Multistep Model Predictive Control of Induction Motors for Reducing Switching Frequency

In this work a study of multistep model predictive direct current control in high switching frequency range will be presented. The optimization problem underlying this control approach is represented by an integer least-square problem, which can be solved by the sphere decoding algorithm. In this paper, the system performance, both in transient and steady-state will be analysed in such a way, that a direct current controller can be obtained for an induction motor operated by a two-level voltage source inverter in high dynamic drive applications, which achieves higher dynamics when compared to the classic modulation based schemes and lower current distortion than other direct control approaches.

In medium-voltage (MV) drives, the switching frequency is limited to a few hundred Hz, for which high-performance control and modulation schemes are necessary to maintain acceptable current and torque distortion. Forced machine current control (FMCC) is a predictive control strategy for MV drives which was proposed in the early 1980s, which can be formulated for either torque or current control. Recently, model predictive direct torque control (MPDTC) and model predictive direct current control (MPDCC) have been developed, sharing with FMCC the use of hysteresis bounds, switching and prediction horizons. However, the relative performances of these schemes are yet to be compared. Through simulation, this paper compares the schemes across a range of operating points. It is shown that the steady-state performance of MPDxC and FMCC is similar when the switching horizon of MPDxC is limited. However, when the switching horizon is extended, the performance of MPDxC is shown to be superior to FMCC, the horizon of which is inherently restricted.

This paper proposes a direct model predictive current control (MPC) strategy for matrix converters (MCs). The proposed method aims to regulate the output current of the MC while minimizing the input reactive power at the same time. In addition, in order to reduce the switching losses, the MPC adjusts the average switching frequency of the converter. The MPC scheme uses the discrete-time model of the MC to predict the future trajectories of the controlled variables of concern. Then, a cost function is formulated to include all the control objectives, i.e. the output current, input reactive power, and switching frequency. Subject to the system dynamics, the cost function is minimized in order to find the optimal control action that can be applied to the converter at the next time-step. In order to examine the proposed control strategy with the MC, simulations based on MATLAB/Simulink are conducted. The simulation results show the effectiveness of the proposed scheme in steady-state and transient operations. The results show that the proposed MPC strategy offers a very good steady-state behavior as well as very fast dynamic responses during transients. Moreover, the switching frequency can be highly reduced which in turn results in low switching losses.

In this paper, a new model predictive direct current control (MPDCC) strategy is proposed for a medium-voltage (MV) neutral-point-clamped converter with an LCL-filter-based connection to the grid. The proposed strategy addresses the issues of resonance damping and harmonic attenuation through the virtual resistor (VR) concept, and is thus, termed MPDCC-VR. Because MPDCC is capable of achieving very long prediction horizons, the VR-based reference terms are predicted in conjunction with the state trajectories at each time-step, thus enabling more accurate decisions to be made by the controller. Simulation results verify the applicability of the MPDCC-VR to an MV case study. It is shown that the proposed approach exhibits a very good level of steady-state performance in the presence of grid voltage distortion, and at the chosen operating point is capable of substantially outperforming multiloop control with space vector modulation. Experimental results, which are provided for a down-scaled prototype, show excellent agreement with those obtained in simulation, further validating the proposed MPDCC-VR strategy.

Modular Multi-level Converters (M2LCs) are mostly controlled by using a hierarchical control scheme, where at least two control loops are required for controlling the load currents and balancing the capacitor voltages. This paper proposes a single controller, which is based on Model Predictive Direct Current Control (MPDCC) with long prediction horizons, to directly control the load currents within tight bounds around their sinusoidal references and balance the capacitor voltages. MPDCC uses a model of the converter for an online optimization process to deliver the best possible performance during both steady-state and transient operating conditions. A conceptual description and control algorithm of the proposed controller are presented in this paper. To validate the proposed concept, simulated performance of a three-phase, three-level 2 MVA grid connected M2LC is presented with a discussion. A comparison with a vector control (VC) pulse width modulation (PWM) scheme is also carried out to demonstrate the improvements in performance associated with the MPDCC scheme.

