Improved Sequential Model Predictive Control of Open-Winding Permanent Magnet Synchronous Motor With Pseudo 3-D Space Vector Modulation

Improved model predictive current control (MPCC) strategies in 3-D space are proposed in this article for an open-winding permanent-magnet synchronous motor (OW-PMSM) system with a common dc bus. In order to simply determine the optimal voltage vector (VV), a cost function related to VVs is established to replace that related to stator currents in conventional MPCC and the weighting factors are obtained by theoretical derivation. In order to reduce the calculation time of the optimal VV, a 3-D space system is established and the candidate VVs and the reference VV can be placed in the 3-D space. Then by means of the space geometric theory, the optimal VV can be selected faster and only four calculations are required in each sampling period. For better tracking of the reference VV, a VV-tracking (VVT) MPCC strategy is proposed and three VVs are selected according to the volt-second balance principle. Experimental results verify the effectiveness of the proposed strategies.

Nowadays, Predictive Torque Control (PTC) strategy is recognized as a strong tool for controlling the motor drive. Intuitive and multi-objective controlling are significant benefits of PTC. Owing to these benefits, its application is introduced for Open Winding Permanent Magnet Synchronous Motor (OW-PMSM) drive. The basic PTC operated OW-PMSM drive consequences high Common Mode Voltage (CMV) and leads to early failure of motor bearings. In this paper, CMV elimination is proposed for OW-PMSM drive using voltage vector selection in PTC operation. In proposed PTC, the possible voltage vectors (VVs) are identified to gain zero CMV and preselected as prediction VVs for cost-function evaluation. From cost-function evaluation, optimal VV is considered for controlling OW-PMSM drive. In addition, decrease in switching frequency is achieved through proper utilization of optimal VV’s redundant switching states. Therefore, the overall modifications in proposed PTC of OW-PMSM drive ensure simple operational control, CMV elimination, and switching frequency and loss reduction. The claims of proposed PTC are verified through Matlab/Simulink platform and its proficiency is highlighted against basic PTC operation. Thus, the enhanced operation of proposed PTC for OW-PMSM drive with zero CMV is justified.KeywordsCommon Mode Voltage (CMV)Open Winding Permanent Magnet Synchronous Motor (OW-PMSM) drivePredictive Torque Control (PTC)

A simplified model predictive control (S-MPC) for open-winding permanent magnet synchronous motor system fed by two isolated power sources is proposed in order to remove the complex tuning work of weighting factors and reduce the calculation burden in the conventional MPC. Control constraints torque and flux in the conventional MPC are converted into an equivalent flux vector for the purpose of eliminating the weighting factor in the cost function. Moreover, a novel prediction strategy and a fast voltage selection scheme are presented to reduce the system calculation burden. As a result, the optimal voltage is selected through only twice evaluations. Furthermore, a switching states distribution strategy is introduced to reduce the switching frequency of a dual inverter. Comparative experiments are conducted to verify the effectiveness of the proposed method.

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.

This work presents a Sequential Model Predictive Voltage Control of packed u cell (PUC) for grid connected applications. PUC inverter has become a research of interest due to the simultaneous advantages of flying capacitor and cascaded H bridge with less component count. Grid connected PUC provides the challenge of simultaneous control of link voltage and load current. Model Predictive Control provides superior multi objective, multi constraint handling and simplicity in implementation. The principle of model predictive control is about minimizing the cost function to get an optimized switching vector for every sample. Sequential model predictive control is a weight factor less algorithm to simultaneously control any number of control variables. However, the performance highly depends on the priority and the switch redundancy of the power converter. Performance analysis of the sequential MPC under various operating conditions has been presented for a PUC inverter. MATLAB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> / SimPowerSystems has been used to perform the simulations and performance has been compared with the existing weight factors based model predictive control algorithm.

This paper proposes an event-triggered variable-sequential model predictive control (ET-VSMPC) to address the issues of high switching frequency and cumbersome weighting factor adjustment in conventional model predictive control (CMPC). The proposed ET-VSMPC has three key features: First, a novel variable-sequential model predictive control (VSMPC) that dynamically adjusts the optimization sequence of control objectives is proposed, thereby resolving the suboptimal issues caused by the fixed control sequence in traditional sequential model predictive control (SMPC). Second, an event-triggered mechanism is incorporated into VSMPC, which triggers control updates only when system states deviate beyond preset thresholds. This significantly reduces the average switching frequency while maintaining robust control performance. Third, ET-VSMPC relaxes the constraints on control objectives through event-triggered conditions, overcoming the limitation of fixed candidate vector numbers in SMPC. This enhances the algorithm's flexibility and adaptability. Finally, both simulation and experimental results validate that the proposed ET-VSMPC exhibits excellent steady-state and dynamic performance. Under identical switching frequencies, ET-VSMPC achieves superior current and voltage control performance compared to CMPC, while improving computational efficiency by 36%.

