A two-step model-free predictive control strategy based on extended SMO and swift voltage vector localisation for power converters

Finite control set model predictive control (FCS‐MPC) has been widely used in the control of grid‐connected converters with the advantages of fast dynamics, multi‐objective control, and easy implement. However, the conventional FCS‐MPC bears with variable switching frequency, high current ripple and computational burden. An improved current model predictive with the cost function‐based modulation scheme (CFM‐MPC) is proposed for a three‐phase three‐level VIENNA rectifier to improve the power quality. First, the mathematical model and voltage vector are given according to the principle of deadbeat control. Then, the voltage vector of different voltage vectors are selected according to the location of the voltage vector reference, and the switching action time of the selected are directly calculated by the inversely proportional with cost function value of the selected vectors. It remains the merits of both the conventional MPC and space vector pulse width modulation schemes to track the optimum voltage vector without increasing the computational burden. Finally, a comparative study with the proposed CFM‐MPC and conventional FCS‐MPC has been conducted to verify the superiority of the proposed scheme. The results show the proposed CFM‐MPC has the advantages of lower power ripple, fixed switching frequency, lower total harmonic distortion and neutral point potential balance.

In this paper, finite control set model predictive current control strategy for five-phase permanent magnet synchronous motor is analysed. At first, virtual voltage vectors are adopted to avoid including all the 32 voltage vectors of five-phase inverter into the control set. Besides, under different rotor speed, estimated voltage vector is predicted in advance so that a new online updated adaptive control set can be established with proper voltage amplitude and only three vectors. Consequently, current ripples and computational burden can be much reduced. At last, experimental results are presented and indicate the performance improvement of the proposed control strategy.

Recently, the finite control set model predictive control (FCS-MPC) strategy has been widely used in power electronics and motor drives. However, the implementation of the traditional FCS-MPC strategy needs to traverse all possible switch combinations, which greatly increases the computational burden of the system. In order to address this problem, this paper proposes a simplified FCS-MPC strategy based on historical data to enhance the performance of the system. In the proposed strategy, the historical data of the optimal vector in the last fundamental frequency period is stored and it is then utilized as the reference when selecting the optimal vector in the current fundamental frequency period. Also, the optimal vector is selected from the adjacent vectors and the zero vector. A simulation model of a three-phase LCL-type grid-connected inverter is established to verify the performance of the proposed control strategy.

This paper presents a variable switching point predictive current control (VSP2CC) method for induction machines (IMs) driven by a three-level neutral point clamped (NPC) inverter with a heuristic preselection of the optimal voltage vector. Enumeration-based model predictive control (MPC) methods are very simple, easy to understand and, in general, offer the possibility to control any nonlinear system with arbitrary user-defined terms in the cost function. However, the two most important drawbacks are the increased computational effort which is required and the high ripples on the controlled variables which limit the applicability of these methods. These high ripples result from the fact that in enumeration-based MPC algorithms the actuating variable can only be changed at the beginning of a sampling interval. However, by changing the applied voltage vector within the sampling interval, a voltage vector can be applied for a shorter time than one sample, which results in a reduced ripple. Since this strategy leads to an additional overhead which is crucial especially for multilevel inverters, it is combined with a heuristic preselection of the optimal voltage vector to reduce the calculation effort. Experimental results are provided to verify the proposed strategy. Furthermore, it will be shown experimentally that a conventional enumeration-based MPC method will lead to very low switching frequencies and high current ripples at low machine speeds; this significant drawback can be overcome with the proposed VSP2CC strategy.

In this article, two kinds of finite control set-model predictive voltage control (FCS-MPVC) methods have been proposed for linear induction machine to eliminate the cumbersome selection of the weighting factor (WF), achieve lower ripples of both thrust and flux, and reduce the heavy calculation steps. The first method, FCS-MPVC I, uses one voltage vector (VV) during the whole control period, and the second method, FCS-MPVC II, uses two VVs. Moreover, the proposed methods use a single cost function without any WF. This function consists of the errors between primary voltage references with their predicted values in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">αβ</i> -axis. Depending on the distance between the reference VV (RVV), as calculated using the deadbeat control method, and all candidate VVs (CVVs), the first proposed method, FCS-MPVC I, can select the nearest VV to the RVV as the optimal VV (OVV), which can reduce the redundant evaluation work greatly. Meanwhile, the second proposed method, FCS-MPVC II, applies two VVs, active VV and null VV, to adjust the duty ratio effectively. To avoid the iterated evaluation for all CVVs, one simplified search order excluding unsuitable CVVs has been proposed to determine the optimal OVV, which selects only one active VV from the two adjacent active VVs in the sector owning the RVV. Afterward, based on a prototype of 3-kW arc induction machine with a large radius, extensive simulation and experiments have fully demonstrated that the proposed methods can get much lower thrust ripples and less calculation steps effectively, compared with those of conventional finite control set-model predictive thrust control strategy.