Model predictive control (MPC) is an efficient and growing approach to power converter control. This paper proposes an improved and simplified model predictive current control (MPCC) technique for a four-level nested neutral point clamped (4L-NNPC) converter. Conventional MPCC exhibits better performances as compared to the conventional linear control system such as fast dynamic response, consideration of the system constraints, and nonlinearities. However, the application of the conventional model predictive current control (MPCC) approach on complex systems provokes a significant number of calculations, which is the main hurdle to its practical implementation. To fix this flaw, this paper proposes an effective algorithm to shorten the execution time of the conventional MPCC. In this proposed technique, 216 current predictions of the conventional MPCC are skipped and converted into one required voltage vector (RVV) prediction. With this equivalent reference voltage transformation, the calculation burden of MPCC is significantly reduced, while the output performance is not influenced. The results of the simplified MPCC for the 4L-NNPC converter are analyzed and compared with the conventional MPCC. The computational time is reduced by 19.56% using the simplified MPCC, while keeping an approximately similar error of output currents. The switching frequency and total harmonic distortion (THD) of the proposed method are reduced by 8.16% and 0.07%, respectively, as compared to the conventional technique. These results demonstrate the fact that that the performance of a conventional MPCC is enhanced with the proposed MPCC. The proposed algorithm can be applied to several inverter topologies.

This study proposes a model predictive stator current control (MPSCC) strategy of doubly fed induction generator (DFIG) under unbalanced grid voltage conditions. Sinusoidal and balanced stator currents injected into the power grid can be ensured due to the direct control of stator currents rather than rotor currents. Model predictive control instead of traditional vector control is adopted, which can increase the current loop bandwidth and obtain faster dynamic responses. Conventional resonant regulators such as second‐order generalised integrators or second‐order vector integrators that are usually used to eliminate unbalanced components in stator currents can also be avoided. Moreover, both extractions of negative sequence components in rotor or stator currents and calculation of commanded rotor current are avoided, which simplifies the control scheme. The proposed MPSCC strategy can also implement the grid connection by introducing the virtual stator currents without any changes in the control scheme. Finally, experimental results based on a 1 kW lab DFIG system are provided to validate the effectiveness of the proposed control strategy.

This paper presents an impartial comparison of the dynamic behavior of permanent magnet synchronous motors (PMSM) under three control techniques i.e., model predictive current control (MPCC), model predictive direct speed control (MPDSC) and field-oriented control (FOC). In MPCC, the voltage vectors applied to the inverter are determined by minimizing a cost function comprising the error in currents; whereas, in MPDSC, the voltage vectors applied to the inverter are determined according to minimization of cost function which includes speed and current errors. In MPCC, PI controller is used to regulate the speed, whereas in the MPDSC, there are no PI controllers. When the FOC is applied, modulated voltage vectors are determined according to three PI controllers for speed and current control. Most crucial implementation issues, which affects the dynamic performance of PMSMs, are addressed during the comparison between three control techniques. These issues include delay time, switching frequency and parameters mismatch. The delay time, due to the large number of calculations, is compensated by predicting the state variables two sampling intervals ahead. Switching frequency is included in the objective function to reduce switching losses. The effect of delay time and switching frequency is related to MPCC and MPDSC; meanwhile parameters mismatch is associated with all control methods. The dynamic behaviors are assessed via simulation results at different load conditions. The final evaluation was performed in terms of rising time, settling time, total harmonic distortion and average switching frequency.

According to the characteristics of dual buck full-bridge inverter, an improved model predictive current control (MPCC) strategy is used. Compared with the traditional model predictive current control scheme, the new model predictive current control can reasonably allocate times of different switching state through the principle of the minimum current error in a single sampling period, which can reduce the inductor current ripple caused by dual buck full-bridge inverter and improve output waveform quality of the dual buck full-bridge inverter. Simulation and experimental results verify the feasibility and correctness of this scheme.

