Model-free Predictive Current Control Research Articles

Model-Free Predictive Current Control of SPMSM Drives Using Extended State Observer

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-Free Predictive Power Control for PWM Rectifiers under Asymmetrical Grids

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.

Robust Predictive Stator Current Control Based on Prediction Error Compensation for a Doubly Fed Induction Generator Under Nonideal Grids

Traditional model-based predictive current control (MBPCC) for a doubly fed induction generator (DFIG) is easily affected by machine parameter variations. Recently, a model-free predictive current control (MFPCC) was proposed to enhance the parameter robustness of DFIG systems by using the past current difference to predict the future current difference under the same switching state. However, this approach has the problems of high sampling frequency, variable switching frequency, and high steady ripples. To solve these problems, this article proposes a robust predictive stator current control (RPCC) method for DFIGs. Different from the prior MFPCC, the current error between the measured value and the predicted value is not only used in the stage of the stator current prediction, but also in the calculation of rotor voltage vector. Hence, the performance deterioration caused by machine parameter variations is significantly alleviated. Furthermore, a support vector machine can be combined with the proposed RPCC. In this way, the proposed method ensures strong parameter robustness and small steady-state power ripples. Finally, by recalculating the current reference value, the method can also achieve a good performance even under nonideal grids. The proposed RPCC is compared with MBPCC and MFPCC and its effectiveness is confirmed by the presented simulation and experimental results.

Model-Free Predictive Current Control of a PWM Rectifier Based on Space Vector Modulation Under Unbalanced and Distorted Grid Conditions

Among various control strategies for pulsewidth-modulated (PWM) rectifiers, model-based predictive current control (MBPCC) based on space vector modulation (SVM) is a simple and effective method due to its quick dynamic response and good steady-state performance. However, SVM-based MBPCC (SVM-MBPCC) suffers from parameter variations and grid disturbances, which are common in practical applications due to temperature effects, saturation, grid faults, and so on. Recently, model-free predictive current control (MFPCC) based on current differences has been proposed to address the problem of parameter robustness. However, this approach suffers from stagnant updating of the current differences, and the steady-state ripples are still high due to the application of a single vector in one control period. This article proposes an SVM-MFPCC, which combines the merits of both SVM-MBPCC and MFPCC. The proposed method uses an ultralocal model rather than a conventional accurate model of the PWM rectifier, which is updated online based on the voltages and currents in past control periods. By using this ultralocal model, a voltage reference to nullify the current error can be calculated based on the principle of deadbeat control and is subsequently synthesized via SVM. Furthermore, by adding an appropriate compensation power to the original power references, the proposed method can achieve sinusoidal and balanced grid currents even under unbalanced and distorted grid conditions. The proposed method is compared to conventional SVM-MBPCC under various grid conditions, and the results of simulations and experiments confirm its effectiveness.

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

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.

A Modified Model-Free Predictive Current Control Method Based on an Extended Finite Control Set for DFIGs Applied to a Nonideal Grid

Conventional model-free predictive current control (MFPCC) for doubly fed induction generator (DFIG) systems presents high steady-power ripples and current harmonics, because it uses only eight basic voltage vectors of a two-level converter. Furthermore, the stator and rotor currents of DFIGs are severely distorted under a nonideal grid if no special measures are taken. In this article, a modified MFPCC (MMFPCC) is proposed to address the problems above. On the one hand, an extended finite control set, including 20- rather than 8-voltage vectors as in the conventional finite control set, is used to improve the steady-state performance. On the other hand, the stator current reference is calculated so that sinusoidal and balanced stator currents are obtained even under a nonideal grid. The proposed MMFPCC is compared to conventional MPC and MFPCC, and the experimental results confirm its effectiveness.

Finite Control Set Model-Free Predictive Current Control of a Permanent Magnet Synchronous Motor

In this paper, a finite control set model-free predictive current control (FCS-MFPCC) of a permanent magnet synchronous motor is presented. The control scheme addresses the problems of large current fluctuation and decline of the motor system performance during parameter perturbation for the traditional finite control set model predictive current control (FCS-MPCC). Firstly, the mathematical model of the motor is analyzed and derived during parameter perturbation, and a new hyperlocal model of the motor is established based on this mathematical model. Secondly, a finite control set model-free predictive current controller is designed based on the new hyperlocal model, and a current error correction factor is introduced to correct the prediction error. Meanwhile, the stability of the observer is demonstrated via the Lyapunov theory. The simulation results show that the proposed control strategy reduces current fluctuation compared with the FCS-MPCC strategy, and the system is robust during parameter perturbation.

