Model-free three-vector predictive current control of permanent magnet synchronous motor based on improved sliding mode observer

Multi-vector model predictive control of permanent magnet synchronous motor can effectively overcome the shortcomings of conventional single-vector model predictive control such as large motor current ripples and limited control accuracy, but its control performance is more susceptible to the influence of motor parameter perturbation. To improve the robustness of the multi-vector predictive control algorithm under the motor parameter perturbation, a novel nonparametric three-vector model predictive current control method for permanent magnet synchronous motors is proposed in this paper. Through constructing an ultra-local predictive current model based on the input and output signals of the permanent magnet synchronous motor, the influence of motor parameter perturbation on current prediction is avoided, and a disturbance observer is designed to estimate the non-modeled and perturbed parts of the ultra-local model. In addition, the direct calculation method of vector duty cycles based on current error is introduced to suppress the influence of motor parameter uncertainty on the duty cycle calculation link, which further improves the system robustness. Finally, simulations and experiments of the proposed method are demonstrated to compare with the conventional parametric three-vector model predictive current control, and the results show that the proposed control strategy can effectively suppress the disturbances caused by the motor parameters and ensure the steady-state performance during motor operation.

This study presents two improved designs of eccentric-shaped permanent magnets and teeth-shaped stators in radial-flux dual three-phase permanent magnet electric machines to reduce cogging torque and torque ripple. The finite element analysis (ANSYS Electromagnetics) has been adopted in simulation, and real radial-flux dual three-phase permanent magnet electric machines have been fabricated in experiment to verify the study. Using the radial-flux dual three-phase permanent magnet electric machines in electric machine systems can improve the reliability and obtain higher output torque. In electrical drives and control, a simplified model-free predictive current control method has been proposed and implemented to drive the radial-flux dual three-phase permanent magnet electric machines, and the control law has been achieved by a TMS320F28377S microcontroller of Texas Instruments. The simplified model-free predictive current control method is senseless to parameter variations and back electromotive force of the permanent magnet electric machines, and only needs current sensors to detect six-phase current. The optimal one has been chosen by 14 various switching modes, which has the minimum cost function, and then the converter can be directly driven and controlled in the next sampling period. The features of the simplified model-free predictive current control method can reduce the algorithm calculation and avoid the defect of conventional model-based predictive current control scheme. A proportional-integral speed controller has also been designed to achieve the speed response of the fixed-speed tracking effect. Finally, the feasibility and effectiveness of the proposed simplified model-free predictive current control method for the dual three-phase permanent magnet electric machines can be verified in the experimental and quantitative results.

For multiobjective model predictive current control (MPCC) method with penalties, the penalty weights are introduced into the cost function to determine the relative importance of control targets. However, the dimensions and value ranges of each control target are different. Thus, the penalty coefficient is difficult to determine. To avoid the difficulty of dimensionless parameter design, this article proposes a multiobjective control method called MPCC with multiple current bounds. In this article, the switching frequency and the common-mode voltage (CMV) are selected as the control target. Their control is converted into the limitation of the current ripple so that the control parameters have a clear physical meaning. On the one hand, the proposed method can realize the cooperative control of reference current tracking, switching frequency suppression, and CMV suppression. On the other hand, the key control parameters have the same dimension and strong correlation with others. Therefore, appropriate parameter design can be realized. Experimental investigations for permanent magnet synchronous motor prove the effectiveness of the control method.

Switching losses minimization and performance improvement of PCC and PTC methods of model predictive direct torque control drives with 15-level inverter

Model predictive current control has high control accuracy and good control performance, and has been widely applied in the control strategy of dual three-phase permanent magnet synchronous motors. This article proposes an improved deadbeat model predictive current control (DMPCC) method by analyzing the mathematical model of a dual three-phase open-winding permanent magnet synchronous motor. One inverter predicts and calculates the optimal voltage vector through the deadbeat model, while the other inverter calculates the optimal voltage vector through the value function rolling optimization. This method reduces the computational workload of the controller, avoids the delay problem of the traditional model predictive current control (MPCC) method, improves the system's response speed, effectively suppresses the current in the harmonic sub-plane, and reduces torque ripple. The simulation results have verified the accuracy and speed of the above control method.

