Finite control set model predictive current control of a five-phase PMSM with virtual voltage vectors and adaptive control set

Abstract

This paper presents an improved finite control set model predictive current control (FCS-MPCC) of a five-phase permanent magnet synchronous motor (PMSM). First, to avoid including all the 32 voltage vectors provided by a two-level five-phase inverter into the control set, virtual voltage vectors are adopted. As the third current harmonics can be much reduced by virtual voltage vectors automatically, the harmonic items in the cost function of conventional FCS-MPCC are not considered. Furthermore, an adaptive control set is proposed based on voltage prediction. Best control set with proper voltage vector amplitude corresponding to different rotor speed can be achieved by this method. Consequently, current ripples can be largely reduced and the system performs much better. At last, simulations are established to verify the steady and transient performance of the proposed FCS-MPCC, and experiments based on a 2 kW five-phase motor are carried out. The results have validated the performance improvement of the proposed control strategy. Index Terms-Adaptive control set, current ripple, finite control set model predictive current control (FCS-MPCC), permanent magnet synchronous motor (PMSM), virtual voltage vectors.

Quasi‐discrete modelling of PMSM phase currents in drives with low switching‐to‐fundamental frequency ratio

“…where the discretisation step is equal to the control cycle duration T (inverse of the switching frequency), and the discrete current values i [k] , i [k+1] correspond to the start points of subsequent control cycles. According to (1) and 2, the current at the beginning of the forthcoming (k + 1)th cycle can be predicted using the model variables determined at the beginning of kth cycle [13,23,24]:…”

“…According to (1) and (2), the current at the beginning of the forthcoming ( k + 1)th cycle can be predicted using the model variables determined at the beginning of k th cycle [13, 23, 24]: where ‘*’ indicates a predicted value.…”

Research on Model Predictive Control of IPMSM Based on Adaline Neural Network Parameter Identification

“…Thus, all control systems are nonlinear to a certain extent. Considering the nonlinear characteristics of the system, common nonlinear control strategies such as sliding mode control [9,10], adaptive control [9,11] and intelligent control [12,13] are proposed. These nonlinear control strategies can improve the dynamic and static performance and the robustness of the system in different ways.…”

“…The key of FCS-MPC is to directly utilize the inherent discrete characteristics and the switch state of power converter. It can also solve the introduced problems including torque ripple and large current harmonic in FCS-MPC in some ways [9,11]. These significant advantages make it a hot topic in the research of predictive control of power electronic system models, and it has to be the best alternative to traditional current loop based on PI regulator.…”

Predictive current control for seven‐phase induction motor based on the optimal operating time of four‐dimensional vector

“…The vector operating time of each vector is used to evaluate the action effects on current, which replaces the predictive current part in the traditional model PCC. The problem that the duty cycle does not work effectively in the PCC with duty cycle modulation is solved [26][27][28][29].…”