Multi-Sampling Rate Finite Control Set Model Predictive Control and Adaptive Method of Single-Phase Inverter

Abstract

With the development of power switches and processor performance in recent years, the control frequency of inverters has been significantly improved. However, limited by technology and price, the sensor sampling frequency in large-scale industrial applications is much lower than the inverter control frequency that can be realized. This frequency mismatch limits the performance improvement of the inverter. In this article, the current and voltage at the non-sampling time are reconstructed using the current prediction control principle and the input observer theory, allowing a single-phase inverter to implement multi-sampling rate control with a low sampling frequency and high control frequency. In addition, an improved adaptive controller is designed to solve the effect of incorrect model parameters, which realizes adaptive control when the sampling frequency and control frequency are mismatched. Finally, the effectiveness of the method is verified through a simulation and experiments. The proposed method can solve the problem of high-speed switching for inverters under low-sampling-frequency conditions, improving the inverter’s adaptive performance and robustness.

FPGA-Based Extended Control Set Model Predictive Current Control with a Simplified Search Strategy for Permanent Magnet Synchronous Motor

“…Permanent magnet synchronous motor (PMSM) has been widely employed in various industrial applications due to the advantages of high torque (power) density, high efficiency and removal of brush and slip rings [1][2][3]. Model predictive control (MPC) is emerging as a promising technique for PMSM drives over the past decade [4][5][6][7], which has proven its intuitive concept and exceptional dynamic performance in other applications [8][9][10][11][12].…”

“…Based on the different voltage vector output methods, MPC strategies can be categorized into finite control set MPC (FCS-MPC) [9,10] and continuous control set MPC (CCS-MPC) [11,12]. Apart from its superior dynamic performance, FCS-MPC offers the merit of multi-objective integrated optimization [13], achieved through the high flexibility of cost function design and the traversal approach for vector determination strategy.…”