[Objective] To address the issues of low robustness and high dependence on motor parameters in model predictive control (MPC) systems for dual three-phase permanent magnet synchronous motor (PMSM)

this study improves and optimizes the sliding mode parameter identification method. A dual three-phase PMSM model predictive current control (MPCC) method based on super-twisting sliding mode observer (ST-SMO) parameter identification is proposed. [Methods] Firstly

a higher-order sliding mode algorithm was introduced to replace the switching function sign in traditional sliding mode observers as the new sliding mode reaching law. A super-twisting algorithm-based ST-SMO was designed to achieve more accurate identification of motor inductance parameters. Then

stability analysis of the designed higher-order sliding mode observer was conducted using Lyapunov theory. Combined with incremental equations

the prediction model in the MPCC system was optimized

eliminating the effect of flux linkage parameters on the robustness of the motor control system. Finally

the inductance parameters were accurately identified using the parameter identification algorithm

reducing the dependence of the MPCC system on motor parameters. [Results] The improved dual three-phase PMSM MPCC system incorporating the ST-SMO and incremental prediction model demonstrated excellent steady-state and dynamic performance. For parameter identification

it eliminated the chattering phenomenon caused by the first-order sliding mode system

improved the accuracy of parameter identification

and enhanced the identification speed of motor parameters. Additionally

the control system maintained high control performance for the dual three-phase PMSM under parameter mismatch conditions. [Conclusion] The MPCC system based on ST-SMO parameter identification proposed in this study demonstrates good feasibility and stability under various operating conditions

such as speed and torque sudden transients.