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