Finite control set model predictive control (FCSMPC) has become popular for grid interactive applications due to its simplicity and ease of implementation. Since the implementation of FCSMPC requires all possible switching states of the inverter, the computational burden is an important issue, which needs to be addressed while applying FCSMPC to the control of multilevel inverters. This letter proposes a novel “S” factor scheme for the FCSMPC implementation, which reduces complexity while controlling grid current and balancing the capacitor voltages in neutral point clamped three-level inverters. The proposed scheme is very easy to implement and can be extended to any higher level inverter without any additional effort. In this method, at every sampling instant, redundant switching states are chosen suitably and the predicted reference inverter voltage (PRIV) vector is derived from predicted grid voltage, predicted reference current, and the present value of the actual current. Subsequently, “S” factor scheme is applied to select the switching state, which is close to the PRIV vector. The selected switching state is then applied at the same sampling instant. The proposed scheme is experimentally compared with a recent FCSMPC algorithm to validate the performance.

In this paper, direct torque control (DTC) of five-phase induction motor (FPIM) is implemented using three-level neutral point clamped (TL-NPC) inverter. One of the advantages of three-level inverter over two-level one for DTC operation is the low torque ripple. Also TL-NPC inverter through space vector modulation technique gives low $ dv/dt$ transition with better voltage waveform. By applying conventional lookup table for DTC, the TL-NPC inverter does not ensures lower $dv/dt$ transition. In this paper, a novel switching scheme for DTC of FPIM using TL-NPC inverter is proposed that ensures the low $ dv/dt$ transition and balancing of dc-link capacitor voltages of TL-NPC inverter. To form the lookup table for DTC operation, instead of using voltage vectors directly, virtual vectors (VVs) are utilized. Two switching states are used in one sample time to generate a VV in $\alpha \beta$ plane, which gives zero resultant voltage in $ xy$ plane. The switching strategy ensures low number of transitions to reduce the switching losses. The switching state redundancies are used in a novel way to balance the dc-link capacitor voltages without using any additional hardware. The proposed technique to balance the dc-link capacitor voltage gives lower switching frequency. The MATLAB/Simulink environment is used for the simulation and the results are validated through experiments.

This paper proposes an improved finite control set model predictive control (FCS-MPC) algorithm based on virtual space vector to reduce computational burden, and achieve a constant switching frequency for two-level three-phase inverters. The voltage vector reference is calculated in a 60° coordinate system according to the principle of deadbeat (DB) technique. A large number of virtual voltage vectors synthesized by discrete space vector modulation (DSVM) is utilized to approach the voltage vector reference. To avoid enumerating all the virtual voltage vectors from a look-up table, an algebraic way using the voltage vector reference is adopted to calculate the three candidate vectors. Then, a cost function is defined to select the optimal voltage vector among them. This new method greatly improves the computation efficiency since plenty of multiply operation is eliminated in the 60° coordinate system. Moreover, a fixed switching frequency is achieved since the durations of each switch states has been calculated. Experimental results under a two-level three-phase inverter verify the effectiveness of the proposed control strategy.

The heavy computational burden and the complexity in pulse generation based on multivector synthesis represent serious challenges for applying finite control set model-predictive control (FCS-MPC) in multiphase open-end winding (OW) drive systems. In this article, a low-complexity model-predictive control with dead-time compensation is proposed for the nine-phase OW permanent magnet synchronous machine drive system. First, to simultaneously eliminate voltage vectors on the third, fifth, and seventh harmonic planes, three vectors in the same direction are selected to synthesize the virtual vector (VV). In addition, a multivector synthesis algorithm is designed to generate a symmetrical pulse sequence. Then, in order to suppress the harmonic voltage vector caused by the dead time, an additional voltage vector was added to the VV. Finally, the deadbeat principle was introduced to calculate the reference voltage vector, and the optimal voltage vector is determined according to the position of the reference voltage vector, which reduces the computational burden successfully. The proposed method is studied and compared with traditional FCS-MPC schemes. Simulation and experimental results have verified the effectiveness of the proposed method, which reduces the computational burden and complexity and enables the use of FCS-MPC for multiphase OW drive systems.

