Model Predictive Current Control with Adaptive-adjusting Timescales for PMSMs

A model predictive current control(MPCC)with adaptive-adjusting method of timescales for permanent magnet synchronous motors(PMSMs)is proposed in this paper to improve the dynamic response and prediction accuracy in transient-state,while lessening the computational burden and improving the control performance in steady-state.The timescale characteristics of different parts of MPCC,such as signal sampling,prediction calculation,control output,model error correction,are analyzed,and the algorithm architecture of MPCC with multi-timescale is proposed.The difference between reference and actual speed,and the change rate of actual speed are utilized to discriminate the transient state of speed change and load change,respectively.Adaptive-adjusting method of control period and prediction stepsize are illustrated in detail after operation condition discrimination.Experimental results of a PMSM are presented to validate the effectiveness of proposed MPCC.In addition,comparative evaluation of single-step MPCC with fixed timescale and proposed MPCC is conducted,which demonstrates the superiority of proposed control strategy.

Finite Control Set Model Predictive Current Control of a Five-Phase PMSM with Virtual Voltage Vectors and Adaptive Control Set

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.

Zero-Sequence Current Suppression Strategy for Open-End Winding Permanent Magnet Synchronous Motor Based on Model Predictive Control

Compared with the traditional three-phase star connection winding,the open-end winding permanent magnet synchronous motor(OW-PMSM)system with a common direct current(DC)bus has a zero-sequence circuit,which makes the common-mode voltage and the back electromotive force(EMF)harmonic generated by the inverters produce the zero-sequence current in the zero-sequence circuit,and the zero-sequence current has great influence on the operation efficiency and stability of the motor control system.A zero-sequence current suppression strategy is presented based on model predictive current control for OW-PMSM.Through the mathematical model of OW-PMSM to establish the predictive model and the zero-sequence circuit model,the common-mode voltage under different voltage vector combinations is fully considered during vector selection and action time calculation.Then zero-sequence loop constraints are established,so as to suppress the zero-sequence current.In the end,the control strategy proposed in this paper is verified by simulation experiments.

Improved Optimal Duty Model Predictive Current Control Strategy for PMSM

To further improve the steady-state performance of the conventional dual vector model predictive current control(MPCC),an improved optimal duty MPCC strategy for permanent magnet synchronous motor(PMSM)is proposed.This strategy is realized by selecting an optimal voltage vector combination and its duration from the five basic voltage vector combinations,followed by acting on the inverter.The five combinations are:the combination of the optimal voltage vector at the previous moment and basic voltage vector with an angle difference of 60°;the combination of the optimal voltage vector at the previous moment and basic voltage vector with an angle difference of-60°;the combination of the aforementioned three basic voltage vectors with the zero vector.Experimental results indicate that the method effectively reduces the stator current ripple without increasing the calculational burden.Furthermore,it improves the steady-state performance of the system without altering the dynamic performance of the system.

Low-complexity model predictive control of a four-level active neutral point clamped inverter without weighting factors

The four-level active neutral point clamped(ANPC)inverter has become increasingly widely used in the renewable energy indus-try since it offers one more voltage level without increasing the total number of active switches compared to the three-level ANPC inverter.The model predictive current control(MPCC)is a promising control method for multi-level inverters.However,the conven-tional MPCC suffers from high computational complexity and tedious weighting factor tuning in multi-level inverter applications.A low-complexity MPCC without weighting factors for a four-level ANPC inverter is proposed in this paper.The computational burden and voltage vector candidate set are reduced according to the relationship between voltage vector and neutral point voltage balance.The proposed MPCC shows excellent steady-state and dynamics performances while ensuring the neutral point voltage balancing.The efficacy of the proposed MPCC is verified by simulation and experimental results.