An Improved Model-Free Predictive Current Control Scheme for Open-Winding PMSM with Common DC Bus

Model predictive control has been extensively researched for many years, which has the advantages of clear principles and fast dynamic response. However, it also has the disadvantage of high sensitivity of system parameters, which requires accurate system models and system parameters to exert its performance. Aiming at the problem that model predictive control is sensitive to the parameters of dual three-phase permanent magnet synchronous motor (DTP-PMSM), a model-free predictive current control method for DTP-PMSM based on the ultra-local model is proposed. The strategy establishes the current ultra-local model of the DTP-PMSM through the current and voltage information of the system. Then based on the current ultra-local model, a model-free stator current controller which does not depend on the DTP-PMSM parameter model is designed. Finally, the comparative analysis of the simulation experiment proves the validity and correctness of the proposed method.KeywordsModel-free predictive controlDual three-phase permanent magnet synchronous motor (DTP-PMSM)Ultra-local model

Conventional predictive control (PC) has been employed for machine drives due to good dynamic response and easy implementation. However, the disturbance caused by machine parameter mismatch is one of main barriers to its widespread application. In order to deal with this issue, this work proposes a novel model-free predictive current control (MFPCC) for surfaced permanent magnet synchronous machines (SPMSM). The contribution of this work is that a novel extended state observer (ESO) is proposed and the proposed MFPCC only utilizes the input and output knowledge of the plant without using any machine parameter in controller, which can effectively suppress the disturbances. In addition, the effect of the initial machine inductance parameter mismatch on the conventional ESO of MFPCC is analyzed in detail. The proposed MFPCC is validated and compared against three methods, namely PC without ESO, PC using ESO, and MFPCC using ESO. The experimental results have been carried out to verify the effectiveness of the proposed MFPCC.

Model-based predictive control that highly depends on system parameters has been widely investigated. In contrast, model -free predictive current control (MFPCC) can be performed based on input or output measured data, rather than on any system model information, hence eliminating the influence of parameter uncertainties. In such a strategy, the current gradients due to each of the possible voltage vectors are stored and used to predict future currents. In this paper, a model free predictive control method based on discrete space vector modulation is used, which increases the effective vector control set by constructing a virtual vector which is composed of linear combination of real vectors. The THD of grid connected current is reduced. Simulation and experimental results confirmed 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 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.

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.

Conventional model predictive power control (MPPC) features a simple concept and quick dynamic response. However, it relies heavily on the system model and its parameter accuracy. Furthermore, the steady ripples are still high due to the use of one voltage vector during one control period. Recently, model-free predictive current control (MFPCC) has been proposed in the current control of PWM rectifiers. Despite the strong parameter robustness, the principle of MFPCC cannot be directly applied to power control, because the relationship between power and converter voltage is more complex. This paper first proposes a basic model-free predictive power control (MFPPC), which successfully extends the principle of MFPCC to power control. Subsequently, an improved MFPPC is proposed, which uses an extended finite control set of voltage vectors to improve the steady-state performance. Furthermore, by using the online updated ultralocal model of PWM rectifiers, the problem of stagnant power updating in basic MFPPC is solved. The ideal three-phase grid voltages are symmetrical and sinusoidal, but the actual grids are usually unsymmetrical. In this paper, the proposed method is extended to asymmetrical power grids by adding an appropriate compensated power to the original power references. The proposed basic MFPPC and improved MFPPC are compared to conventional MPPC. The presented experimental results confirm the effectiveness of the proposed methods.

Traditional model predictive current control (MPCC) is heavily dependent on the accuracy of motor parameters and incurs high computational costs. To address these challenges, this paper proposes an enhanced model-free predictive current control (MFPCC) strategy based on ultra-local models (ULMs). Initially, a Kalman filter (KF) is used to estimate the current gain, while an adaptive sliding mode observer (SMO) is employed to estimate current disturbances. Subsequently, an equivalent transformation of the cost function is carried out in the αβ domain, and the voltage vector combinations are reduced to a single one via sector distribution. Hence, the proposed MFPCC is independent of motor parameters and capable of reducing computational complexity. Simulation and experimental results demonstrate that the proposed MFPCC method significantly improves computational efficiency and the robustness of current prediction, enabling precise current tracking even in the presence of motor parameter mismatches.