Conventional model predictive current control (MPCC) applies only one vector during one control period, which produces large torque and flux ripples and high current harmonics in permanent magnet synchronous motor (PMSM) drives. Recently MPCC with duty cycle control has been proposed to improve the steady state performance by applying one non-zero vector and one null vector during one control period. However, the prior method requires lots of calculations and predictions to find the optimal voltage vectors and calculate their respective duration. Different from prior enumeration-based MPCC, this paper proposes an efficient two-vector MPCC by applying two arbitrary voltage vectors during one control period. The reference voltage vector is firstly calculated based on the principle of deadbeat current control. Two optimal vectors and their duration are then obtained in a very efficient way, which does not require the calculation of current slopes in prior MPCC methods. The proposed method is compared to the state-of-the-art predictive control methods, including conventional MPCC, MPCC with duty cycle control, deadbeat control with space vector modulation (SVM) and modulated model predictive control (M2PC). Both simulation and experimental results prove that the proposed method achieves better steady state performance than conventional MPCC with or without duty cycle and the dynamic response is not degraded. Under the condition of insufficient dc bus voltage, the proposed method outperforms deadbeat control and M2PC by presenting even higher speed range and less torque ripples.

To improve the dynamic and steady-state control performance of permanent magnet synchronous motors under the three-vector model predictive current control method, this study proposes a switching current predictive control method based on the exponential moving average algorithm, which evaluates the magnitude of the change of the q-axis current slope in real time to discriminate the motor’s operating conditions and selects the optimal control method for different operating conditions. Meanwhile, the traditional three-vector model predictive current control method is improved by introducing a comparison mechanism for the q-axis current slope to select the second effective voltage vector, avoiding the secondary optimization calculation of the value function and reducing the computational complexity of the traditional method. By comparing the proposed method with the traditional three-vector model predictive current control method, the experimental results prove that the proposed method improves the system’s dynamic response and steady-state performance.

Bidirectional DC-DC converters are widely used in photovoltaic energy storage systems, and good dynamic response performance is the key to efficient and stable circuit operation. A model predictive current control method is supplied to lessen the processor's computational load and optimize the converter's dynamic performance. Analyze the bidirectional DC-DC converter's utilization and establish a current control cost function, so that the predicted current tracks the given reference power. Current control avoids the selection of multi-objective optimization weights in the cost function, making the calculation process of the processor more simplified. Simulation and experimental validation have confirmed the effectiveness of the proposed model in predicting current control under steady-state conditions, and it has faster response speed and smaller overshoot compared to traditional PI control.

This study proposes a novel robust predictive current control that is based on a discrete‐time disturbance observer for an interior permanent magnet synchronous motor (IPMSM), does not require rotor flux information. To confirm the effects of the current control response on a parameter mismatch, the parameter sensitivity for the current prediction of a conventional deadbeat predictive current control (DPCC) is analysed. With the proposed method, disturbances owing to a parameter mismatch, rotor flux term, and unmodelled dynamics are estimated using a Luenberger observer in the discrete‐time domain. The estimated disturbances are compensated with the predicted reference voltage model considering a digital delay. The stability of the proposed disturbance observer owing to a parameter mismatch of the stator resistance and d–q inductance is also analysed. The proposed method is robust against the stator resistance and an inductance variation, and an accurate predicted current control can be obtained without an offline or online estimation of the rotor flux. Compared with the conventional DPCC, the proposed method can eliminate a steady‐state current and transient state error caused by disturbances of the system. Experimental results are presented to verify the proposed control scheme even with mismatched parameters of the IPMSM.

The five-phase voltage source inverters (VSIs) have drawn more and more attention in the recent years, and required the extension of control schemes already well known for three-phase voltage source inverters. Nevertheless, the low-order harmonic currents suppression is a special issue of control methods for five-phase VSIs with the common-mode voltage (CMV) still remaining to be reduced. Model predictive current control (MPCC) method has been developed as a widespread and effective control technique in power electronics, especially for multiple constraints system, and has been applied to five-phase VSIs with the consideration of low-order harmonic currents suppression and CMV reduction, simultaneously. However, the complex cost function and huge computation amount in the existing works become another deficiency, and the weighting factors of different components in the cost function is difficult to adjust. In this paper, a new MPCC method based on virtual voltage vectors has been proposed for five-phase VSIs. The zero and medium voltage vectors have been eliminated and by this way the CMV can be reduced by 80%. Then, following the volt-second balance law, ten active virtual voltage vectors and active zero vectors are synthesized to acquire zero volt-second value in x-y subspace, as a consequence, low-order harmonic currents are suppressed. Finally, a simplified cost function without weighting factor is adopted. Simulation and experimental results have verified that the proposed MPCC method can reduce CMV by 80% and suppress low-order harmonic currents successfully.

