Model-Free Predictive Power Control for PWM Rectifiers under Asymmetrical Grids

This paper presents a three‐vector model predictive power control with neutral‐point voltage balance (TVMPPC‐NPVB) and a dead‐beat control for three‐phase three‐level T‐type rectifier. Three vectors are adopted to reduce the current ripple, balance the neutral‐point voltage and achieve unity power factor on the AC side simultaneously, while the conventional model predictive power control suffers from large current ripple, difficulty in designing the weighting factor and computational burden. In the proposed strategy, two cost functions are derived to select the optimal space vector sequence and reduce the computational burden. The action time of each vector is introduced in detail. The traditional PWM rectifier utilizes PI controller to regulate the output voltage. But PI controller tends to have a long transition time and big variation on output voltage when the load changes suddenly. The proposed dead‐beat control is capable of solving these problems caused by conventional PI controller. Both simulation and experimental results are shown to confirm the effectiveness of the proposed method.

Nowadays, a model predictive control has gained a lot of attractions for power converter. To overcome the defects of the conventional model predictive control and achieve better performance, this article presents a multivector model predictive power control (MV-MPPC) with low computational burden for the grid-tied quasi-Z-source inverter (qZSI). Compared with the conventional model predictive power control (MPPC) that adopts only one vector in one control period, the proposed MV-MPPC applies several vectors including two active vectors, one null vector, and one shoot-through (ST) vector to fulfill the expected performance for the grid-tied qZSI. By calculating the optimal duty ratios of the ST vector, the inductor current ripple can be greatly reduced. To eliminate the weighting factor, a modified sliding-mode control method for simultaneously controlling the capacitor voltage and the inductor current to their setting points is proposed. Furthermore, a quick and effective optimal sector selection method is given, which chooses the optimal sector through a look-up table instead of a bunch of complex calculations. Hence, the computational burden of the proposed MV-MPPC is decreased with significance. Both simulation and experimental results are shown to validate the effectiveness and the advantages of the proposed method.

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.

Although the robustness is enhanced essentially by the model-free predictive control, the accuracy of its data-driven model is directly affected by the unnecessary current harmonics, such as the harmonics caused by the control strategy itself. To reduce the influences of these harmonics, a continuous-control-set model-free predictive fundamental current control (MF-PFCC) strategy is proposed in this article for the permanent magnet synchronous motor driving system. Harmonics due to the controller including side frequency effect and frequency coupling effect are analyzed in principle, and a frequency-converting double second-order generalized integrator structure is designed to exclude the fundamental terms from the sampled current to cut the coupling path of the effects and to improve the accuracy of the data-driven model in the MF-PFCC without any time-variable physical parameters. The analyses of the effects and the effectiveness of the proposed method are verified by experimental and supplementary simulation results, as well as advantages including the reduced influence of effects and enhanced robustness with improved stator current quality.

To lessen parameter dependence and suppress current harmonics, a continuous-control-set model-free predictive current control (MFPCC) strategy with voltage vector correction and duty cycle optimization is proposed for five-phase permanent magnet synchronous machine (PMSM) drives. Firstly, the MFPCC method with an ultralocal model is developed based on the mathematical model of a five-phase PMSM and the virtual voltage vector (VVV). The lumped parameters of the ultralocal model are estimated with current and voltage variations rather than observers. Thus, the stability of the five-phase PMSM drive is not affected. Secondly, the reason for the current distortion is investigated by analyzing the numerator and the denominator of the division operation in uncertain terms identification. A voltage vector correction mechanism is accordingly designed based on the power factor angle to decrease current distortions. Meanwhile, the duty cycle of the selected VVV is optimized to further reduce the tracking error. The proposed strategy not only theoretically eliminates the impact of machine parameters but also improves steady-state performance. Finally, the effectiveness of the MFPCC is validated by experiments.

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.

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.

A novel power and rate control system model for wireless communication networks is presented, which includes uncertainties, input constraints, and time-varying delays in both state and control input. A robust delay-dependent model predictive power and rate control method is proposed, and the state feedback control law is obtained by solving an optimization problem that is derived by using linear matrix inequality (LMI) techniques. Simulation results are given to illustrate the effectiveness of the proposed method.

A power compensated triple-vector model predictive direct power control (PCTV-MPDPC) strategy is proposed in this paper for non-redundant fault-tolerant three-phase rectifiers. The system fault tolerance ability and post-fault control performance are enhanced with low hardware complexity. By defining the negative conjugate of complex power as the control variable, a straight-forward pattern of power-voltage relationship analysis is obtained. Besides, a novel voltage vector selection method is put forward based on the location of the error vector between the reference and real control variables. The sector identification process can be omitted to derive a unified expression for duty ratio calculation. On top of that, an intuitive power compensation architecture is proposed to eliminate the dc-bus voltage deviation so that its utilization rate can be maximized. With the proposed PCTV-MPDPC strategy, the power ripples and input current harmonics can be significantly reduced. Superior steady-state and dynamic performance can be achieved compared with the conventional finite-control-set MPDPC (FCS-MPDPC) scheme and a recently proposed multiple-vector model predictive power control (MV-MPPC) method, along with excellent dc-bus voltage deviation suppression effect. Furthermore, simulation validation in Matlab/Simulink and experimental verification on a four-switch three-phase rectifier are carried out to demonstrate the effectiveness of the proposed PCTV-MPDPC strategy.

The parameter mismatch of conventional model predictive control (MPC) may affect the grid current performance of LCL-filtered voltage source inverter (VSI). To improve the robustness of grid current against parameter mismatch, a model-free predictive control (MFPC) with parameter-free references (PFRs) is presented in this article. First, a model-free prediction method is presented, where the current–voltage differences corresponding to different voltage vectors are updated, stored, and adopted for current–voltage predictions, eliminating the parametric effect on conventional prediction method. In addition, a PFRs method is presented, where the cosine functions between the sampled current–voltage signals of LCL-filtered VSI are established to obtain current–voltage references, which eliminates the parametric effect on conventional reference method. Finally, the performance of the proposed method is evaluated on the LCL-filtered VSI under accurate and mismatched parameters. Compared with conventional MPC and MFPC methods, the proposed MFPC-PFRs achieve comparable current performance and a greater robustness against parameter mismatch.

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

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.

In order to alleviate the congestion between the side road and the freeway, a novel balancing control scheme is proposed based on model free adaptive predictive balancing control (MFAPBC). The proposed MFAPBC is a typical data driven control method, which merely utilize the measured input and output (I/O) data of side road and freeway to update the control input signal. Model free adaptive predictive control (MFAPC) has the advantages of model predictive control (MPC) and model free adaptive control (MFAC); that is, only the I/O data of the controlled plant are used to predict the system output sequence within the prediction horizon and to calculate the control input sequence within the control input horizon, without the controlled plant model involved. The effectiveness of the proposed MFAPBC method is test in simulation for side road and freeway system.

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.