2025 lEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE 2025)

The conventional model predictive control (MPC) is highly dependent on load parameters and has limited robustness. In this paper, a dual-vector model predictive control algorithm based on particle swarm optimization (PSO) for parameter identification is proposed. The PSO algorithm is used to identify the load parameters, while the dual-vector method enhances the prediction accuracy and robustness. MATLAB/Simulink is used for simulation analysis, and experiments are conducted to validate the effectiveness of the proposed method.

particle swarm optimization,parameter identification,dual-vector,robustness,model predictive control