Theoretical and Experimental Comparative Analysis of Finite Control Set Model Predictive Control Strategies

Abstract

This research paper studies and highlights the features of the most popular finite control set model predictive control (FCS-MPC) strategies available in the state of the art, which are the optimal switching vector (OSV-MPC), modulated model predictive control (M2PC), and optimal switching sequence (OSS-MPC) methods. Thus, these strategies are studied experimentally by analyzing the transient and steady state performance using a grid tie conventional three-phase two-level voltage source inverter (VSI) with inductive output filter in a Typhoon HIL real-time simulator (RTS) with a Texas Instruments F28379D digital signal processor (DSP). Hence, quantitative indicators, such as the maximum tracking error, the mean absolute error, the settling time, the total harmonic distortion, the switching frequency spectrum, the switching pattern, and the computational burden are compared with the aim to deduce the best strategy for each criteria.

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

“…Total harmonic distortion(THD) is the ratio of the root mean square value of each harmonic (2nd, 3rd to nth) of the signal to the root mean square value of the fundamental waveform and is an important indicator of the quality of a sine waveform. The current THD is an important performance parameter, as grid-tie converters must follow legislation limits to be connected to the grid [24,25]. The output current waveform and THD are shown in Figure 6.…”

“…When the frequencies do not match, if the kT s moment is the sampling moment, the following sampling moment is the (k + N)T s moment. From the system state space Equation (24), the expression of the system state quantity i at the first non-sampling moment (k + 1)T s can be obtained.…”

Unscented Kalman Filter Sensorless Permament Magnet Synchronous Motor Model Predictive Control

“…MPC operates on an iterative control concept, leveraging a system model to predict future states within finite horizons, considering constraints and dynamic behaviors with necessity of knowledge of full system state -measured or estimated.. Notably, it has found successful applications in electromechanical systems in [24,25] and robotics in [26]. Recent literature as [27] introduces MPC solutions as for electric drive control, including current control in Induction machines [16,28,29], and even in the domain of brushless DC Motors with separate speed and current control [30].…”

Unscented Kalman filter Sensorless Permament Magnet Synchronous Motor Model Predictive Control

“…MPC operates on an iterative control concept, leveraging a system model to predict future states within finite horizons, considering constraints and dynamic behaviors. Notably, it has found successful applications in electromechanical systems in [25,26] and robotics in [27]. Recent literature as [28] introduces MPC solutions as for electric drive control, including current control in Induction machines [16,29,30], and even in the domain of brushless DC Motors with separate speed and current control [31].…”