The research object of this paper is single-phase PWM rectifier, the purpose is to reduce the total harmonic distortion (THD) of the grid-side current. A model predictive current control (MPCC) with fixed switching frequency and dead-time compensation is proposed. First, a combination of an effective vector and two zero vectors is used to fix the switching frequency, and a current prediction equation based on the effective vector’s optimal action time is derived. The optimal action time is resolved from the cost function. Furthermore, in order to perfect the established prediction model and suppress the current waveform distortion as a consequence of the dead-time effect, the dead-time’s influence on the switching vector’s action time is analyzed, and the current prediction equation is revised. According to the experimental results, the conclusion is that, firstly, compared with finite-control-set model predictive control, proportional-integral-based instantaneous current control (PI-ICC) scheme and model predictive direct power control (MP-DPC), the proposed MPCC has the lowest current THD. In addition, the proposed MPCC has a shorter execution time than MP-DPC and has fewer adjusted parameters than PI-ICC. In addition, the dead-time compensation scheme successfully suppresses the zero-current clamping effects, and reduce the current THD.

In this article, the predictive horizon of model predictive current control (MPCC) is extended for achieving better operational performance. However, the computational burden of MPCC exponentially increases with its predictive steps, and thus, the predictive step is usually chosen as one. In order to explore the potential advantage of multistep MPCC (M-MPCC), one simplified search method is proposed so that the M-MPCC can be executed in practice with greatly reduced complexity. In the proposed search method, a large number of possible voltage vector sequences can be excluded in advance with the guide of the defined reference voltage vector sequence. Then, one simplified M-MPCC is applied to two 3 kW arc induction machines with a large radius, which can make a full demonstration on the drive performance of linear induction machine in practice. The comprehensive simulation and experiments have clearly indicated the relationship between the predictive step and the performance of the proposed M-MPCC. It has persuasively verified the feasibility of low complexity and decreasing time consumption based on the proposed search method in this work.

In this paper, using the two control methods of model predictive control (MPC), model predictive current control (MPCC) and model predictive torque control (MPTC) is applied to the control system of permanent magnet synchronous motor(PMSM). The algorithms are based on the minimization of different cost functions, and the corresponding optimal voltage vector can be obtained. Comparison of the dynamic and steady state performance of the tested PMSM with MPCC and MPTC is elaborated. The delay compensation is considered to reduce the fluctuation of current and torque, in order to reduce the computational burden by using a single vector, comparison of two methods in the speed control system of dynamic and steady-state performance and speed tracking performance and resistance to load disturbance. The simulation results show that both MPCC and MPTC can be well applied to the PMSM driving systems with good speed tracking performance. However the MPCC can achieve less harmonics in the stator currents, and consequently the circular flux linkage trajectory is smoother, while the MPTC shows advantages in smaller ripples in the mechanical torque.

Nowadays, model predictive current control strategy has become a viable alternative because of its fast response for high-reliability systems, such as multiphase machines. In that regard, this paper proposes a comparative assessment of four current controllers based on the model using different approaches as virtual vectors, modulation techniques and further, combining these strategies in order to deal at the same time with the regulation of the main and secondary currents components, known as (α-s) and (x-y), respectively, applied to six-phase induction machines. Simulation results are presented so as to show the effectiveness of the four model predictive current controllers, taking into account the mean squared error and the total harmonic distortion of the stator currents in both steady and dynamic conditions.

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.

The research on grid-connected inverters (GCI) is gaining momentum as more and more focus is given to clean energy sources like solar photovoltaic (PV) and wind energy. Conventional GCI uses the proportional-integral (PI) controller in conjunction with the sinusoidal pulse width modulation (SPWM) technique for pumping power to the grid. Model predictive control (MPC) is becoming more relevant in the field of power electronics. Conventional MPC uses a single prediction horizon or single step. This paper proposes the estimation of the optimization criterion for more than one prediction horizon, termed a multistep MPC. The multistep MPC is an extension of the basic MPC. The MATLAB/Simulink simulations of the proposed multistep MPC and the conventional single step MPC are presented in this paper. The total harmonic distortion (THD) values are compared between the conventional MPC and multistep MPC for varied operating conditions. The settling time of the controllers as well as the deviations in the cost function are compared. The simulation results indicate a better performance of the multistep MPC in comparison with a conventional single horizon MPC.