During the past few years, finite control set predictive current control (PCC) method has attracted more and more attention in research and industry applications. However, PCC method could be improved by considering two points. First, the current reference used in the cost function of PCC control scheme is usually produced by a proportional and integration speed controller. Confront with load torque and time-varying system parameters, it is a better solution to design a disturbance estimation based feed-forward compensated controller. In this way, the current reference could be generated faster and more accurate. Second, the PCC method is model-based method which means the accuracy of the model parameters are essential. In real system, time-varying parameters existed almost always. This paper investigates a generalized proportional integral observer based PCC approach for dealing with load torque disturbance, time-varying parameter uncertainties. The effectiveness of the proposed method has also been confirmed by simulation and a lab-constructed experimental prototype.

This article proposes an improved model-free deadbeat predictive current control (MFCC) method for permanent magnet synchronous motors (PMSMs) based on the ultralocal model and H∞ norm. Firstly, the traditional deadbeat predictive current control (DPCC) method is introduced and a theoretical analysis is conducted on its sensitivity to parameters. Building upon this, the limitations of model dependence and the limited robustness of the deadbeat predictive current control method based on the extended state observer (ESO-DPCC) are theoretically analyzed. Furthermore, an improved MFCC method based on the ultralocal model is proposed, and the influence of the observer on MFCC is theoretically analyzed. This study combined the proposed method with the H∞ norm, and the optimal coefficients of the observer were tuned to enhance the robustness and dynamic performance of the current loop. Finally, the proposed algorithms were validated on a 400 W PMSM platform.

The general model predictive current control uses a discrete-time model of the system to predict the future current behavior for all the possible voltage vectors generated by the inverter, and then the vector which minimizes a cost function is selected and applied. In this paper, a simplified model predictive current control method for a three-phase voltage-source inverter is presented in order to reduce the amount of calculations in the practical implementation of this method, especially for multilevel ones with large predictions. Utilizing the sector information of the reference voltage space vector, the proposed method just needs a subset of all the available voltage vectors for the prediction and optimization. The computational effort has been greatly reduced while the steady-state performance and dynamic response of the current control are improved. In addition, a reduced switching frequency is obtained. Simulation results are presented to verify the proposed method.

A current sharing method considering battery state of charge(SOC) for multi-module uninterruptable power supply(UPS) system is proposed. Modularized UPS system consists of a single master module and multi slave modules. A master module performs output voltage control and distributes current commands to slave modules. The current commands are generated by using the proposed current sharing algorithm to balance the SOC of each UPS module. Moreover, the UPS mode transfer time can be significantly reduced by using the predictive current control. The predictive current control method is mathematically verified by analyzing the UPS system. The validity of proposed balancing method and predictive control is verified by simulation results.

In this paper, using the two control methods of model predictive control (MPC), model predictive current control (MPCC) and model predictive torque control (MPTC) is applied to the control system of permanent magnet synchronous motor(PMSM). The algorithms are based on the minimization of different cost functions, and the corresponding optimal voltage vector can be obtained. Comparison of the dynamic and steady state performance of the tested PMSM with MPCC and MPTC is elaborated. The delay compensation is considered to reduce the fluctuation of current and torque, in order to reduce the computational burden by using a single vector, comparison of two methods in the speed control system of dynamic and steady-state performance and speed tracking performance and resistance to load disturbance. The simulation results show that both MPCC and MPTC can be well applied to the PMSM driving systems with good speed tracking performance. However the MPCC can achieve less harmonics in the stator currents, and consequently the circular flux linkage trajectory is smoother, while the MPTC shows advantages in smaller ripples in the mechanical torque.