Modulated Dual-Voltage-Vector Model-Free Predictive Current Controller for Synchronous Reluctance Motor Drives With Online Duty Cycle Calculation

This article proposes a modulated dual-voltage-vector model-free predictive current control with online duty cycle calculation, which is used to drive synchronous reluctance motors. The dual-voltage-vector modulation scheme reduces the current ripples and current errors present in the single-voltage-vector. The proposed method reduces the predictive current controller’s calculation time by first setting the initial value of the duty cycle to a constant. Then, an optimal switching mode can be selected by minimizing a cost function. Next, the required duty cycle can be calculated directly by the proposed method without any differential calculations instead of its initial value. The proposed method can effectively track the current, reducing the maximum average current error by 34.8% compared to its conventional single-voltage-vector one. Finally, the correctness and feasibility of the modulating model-free predictive current controller with the online duty cycle calculation proposed in this article are verified by the experimental results using Texas Instruments micro-controller TMS320F28379D.

An Improved Model Free Predictive Current Control for PMSM With Current Prediction Error Variations

The conventional model predictive current control is a model-based control method, and the accuracy of the predicted currents is affected by motor parameters such as flux linkage, inductance, and resistance. To get rid of model parameters dependencies, a model-free predictive current control (MFCC) was proposed before, which can improve the parameter robustness without utilizing any knowledge of the initial motor parameters. However, the stagnant current update detection is one of the main problems that limit the current predictive performance. To solve this problem, a current prediction error model according to the contiguous instant current error variations is proposed to reconstruct the surface-permanent magnet synchronous motor (SPMSM) model in this paper. Afterwards, a novel MFCC method with the online parameter identification is developed. This method takes advantage of mathematical relationships in the current prediction error model, and the motor parameters can be updated within each period to improve prediction accuracy. Simulation and experimental results verify that this proposed MFCC method can significantly reduce the stagnation effect and improve MFCC performance under different parameter disturbances.

Hybrid Switching of Four-Voltage-Vector Model-Free Predictive Current Control for Four-Switch Three-Phase Inverter-Fed SynRM Drive Systems

Conventional model-based predictive current control (MBPCC) suffers from common drawbacks of high reliance on system model parameters and the use of a single input voltage vector that result in large pulsating current ripples and prediction errors. In the case of a four-switch three-phase inverter (FSTPI) topology, the implementation of the predictive controller is exacerbated due to limited numbers of candidate voltage vectors. This paper presents an integrated model-free predictive current control with a hybrid switching mechanism to solve the problem. The proposed method introduces the combined switching mechanism of input voltage vectors with fixed and variable modulations by increasing the number of switching voltage vectors. The four basic voltage vectors generated in the four-switch three-phase inverter create twenty-four new synthesized voltage vectors through fourfold linear expansions of the space vector plane. The switching durations of input voltage vectors are determined by calculating their optimal duty ratios. As a result, the proposed method improved the prediction accuracy by increasing the iteration calculations of current differences every sampling period. The proposed method, known as the hybrid switching of four-voltage-vector model-free predictive current control (HS-4VV-MFPCC), is practically tested <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">via</i> simulation and experimental works to evaluate its effectiveness and performance improvement.

Model-Free Predictive Current Control of Synchronous Reluctance Motor Drives for Pump Applications

Climate changes and the lack of running water across vast territories require the massive use of pumping systems, often powered by solar energy sources. In this context, simple drives with high-efficiency motors can be expected to take hold. It is important to emphasise that simplicity does not necessarily lie in the control algorithm itself, but in the absence of complex manual calibration. These characteristics are met by synchronous reluctance motors provided that the calibration of the current loops is made autonomous. The goal of the present research was the development of a current control algorithm for reluctance synchronous motors that does not require an explicit model of the motor, and that self-calibrates in the first moments of operation without the supervision of a human expert. The results, both simulated and experimental, confirm this ability. The proposed algorithm adapts itself to different motor types, without the need for any initial calibration. The proposed technique is fully within the paradigm of smarter electrical drives, which, similarly to today’s smartphones, offer advanced performance by making any technological complexity transparent to the user.

