Set Model Predictive Current Control Research Articles

Novel Compensation Strategy for Calculation Delay of Finite Control Set Model Predictive Current Control in PMSM

Time delay of the finite control set model predictive current control caused by a large amount of calculations will degrade the control performance of the permanent magnet synchronous machine system. This letter first demonstrates the delay issue at length. Then, a new direct compensation method, by predicting the current variation within the delay time, is proposed. In comparison with the traditional two-step prediction strategy, the novel method is also easy to implement and can single out the best switching state in each period for optimal control. Simulation and experimental results are presented to verify the effectiveness of the proposed method.

An Improved Finite Control Set Model Predictive Current Control Strategy for Five-Phase PMSMs

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.

A Robust Deadbeat Finite Set Model Predictive Current Control Based on Discrete Space Vector Modulation for a Grid-Connected Voltage Source Inverter

This paper proposes a deadbeat finite set model predictive current control based on discrete space vector modulation (DSVM) in order to achieve robust characteristics from the grid impedance variation while guaranteeing high performance of output current waveforms and a fixed switching frequency. The proposed DSVM strategy enables to obtain a large number of virtual voltage vectors without complex initialization process or lookup table, which are the drawbacks of the conventional DSVM strategies. A deadbeat technique was adopted to minimize the number of candidate voltage vectors considered in the cost function in order to avoid the high computational burden caused by considering all voltage vectors. The simulation and experimental results were presented to verify the effectiveness and performance of the proposed method using a two-level grid-connected voltage source inverter.

Speed sensorless model predictive current control of doubly-fed induction machine drive using model reference adaptive system

This paper presents a speed sensorless control scheme named as finite control set-model predictive current control (FCS-MPCC) using a modified fictitious ohmic quantity (R) based model reference adaptive system (MRAS) for grid-connected doubly-fed induction machine (DFIM) drive. The variables of the reference model of this speed sensorless scheme (R-MRAS) are represented in stationary reference frame while those for the adaptive model are denoted in synchronously rotating reference frame. The sensorless formulation thus obtained is completely independent of any stator/rotor resistance terms. The scheme is also devoid of any stator/rotor flux estimation. Moreover, the intuitiveness of FCS-MPCC brings in additional flexibility in comparison to the conventional control techniques like field oriented control (FOC) and direct torque control (DTC). The overall scheme demonstrates faster execution time than FOC/DTC based control of DFIM drive. The proposed control algorithm is simulated and tested for limited speed range application in MATLAB/Simulink. The validation of simulation results are further done by experimentation on a dSPACE-1103 based DFIM laboratory setup.

Finite control set model predictive current control of a five-phase PMSM with virtual voltage vectors and adaptive control set

This paper presents an improved finite control set model predictive current control (FCS-MPCC) of a five-phase permanent magnet synchronous motor (PMSM). First, to avoid including all the 32 voltage vectors provided by a two-level five-phase inverter into the control set, virtual voltage vectors are adopted. As the third current harmonics can be much reduced by virtual voltage vectors automatically, the harmonic items in the cost function of conventional FCS-MPCC are not considered. Furthermore, an adaptive control set is proposed based on voltage prediction. Best control set with proper voltage vector amplitude corresponding to different rotor speed can be achieved by this method. Consequently, current ripples can be largely reduced and the system performs much better. At last, simulations are established to verify the steady and transient performance of the proposed FCS-MPCC, and experiments based on a 2 kW five-phase motor are carried out. The results have validated the performance improvement of the proposed control strategy.

Model predictive control for parallel three‐level T‐type grid‐connected inverters in renewable power generations

As the penetration of renewable power generation units connected to the grid increases, high power quality and high efficiency have been widespread concerned. The parallel 3LT 2 Is are necessary to improve the quality and efficiency of applications. The use of common dc-link without isolating transformers will cause the zero-sequence circulating current (ZSCC) problem. This study proposes a finite control set model predictive current control to suppress the ZSCC, balance the neutral-point (NP) voltage as well as control the currents with fast transient response. The cause of the ZSCC generated in the parallel 3LT 2 I is analysed. The proposed ZSCC suppression technique is achieved through changing the redundant small vectors in the parallel 3LT 2 Is. Moreover, the coupling problem between the ZSCC and NP balance is solved by the amplitude comparison of NP voltage and ZSCC. As a result, better ZSCC suppression and NP voltage performance can be achieved in different output currents and filter inductance condition. The proposed method has been verified experimentally.

