This paper proposed a modified algorithm for controlling a single-phase split-source inverter. The proposed algorithm is a modified model predictive control based on Harris Hawks optimization, where the AC output voltage, the DC-link voltage, and the DC input current are controlled within one cost function. Hence, the discrete time models of both AC-side and DC-side are obtained. For proper operation of the modified MPC, each error term within the cost function has a weighting factor. Harris Hawks optimization technique is used to determine the weighting factors at each term of the cost function. The proposed algorithm is validated using MATLAB/Simulink. Simulation results show that the system has succeeded in controlling AC load voltage, input current, and achieving constant DC-link voltage over a wide operating range.

In this paper, for standalone and grid-connected PV systems, a three-phase simplified split-source inverter (SSI) is proposed and controlled using a model-predictive control (MPC). The maximum power point tracking (MPPT) approach used is an incremental conductance method based on a PI controller for both systems. The standalone system is composed of PV modules, a three-phase SSI, and a bidirectional power DC-DC converter that connects a battery bank and a DC-side capacitor. The output AC voltages of SSI are controlled using model-predictive control. The bidirectional power DC/DC converter regulates the DC-link voltage (DCLV). The grid-connected system consists of PV modules, a three-phase SSI, and an AC-side L-filter. The DC-link PI controller generates reference currents for the MPC algorithm. The MPC uses these reference currents to adjust and deliver the PV power to the grid while regulating the DCLV. The PI controllers’ parameters are selected for both systems using the Harris Hawks optimization method. Both PV systems simulation results show that under various operating conditions, they have succeeded in fixing a DCLV and producing a high-quality AC output voltage and current at low THD. Experimental results for the three-phase standalone PV system used to verify the system’s performance.

Permanent magnet synchronous motors (PMSMs) are widely employed in electric vehicles (EVs) owing to their high efficiency, high power density, and reliability. Although sensors may raise costs, the accurate estimation of both rotor position and actual speed is crucial to achieve high motor performance. This paper presents a comparative assessment of sensorless PMSM drive control using field-oriented control (FOC) and model predictive control (MPC) under various representative automotive drive cycle conditions. A model reference adaptive system (MRAS) observer is employed to estimate rotor position and speed, and the complete drive system is simulated via MATLAB/Simulink R2025b. Performance is assessed using representative synthetic urban and highway drive cycle segments, designed to capture key dynamic features (stop-and-go transients, steady-state cruising, and road grade variation) analogous to those found in standardized cycles such as WLTP, considering speed tracking error, torque ripple, and current/estimation quality indicators. The results indicate that FOC–MRAS provides robust steady-state behavior and reliable estimation under low-speed urban segments, whereas MPC–MRAS achieves faster transient response and improved current regulation in higher-speed segments at the expense of increased computational complexity. The reported findings elucidate the practical trade-offs between robustness and predictive capability and provide simulation-based guidance for selecting sensorless control strategies for PMSM-based EV traction drives.