Model-Free Predictive Current Control for Three-Level Inverter-Fed IPMSM With an Improved Current Difference Updating Technique

Parameter mismatches in conventional model predictive current control (MPCC) would worsen torque and THD performance. To eliminate the parameter dependence of MPCC, this letter proposes a model-free predictive current control (MFPCC) for a three-level inverter-fed interior permanent magnet synchronous motor (IPMSM). Firstly, to increase the current difference updating frequency in the diode neutral-point-clamped (NPC) three-level inverter, a synchronous updating method is proposed in a manner where the number of updated current differences during one sampling period is increased up to 6. Secondly, for the sake of computational burden alleviation, a candidate vector selection algorithm is introduced in such a way that the number of candidate vectors is reduced to only 7–9. Then, the problem of neutral-point voltage fluctuation which is inherent to the three-level NPC inverter is investigated, and a reasonable replacement method incorporating the redundancy characteristic of small vectors is developed. Finally, experimental results verify that the system robustness against mismatched parameters can be greatly enhanced by use of the proposed control scheme; meanwhile, steady-state performance and dynamic-state response of the system can be improved to some extent as well.

Model-free predictive current control for IPMSMs with multiple current difference updating technique

This paper proposes a model-free predictive current control (MFPCC) for interior permanent magnet synchronous motors (IPMSMs) with a multiple current difference updating (MCDU) technique. Firstly, to improve robustness against machine parameter variations and reduce the computational burden, current difference-based prediction that only requires accumulation operation is implemented, which avoids the model-based complex calculations in traditional model predictive current control (MPCC). Secondly, a MCDU technique is conducted with the characteristic of updating the current differences associated with voltage vectors in opposite directions. Thus, the updating frequency of the current difference can be significantly increased. Finally, the effectiveness of the proposed method is verified by simulation and experimental results.

A Synchronized Current Difference Updating Technique for Model-Free Predictive Current Control of PMSM Drives

Model-free predictive current control (MFPCC) is a promising substitute for model predictive current control (MPCC). However, the performance of the MFPCC, to a large extent, hinges on the update frequency of its lookup table. Conventionally, the update is only performed when two successive switching states applied by the controller are identical, causing a stagnation problem to the current difference of those switching states that are not applied. To address the stagnation problem, this paper proposes a novel mechanism called synchronized current difference update for model-free predictive current control (SCDU-MFPCC). The presented scheme uses the model of permanent-magnet synchronous motor (PMSM) to construct equivalent differential stator currents corresponding to seven basic voltage vectors. To that end, current slope will be defined from the current difference of two successively applied voltage vectors. An updating factor associated with the current slope is then introduced into the prediction scheme to correct the enforced response of all switching states. This scheme is applied on every current measurement to update the stored information regardless of the successive switching states applied are distinct or not. Finally, experiments are conducted to assess the performance of the new approach using a TMS320F28379D microcontroller. Experimental results demonstrate that the proposed method substantially reduces the stagnation effect under steady-state and dynamic operations.

Triple-Voltage-Vector Model-Free Predictive Current Control for Four-Switch Three-Phase Inverter-Fed SPMSM Based on Discrete-Space-Vector Modulation

The four-switch three-phase (FSTP) inverters are known for their cost-effective advantages and minimal switching losses. However, such inverter topology’s progress is lagging due to control constraints and requirements, including voltage vector limitations and parameter perturbations. To overcome the issue, this paper proposes a triple-voltage-vector model-free predictive current control (TVV-MFPCC) for FSTP inverter-fed surface permanent magnet synchronous motor (SPMSM) drives. The proposed TVV-MFPCC uses the principle of discrete-space-vector modulation (DSVM) to increase the voltage vector selections. Three primary voltage vectors, either the same or distinct, are linearly combined to yield the synthesized voltage vectors. A redundant voltage vector reduction scheme is also introduced to lessen calculations by optimally reducing the candidate voltage vectors to sixteen equivalent hybrid switching modes. To improve prediction accuracy, the TVV-MFPCC performs three different current readings and three current difference calculations in each sampling period. Experiments using a TMS320F28379D microcontroller are conducted to compare the performance of the proposed TVV-MFPCC against conventional MFPCC (C-MFPCC) and validate the scheme.

