Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems

Abstract

This article presents a multiple-vector finite-control-set model predictive control (MV-FCS-MPC) scheme with fuzzy logic for permanent-magnet synchronous motors (PMSMs) used in electric drive systems. The proposed technique is based on discrete space vector modulation (DSVM). The converter’s real voltage vectors are utilized along with new virtual voltage vectors to form switching sequences for each sampling period in order to improve the steady-state performance. Furthermore, to obtain the reference voltage vector (VV) directly from the reference current and to reduce the calculation load of the proposed MV-FCS-MPC technique, a deadbeat function (DB) is added. Subsequently, the best real or virtual voltage vector to be applied in the next sampling instant is selected based on a certain cost function. Moreover, a fuzzy logic controller is employed in the outer loop for controlling the speed of the rotor. Accordingly, the dynamic response of the speed is improved and the difficulty of the proportional-integral (PI) controller tuning is avoided. The response of the suggested technique is verified by simulation results and compared with that of the conventional FCS-MPC.

Optimal Load Distribution in DG Sources Using Model Predictive Control and the State Feedback Controller for Switching Control

“…Figure 4: Principles of the operation of the predictive method. Hence, the following state vectors are given that by equation (13).…”

“…is new technique is called the discrete space vector modulation-model predictive control (DSVM-MPC), using which the required sampling frequency is reduced, while the switching frequency of the converter is stabilized [12]. In addition to the advantages of the FS-MPC method, this control method also provides other benefits, including fixed switching frequency and low sampling frequency [13]. However, due to the use of the same algorithm and its discrete nature, it includes a limited and discrete number of converter vector space points.…”

Optimization of a Permanent Magnet Synchronous Motor for e-Mobility Using Metamodels

“…Permanent magnet synchronous motors (PMSMs) have been widely used in a variety of industries (such as appliances, industrial tools, and driving motors for electric vehicles) because of their excellent efficiency, torque density, and low maintenance [4][5][6][7]. PMSMs with rectangular coils in hairpin winding arrangements can reduce the space factor by reducing the unnecessary space in the slots when compared with those with round coils [8].…”

Low-Speed Transient and Steady-State Performance Analysis of IPMSM for Nonlinear Speed Regulator with Effective Compensation Scheme

“…Several control strategies based on conventional controllers, such as proportional-integral (PI) control [5,6] have attracted attention owing to their simplicity and ease of implementation on hardware; however, they are highly dependent on actual drive parameters and require exact parameter values to tune the PI gain to achieve efficient closed-loop performance. To eliminate the parameter dependency on the control design, nonlinear controllers have been developed [7][8][9][10][11][12][13][14]. In previous research [7][8][9], deadbeat control showed excellent speed tracking performance, but it depends highly on the IPMSM parameters to achieve efficient tracking performance by setting the closed-loop poles to zero.…”

“…In previous research [7][8][9], deadbeat control showed excellent speed tracking performance, but it depends highly on the IPMSM parameters to achieve efficient tracking performance by setting the closed-loop poles to zero. Fuzzy logic controllers [10,11] can effectively deal with drive nonlinearities and model unknown parameter uncertainties. However, this control scheme depends highly on gains and requires extensive knowledge to choose appropriate fuzzy interference rules to achieve excellent speed tracking.…”