Traditional model predictive torque control (MPTC) predicts the torque and flux values for the next time step and selects the voltage vector that minimizes the cost function as the optimal vector to apply to the inverter. This control approach is straightforward and allows for multi-objective control, but it has some issues in terms of the dynamic steady-state performance and parameter robustness. Therefore, this paper proposes a weightless model predictive control method based on an extended state observer (ESO). By designing an improved ESO to observe and compensate for motor parameter disturbances in real time, and employing a novel 2-D switching table and voltage vector sector selection diagram, the method evaluates three out of eight voltage vectors based on the torque and stator flux error signals. This reduces the computational load while increasing the number of candidate voltage vectors. Finally, a cost function without weighting factors is designed to lower the computational complexity. The simulation results show that the proposed new control method effectively reduces the torque and flux ripple and improves the current waveform compared to traditional MPTC.

Conventional model predictive control for permanent magnet synchronous motor (PMSM) drives often introduces a weighting factor for lowering the switching frequency, which unfortunately leads to the tedious tuning works on weighting factors. Moreover, the computational burden is quite heavy since all the voltage candidates have to be enumerated. In this article, a cascaded band based model predictive current control is proposed, which can reduce the switching frequency without the use of weighting factor as well as alleviate the computational burden. In this method, the latest applied voltage vector will be adopted to predict the current error prior to the iterative prediction process. Next, a band is defined to compare with the precalculated current error to determine whether it is to be maintained. As a result, the iterative cost function minimization turns to be an event triggered process. In addition, the hard switch constraints for the candidate voltage vectors are developed to combine with the cascaded band, which can effectively reduce the iterations and avoid the suboptimum. The effectiveness of the proposed method is verified on a PMSM platform fed by three-level T-type inverter.

In order to solve the problem of 3rd, 5th and 7th harmonics in the inverter modulation process of Open-Winding Permanent Magnet Synchronous Motor with Common DC Bus (OW-PMSM-CDCB). In this paper, with the help of traditional Proportional-Integral (PI) and Proportional-Resonant (PR) control, an improved Proportional-Integral-Resonant (QPIR) harmonic suppression method which can be applied to different speed conditions is studied. The 3rd, 5th and 7th harmonics tracking are realized to solve the problem that PR control cannot change with the speed change. The stability proof and parameter selection of QPIR are given. Finally, an OW-PMSM-CDCB drive system experimental platform was built for verification. Simulation and experimental results show that the QPIR control strategy can effectively suppress the 3rd, 5th and 7th harmonics of the OW-PMSM system at different speeds.

Aiming at the traditional predictive current control of permanent magnet synchronous motor model, the enumeration method is used to select only one optimal voltage vector action in one control cycle, so that the current tracking control has the problem of over-regulation or under-regulation. A model predictive current control algorithm based on duty cycle control is proposed. This method calculates the effective voltage vector based on the minimization of the value function, and then calculates the duty cycle of the optimal voltage vector based on the idea of deadbeat, and maps each effective voltage vector and the corresponding operating time to the inverter. This is equivalent to optimizing the combination of the effective voltage vector and the zero voltage vector, ensuring that the optimal voltage vector is the global optimum. Compared with the traditional model predictive control, the steady-state current pulsation output by this method is smaller, and the torque pulsation is also smaller. Finally, the validity and reliability of the proposed method are verified through experiments.

In the open-winding permanent magnet synchronous motor (OW-PMSM) system fed by dual inverter with common dc bus, suitable control strategy should be adopted to suppress the torque ripple generated by the third harmonic flux and 0-axis current. However, the mathematical model will change when the motor is under open-phase fault so that the coordinate transformations and control strategy should be redesigned accordingly in order to suppress the torque ripple. In this article, the postfault OW-PMSM system is remodeled considering the third harmonic flux. Based on the postfault model, new coordinate transformations are designed to obtain a concise torque and decoupling voltage expressions for the simple and accurate control of torque. Moreover, the torque ripple is suppressed by modifying the current reference. In addition, a simple decoupling modulation is designed to directly obtain the switching signals for the dual inverter. Experimental results confirm the effectiveness of the proposed fault-tolerant control strategy.

To improve the parameter robustness of the open-winding permanent magnet synchronous motor (OW-PMSM), this paper proposes an improved model-free predictive current control (MFPCC) method. First, the first-order ultra-local model of OW-PMSM is established, which does not contain any motor parameter. Secondly, a sliding mode observer (SMO) is introduced into the model to estimate the current in d-q-0 axis. Then, to suppress the torque ripple generated by the zero-sequence current (ZSC), the central hexagonal modulation strategy and the q-axis current injection method are combined, and the compensation current that needs to be injected into the q-axis is calculated according to the estimated value of ZSC. Finally, after considering the one-step delay, the voltage reference at the next instant is calculated based on the estimated values of d-q axis current and compensation current. Simulation and experimental results indicate that the proposed method can improve the parameter robustness of OW-PMSM, and the performance of current and torque has been improved under various parameter mismatch conditions.

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.

In this paper, solutions to the main drawbacks of the traditional finite-set model predictive current control (FS-MPCC) of interior permanent magnet synchronous motors (IPMSM) are proposed. These drawbacks are: high computational load and high sensitivity to any model mismatch. The proposed computationally efficient FS-MPCC is based on finding the optimal voltage vector (VV) that would enhance the flow of the desired currents analytically. Based on its location in the stationary $\alpha- \beta$ plane, only three iterations of a modified cost function will be needed. Furthermore, a novel model-based online inductance estimation technique is proposed to enhance the robustness of the controller against model mismatch.

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.