A Modulated Model-Free Predictive Current Control for Four-Switch Three-Phase Inverter-Fed SynRM Drive Systems

This paper presents a modulated model-free predictive current control for four-switch three-phase inverter-fed synchronous reluctance motor drive systems to improve performance against existing methods. The study focuses on six switching modes modulated with variable duty ratios that are optimized and computed in real-time. The stator currents corresponding to the six modulated switching modes are predicted without relying on the motor’s mathematical model nor parameters thanks to its model-free nature. To enhance the performance of the controller, adaptive current detection technique and current difference modification are employed, yielding good adaptation to the duty ratio modulations. Implementation of the proposed method is realized via a TMS320F28379D microcontroller on a testbed to assess its effectiveness. Finally, comparisons between the proposed scheme and a non-adaptive predecessor under various experimental settings are made to demonstrate its salient performance.

Model-Free Predictive Current Control for SynRM Drives Based on Optimized Modulation of Triple-Voltage-Vector

A model-free predictive current control for synchronous reluctance motor drives based on triple-voltage-vector with optimized duty ratio modulation is presented in this paper. The proposed scheme introduces an effective and low complexity strategy of selecting candidate voltage vectors to largely reduce computational loadings. First, the basic voltage vectors are organized to apportion six regions, representing six composite switching modes. Each region comprises two active voltage vectors and a zero voltage vector modulated by distinct duty ratios. The scheme starts from the selection of an initial basic voltage vector corresponding to a predefined cost function. The second candidate voltage vector then follows, which is chosen from the region adjacent to the initial one. The third vector is a default zero voltage vector. As a result, voltage vectors outside the selected regions are excluded from the process. Moreover, the adjustable feature makes the optimization of duty ratios adaptive. Finally, the proposed method is experimentally put to tests under various operating conditions utilizing the synchronous reluctance motor drives. Experimental results are presented to demonstrate the effectiveness of the proposal.

A Two-Staged Optimization Approach to Modulated Model-Free Predictive Current Control for RL-Connected Three-Phase Two-Level Four-Leg Inverters

This paper proposes a modulated model-free predictive current controller for three-phase two-level four-leg RL -connected inverters based on a two-staged optimization. The proposed scheme is model-free and does not require any knowledge of system parameters nor the load model to perform current predictions. To improve current prediction accuracy, two input voltage vectors are linearly modulated in every sampling period, doubling the current difference calculation and update frequency. Moreover, the input voltage vectors are independently chosen and optimized in each stage using the integrated approach of cost function minimization and duty ratio optimization, known as the two-staged process. The obtained vectors are adaptive and modulated with variable durations. The proposed method is implemented in a drive circuit using TMS320F28379D and put to tests under various loading conditions of steady-state, dynamic, and parameter mismatch. Results from simulations and experiments demonstrate the feasibility of the proposed method.

An Improved Model-Free Predictive Current Control With Advanced Current Gradient Updating Mechanism

Model-based predictive control that highly depends on system parameters is 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. Current gradient knowledge therefore provides a significant foundation for MFPCC. In conventional MFPCC, however, the stagnation existing in the current gradient update always impacts the reliability of current gradients and further worsens the control performance. In this article, an improved MFPCC with an advanced current gradient updating mechanism is proposed. In the proposed strategy, to guarantee the reliability of the current gradients, two contiguous measured current gradients due to the applied voltage vectors are used to estimate the current gradients for all the possible voltage vectors. This simple method takes advantage of the mathematical relationships between the voltage vectors. In this way, all the current gradients can be obtained within one control period, effectively reducing the stagnation effect in conventional MFPCC. The proposed MFPCC scheme is evaluated on a permanent magnet synchronous machine drive setup to demonstrate its effectiveness.

Model-Free Predictive Current Control of DFIG Based on an Extended State Observer Under Unbalanced and Distorted Grid

The traditional method of controlling a doubly fed induction generator based on a mathematical model has poor control performance when the motor parameters are inaccurate. To solve this problem, this article proposes a new model-free predictive current control (MFPCC) scheme. In the proposed method, an ultralocal model is used to replace the mathematical model of the motor, and an extended state observer (ESO) is used to estimate the value of the disturbance to improve the control performance. Since only the measured stator voltage and current values are required in the final control expression, the control system achieves good parameter robustness. In addition to superior control performance when the parameters are inaccurate, the proposed method also has good steady state and dynamic performance when the parameters are accurate. The proposed MFPCC scheme based on an ESO is extended to an unbalanced and distorted grid by modifying the stator current reference. The presented experimental results confirm the effectiveness of the proposed method.