To address the complexity of vector selection in the conventional predictive current control strategy and the reduction of control system robustness when the motor parameters change, this paper proposes a finite-set model-free three-vector predictive current control method for permanent magnet synchronous motor based on the improved sliding-mode perturbation observer. According to the mathematical model of a permanent magnet synchronous motor under perturbation, a new hyperlocal model is established, and the conventional vector traversal optimization method is optimized to obtain the necessary basic voltage vector by analyzing the function with one judgment. Meanwhile, the sliding mode perturbation observer is designed using the fast exponential convergence law to observe the unknown part. Finally, by experimentally contrasting the proposed technique with the conventional three-vector model predictive current control, the method suggested in this paper can successfully reduce the current pulsation and inhibit the system perturbation caused by the parameter change, ensuring the steady state performance of the motor, as demonstrated by the experimental comparison with the conventional three-vector model predictive current control method.

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

This paper proposes an improved model-free predictive current control (MFPCC) scheme for four-switch three-phase (FSTP) inverter-fed synchronous reluctance motor (SynRM) drive systems. Unlike the existing model-based predictive current control (MBPCC), the improved MFPCC does not need any motor parameters due to the current sensor based approach, where only once current measurement is required in each sampling interval. According to the measured stator current and pre-saved current differences, the future stator current can be predicted without estimating the back-EMF of motor. Next, a cost function is defined to compute all possible future current errors under four switching states generated by the FSTP inverter. The next switching state can be decided to control the FSTP in the next sampling interval to obtain the cost function. Additionally, a passivity-based adaptive backstepping complementary sliding-mode (PBABCSM) controller is adopted in this study to improve the position tracking performance of the FSTP inverter-fed SynRM drives. The stability of the closed-loop system has been verified through passivity theory and Barbalat's lemma. Furthermore, simulation results are provided to validate the effectiveness of the proposed method.

This paper proposes a novel pseudo 3-D space vector modulation (SVM)-based sequential model predictive control (SMPC) for the open-winding permanent magnet synchronous motor (OW-PMSM), which eliminates the weighting factor and improves the current quality. In the OW-PMSM drive with a common dc bus, because of the large difference between the 0-axis and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i>-axes impedance, the weighting factor is required in traditional model predictive control (MPC) to balance the 0-axis and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i>-axes current control. To avoid the weighting factor and its tedious tuning work, an improved SMPC is put forward, where two sequential predictive stages are established to evaluate voltage vectors (VVs). In the first stage, several possible zero-sequence voltage components of all candidate VVs are evaluated to obtain the VV group with the best 0-axis current tracking performance. In the second stage, according to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i>-axes current control, a multivector-based modulation scheme is designed to synthesize the optimal VV. Due to the characteristics of the modulation process in two predictive stages, the proposed modulation process is named pseudo 3-D SVM, which achieves better current quality and output performance. Experimental verification is provided to prove the effectiveness of the proposed scheme.

To enhance motor control robustness against parameter mismatch, this study develops an inductance identification-based model-free predictive current control (MFPCC) strategy combining ultralocal modeling with extended state observer techniques. The proposed approach utilizes only system input-output data through ultralocal model-based MFPCC, eliminating dependence on motor parameters while significantly improving system robustness. The method of introducing the ESO (extended state observer) reduces the number of controller parameters needed very well. Nevertheless, this approach’s dependence on motor inductance parameters compromises control performance during parameter mismatch conditions, ultimately diminishing system robustness. Through the method of inductance parameter identification, while ensuring the advantages of the former, it can track the changes of the inductance parameters in real time, greatly improving the robustness of the system. The proposed methodology is validated through comprehensive MATLAB/Simulink simulations.

With the high-speed development of digital signal processors, model predictive current control (MPCC) has been widely used in power converters. However, the control robustness of MPCC is poor because of its strong dependence on the model parameters. In this paper, an ultra-local model-free predictive current control (MFPCC) for three-level grid-connected inverters (GCI) with LCL filters is proposed. Based on ultra-local theory, a third-order ultra-local model of the GCI with LCL filters is constructed. Then, a Kalman filter (KF) is introduced to estimate the three perturbations. Moreover, the perturbations are compensated to the predictive current model, thus reducing the number of model parameters involved in the predictive control. Finally, simulation results show that the proposed MFPCC improves the tracking performance of the grid current and strengthens the dynamic response capabilities under the parameter mismatch. Furthermore, the output power quality becomes higher, and the system robustness is also enhanced under noisy environment.

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.