In this paper a novel two-step prediction Finite Control Set Model Predictive Control (FCS-MPC) strategy with weighting factor look up table and divided control interval is presented. The method can be applied for a two-level inverter because of its low torque ripple. The weighting factor in cost function is selected via a look up table which is based on torque ripple minimization. Two-step prediction method is combined with dividing the control interval in two parts: active time for applying the active voltage vectors (AVV) and zero time for applying the zero voltage vector (ZVV). By using this technique the prediction horizon is doubled without serious increase of calculation burden. Simulation and experimental results prove the validity of the proposed method in a wide range of speed.

This paper studies the acoustic noise of induction motor with low switching frequency model predictive control and compares its noise performance with the traditional pulse width modulation with proportional-integral controllers. First, the relationship among the air-gap magnetic field, the electromagnetic force, and the acoustic noise is modeled to describe the influence of control algorithm on the noise characteristics of motors. As the counterpart with the traditional pulse width modulation control, a finite control set model predictive control strategy with low switching frequency is proposed. The steady-state performance and switching frequency constraint ability of the proposed model predictive control strategy are verified on the experimental platform. Second, the noise spectrums of these two control strategies are sampled and compared in different operating conditions, and the influence of the weighting factor on the noise spectrum is also discussed. It is proved by the experimental results that model predictive control has better noise performance than the traditional sinusoidal pulse width modulation under the same switching frequency and the total harmonic distortion of stator current.

A finite control set model predictive control strategy for the control of the stator currents of a synchronous reluctance motor driven by a three-level neutral point clamped inverter is presented in this paper. The presented algorithm minimizes the stator current distortions while operating the drive system at switching frequencies of a few hundred Hertz. Moreover, the power electronic converter is protected by overcurrents and/or overvoltages owing to a hard constraint imposed on the stator currents. To efficiently solve the underlying integer nonlinear optimization problem a sphere decoding algorithm serves as optimizer. To this end, a numerical calculation of the unconstrained solution of the optimization problem is proposed, along with modifications in the algorithm proposed in [1] so as to meet the above-mentioned control objectives. Simulation results show the effectiveness of the proposed control algorithm.

This paper proposes a torque control method for surface mounted permanent magnetic synchronous motor (PMSM) driven by a 2-level voltage source driven inverter, which has fast torque response and small torque ripple. The proposed torque control method follows the finite control set model predictive control (FCS-MPC) strategy. A reference state is derived at each time step for the given time varying torque reference and the cost index is defined so that the tracking error for this reference state should be penalized. The choice of an optimal output voltage vector is made first from the 6 possible active voltage vectors of the 2-level voltage source inverter. Then a modulation factor for the chosen optimal voltage vector is obtained so that the torque ripple can be reduced further. It is shown that the proposed FCS-MPC control method yields fast torque tracking response and small torque ripple through simulation and experiments.

The conventional finite control set-model predictive control (FCS-MPC) suffers from poor robustness against model mismatches. This paper presents an improved FCS-MPC with a reconstructed mathematical model to predict current variations without using a lookup table (LUT) or motor parameters. The model coefficients and current variations related to different voltage vectors can be updated during each control period. As a result, the prediction error is significantly reduced at low switching frequency when compared with the prior LUT-based model-free predictive current control (MFPCC). Additionally, the tracking accuracy of the proposed method at high speeds is improved due to the elimination of approximation error. Furthermore, a simple scheme is developed to suppress neutral-point-potential drift without knowledge of the dc-bus capacitor. Simulation and experimental tests, along with comparisons with prior arts carried out on a three-level inverter-fed surface-mounted permanent magnet synchronous motor drive, confirm the superiority of the proposed method.

Traditional finite control set model predictive control (FCS-MPC) selects an optimal voltage vector in one control cycle. However, considering multiple control cycles, it cannot be proved that the voltage vector is optimal, and the uncertainty of PMSM parameters seriously affects the prediction accuracy. In order to balance the relationship between switching frequency, steady-state performance, and robustness, a multi-step robust FCS-MPC is proposed. Firstly, an incremental prediction model is established to eliminate the flux linkage of permanent magnets. The inductance parameters in the incremental model are identified online based on the observer and inductance extraction algorithm. Then, based on the principle of no-beat, the candidate voltage vector in two control periods is simplified to get the candidate voltage vector, and the cost function is used to determine the optimal voltage vector again. Finally, the proposed FCS-MPC method is compared with the traditional FCS-MPC method. The experimental results show the effectiveness of FCS-MPC strategy.