Finite Control Set Model Predictive Control (FCS-MPC) has the ability to achieve multi-objective optimization, but there are still many challenges. The key to realizing multi-objective optimization in FCS-MPC lies in the design of the cost function. However, the different dimensions of penalty terms in the cost function often lead to difficulties in designing weighting coefficients. Incorrect weighting coefficients may result in truncation errors in calculations of DSPs and FPGAs, thereby affecting the algorithm’s control performance. Therefore, this article focuses on a system driving an induction motor with a three-level Neutral Point Clamped (NPC) inverter, and selects stator current and switching frequency as penalty terms in the cost function. An improved method is proposed to unify the dimensions of both penalty terms in the cost function. By unifying the dimensions of the penalty terms, a simple design of weighting coefficients can be achieved. Subsequently, to balance the inverter’s switching frequency and the dynamic response performance of the motor, a composite cost function is further proposed. Finally, the rationality of the proposed method is validated through simulation and experimental platforms.

In parallel interleaved multilevel inverter (PIMI) applications, the objectives of suppressing the phase circulating current (PCC) and the zero-sequence circulating current (ZSCC) under conventional SVPWM are often conflicting. This makes it challenging for conventional modulation strategies to achieve their coordinated mitigation. To address this issue, this paper proposes an improved asymmetric regular sampling variable-switching-frequency PWM (ARS-VSFPWM) strategy incorporating a cost function model. First, to overcome the failure of symmetric regular sampling (SRS) under low-switching-frequency conditions, a predictive model for PCC and ZSCC is established based on ARS while accounting for control delay. Then, cost-function-based online optimization of vector selection achieves qualitative coordinated suppression of both circulating currents. Furthermore, variable switching frequency introduces an additional degree of freedom, dynamically adjusting the switching frequency per control cycle to quantitatively constrain peak circulating currents to predefined thresholds. Finally, results from 36-MW simulations and experiments validate the effectiveness and superiority over conventional methods.