[Objective] To address the issues of limited number of control sets and the presence of the system-wide disturbances in motors under finite set model predictive current control

a duty cycle control set model predictive control (ADCS-MPC) strategy based on an anti-disturbance prediction model is proposed. [Methods] Firstly

a mathematical model of the surface-mounted permanent magnet synchronous motor (SPMSM) under time-varying parameters and unmodeled disturbances was established. By designing a current extended state observer to observe unmodeled and time-varying parameter disturbances

the current harmonics caused by inaccurate prediction models were avoided and the robustness of the controller was improved. Secondly

based on finite set model predictive control

a discrete duty cycle control set optimization scheme was designed using the basic voltage vector. By expanding the 6 effective basic voltage vectors into 60 effective virtual voltage vectors

the prediction deviation in each control cycle was reduced. The optimal and suboptimal voltage vectors were used to determine the sector where the target vector was located. A sector judgment mechanism and voltage vector positioning strategy were designed to save control cycles

and the optimal vector with low prediction deviation was iteratively produced to reduce the computational complexity of the method. [Results] Finally

a driving system experimental platform for SPMSM based on TI’s 32-bit floating point DSP TMSF28379D microprocessor was designed and built

the algorithm proposed in this paper was experimentally verified using this platform. And it was verified that the proposed ADCS-MPC strategy is capable of eliminating prediction errors and enabling disturbance-free current control

particularly in suppressing time-varying parameter disturbances and reducing current harmonic. [Conclusion] The proposed ADCS-MPC strategy mitigates the adverse effects of unmodeled and time-varying parameter disturbances on the system

improves the control accuracy within a single control cycle with minimal computational cost

significantly reduces prediction error

and enables the system to achieve fast response and high control accuracy.