent reliability. To address this, this paper proposes a virtual vector FCS-MPC control method for aviation Vienna rectifiers. The method achieves output voltage balancing and unity power factor control, while enhancing current control accuracy by outputting multiple basic vectors within one switching cycle. Additionally, a digital twin-based parameter identification method is proposed, where the RungeKutta method is used to establish discrete mathematical models for the aviation Vienna rectifier's power circuit and controllers. Particle Swarm Optimization (PSO) is then applied to optimize the circuit parameters of the digital twin, enabling precise identification of key parameters. The effectiveness of both the FCS-MPC control method and the parameter identification approach is validated through MATLAB/Simulink.