Speed sensorless model predictive current control with ability to start a free running induction motor

In this study, a speed-sensorless finite control set-model predictive current control method is proposed based on an adaptive full order observer. The control system features simplicity and low cost because of no requirement for speed measurement, modulator and tuning of weighting factors. In most sensorless based schemes, the motor is assumed to be started from standstill. There is limited research considering starting a free running motor with unknown rotational direction and speed. To start a free-running induction motor (IM), the feedback gain matrix is designed to guarantee the convergence of estimated speed to actual speed even with incorrect initial value. To improve the efficiency, amplitude of flux is adjusted along with load condition. The presented results show that the proposed method is able to smoothly start an IM with unknown initial speed and work well over a wide speed range. The effectiveness of the proposed method is verified by both simulation and experimental tests on a two-level inverter fed IM drive platform.

A simplified PWM strategy based on MPC for NPC three-level APF systems

As a control algorithm for high performance of active power filter (APF) systems, finite control set model predictive current control (FCS-MPC) has the disadvantages of unfixed switching frequency and large amount of calculations for three-level APF systems. Therefore, in order to overcome the drawback of FCS-MPC, a simplified Pulse Width Modulation (PWM) strategy based on MPC is proposed in this paper. First, the strategy, with no rolling of all the 27 vectors, adopts the idea of a cost function to obtain the optimal reference vector of each phase. Second, the duration time of each vector in this strategy can be directly calculated by judging the sector of the virtual reference vector. Third, the neutral-point balancing problem is solved by modifying the duration time. In the meantime, the strategy is studied in detail and verified experimentally in this paper. The experiment results indicate the validity and effectiveness of the proposed strategy.

Finite Control Set Model Predictive Current Control for a Cascaded Multilevel Inverter

In this paper, a Finite Control Set Model Predictive Control (FCS-MPC) for a five level cascaded multilevel inverter (CMLI) with reduced switch topology is proposed. Five switches are used here instead of conventionally used eight switches. The main contribution of this paper is to make the MPC controller work for the reduced switch topology using only 19 voltage vectors in place of conventional 61 voltage vectors for a five level CMLI. This simplifies the execution of the MPC algorithm, paving a way for the significant reduction in the computational time. The controller makes use of the excellent ability of MPC to multitask, by adding one more objective which is to reduce the average switching frequency in addition to controlling the load current. This is especially important, since switching losses and therefore switching frequency is significant for high-power applications. The trade-off of this MPC is that the current is not as smooth as the 61 vector scheme, but well within the limits of IEEE standards. The results shown prove that this MPC works well in steady state and dynamic conditions too.

Sensorless Finite-Control Set Model Predictive Control for IPMSM Drives

This paper investigates the feasibility of a sensorless field-oriented control combined with a finite-control set model predictive current control (FCS-MPC) for an interior permanent-magnet synchronous motor. The use of an FCS-MPC makes the implementation of most of the existing sensorless techniques difficult due to the lack of a modulator. The proposed sensorless algorithm exploits the saliency of the motor and the intrinsic higher current ripple of the FCS-MPC to extract position and speed information using a model-based approach. This method does not require the injection of additional voltage vectors or the periodic interruption of the control algorithm and consequently it has no impact on the performance of the current control. The proposed algorithm has been tested in simulation and validated on an experimental setup, showing promising results.

Finite Control Set Model Predictive Current Control FCS-MPC Based on Cost Function Optimization, with Current Limit Constraints for Four-Leg VSI

Finite Control Set Model Predictive Current Control FCS-MPC Based on Cost Function Optimization, with Current Limit Constraints for Four-Leg VSI

Finite Control Set Model Predictive Current Control FCS-MPC Based on Cost Function Optimization, with Current Limit Constraints for Four-Leg VSI

A Matlab/Simulink model for the Finite Control Set Model Predictive current Control FCS-MPC based on cost function optimization, with current limit constraints for four-leg VSI is presented in this paper, as a new control algorithm. The algorithm selects the switching states that produce minimum error between the reference currents and the predicted currents via optimization process, and apply the corresponding switching control signals to the inverter switches. The new algorithm also implements current constraints which excludes any switching state that produces currents above the desired references. Therefore, the system response is enhanced since there is no overshoots or deviations from references. Comparison is made between the Space Vector Pulse Width Modulation SVPWM and the FCS-MPC control strategies for the same load conditions. The results show the superiority of the new control strategy with observed reduction in inverter output voltage THD by 10% which makes the FCS-MPC strategy more preferable for loads that requires less harmonics distortion.