Online Learning-Based Multistep Predictive Control for PMSM Drives With a Small DC-Link Capacitor

Power converters with a small dc-link capacitor are designed to increase the lifetime by replacing bulky electrolytic capacitors or power factor correction circuits, presenting an attractive solution. However, when these power converters are connected to a weak grid, a resonance can occur between the line-side impedance and the dc-link capacitor. This resonance can lead to issues such as increased total harmonic distortion (THD) of the line current due to ac line harmonics. This article proposes an advanced finite control set model predictive control (FCS-MPC) methodology with a multistep ahead prediction horizon for a three-phase diode rectifier system equipped with a small dc-link capacitor, driving a permanent magnet synchronous motor (PMSM). The FCS-MPC cost functions are designed to control the currents on both the load and dc-link sides, aiming to reduce THD and suppress harmonics in the grid-side current. To adapt the system to varying operational conditions and ensure stability under variable load, torque, or speed conditions, two brain emotional learning-based intelligent controllers (BELBIC) are employed to design the weighting factors, aiming for a significant reduction in harmonics on both the grid and load sides. The effectiveness of the proposed method is validated through experimental tests.