Set Model Predictive Current Control Research Articles

An Improved Finite Control Set Model Predictive Current Control for a Two-Phase Hybrid Stepper Motor Fed by a Three-Phase VSI

In this paper, an improved finite control set model predictive current control (FCS-MPCC) is proposed for a two-phase hybrid stepper motor fed by a three-phase voltage source inverter (VSI). The conventional FCS-MPCC selects an optimal voltage vector (VV) from six active and one null VVs by evaluating a simple cost function and then applies the optimal VV directly to the VSI. Though the implementation is simple, it features a large current ripple and total harmonic distortion (THD). The proposed improved FCS-MPCC builds an extended control set consisting of 37 VVs to replace the original control set with only seven VVs. The increase in the amount of VVs helps to regulate the current more accurately. In each control period, the improved FCS-MPCC takes advantage of deadbeat control to calculate a reference VV, and only the three VVs adjacent to the reference VV are predicted and evaluated, which decrease the computational workload significantly. Build waveform patterns for all VVs in the unbalanced circuit structure to modulate the optimal VV using discrete space vector modulation, which improves the current quality in reducing current ripple and THD. The comparative simulations and experimental results validate the effectiveness of the proposed method.

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.

Model predictive control of grid-connected PV power generation system considering optimal MPPT control of PV modules

Because of system constraints caused by the external environment and grid faults, the conventional maximum power point tracking (MPPT) and inverter control methods of a PV power generation system cannot achieve optimal power output. They can also lead to misjudgments and poor dynamic performance. To address these issues, this paper proposes a new MPPT method of PV modules based on model predictive control (MPC) and a finite control set model predictive current control (FCS-MPCC) of an inverter. Using the identification model of PV arrays, the module-based MPC controller is designed, and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature. An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors, the optimal voltage vector is selected according to the optimal value function, and the corresponding optimal switching state is applied to power semiconductor devices of the inverter. The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified, and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink. The results show that MPC has better tracking performance under constraints, and the system has faster and more accurate dynamic response and flexibility than conventional PI control.

An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches

The control performance of the finite control set model predictive current control (FCS-MPCC) for the interior permanent magnet synchronous machine (IPMSM) depends on the accuracy of the mathematical model. A novel robust model predictive current control method based on error compensation is proposed in order to reduce the parameter sensitivity and improve the current control robustness. In this method, the equivalent parameters are obtained from the known voltage and current information at the past time and the error between the predicted current and the actual current at the present time, which is utilized in the two-step prediction process to compensate the parameter mismatch error. Finally, the optimal voltage vector is selected by the cost function. The proposed method is compared with the traditional model predictive current control method through experiments. The experimental results show the effectiveness of the proposed method.

Finite control set model predictive direct current control strategy with constraints applying to drive three-phase induction motor

In this, work the finite control set (FCS) model predictive direct current control strategy with constraints, is applied to drive three-phase induction motor (IM) using the well-known field-oriented control. As a modern algorithm approach of control, this kind of algorithm decides the suitable switching combination that brings the error between the desired command currents and the predicated currents, as low as possible, according to the process of optimization. The suggested algorithm simulates the constraints of maximum allowable current and the accepted deviation, between the desired command and actual currents. The new constraints produce an improvement in system performance, with the predefined error threshold. This can be applied by avoiding the switching combination that exceeds the limited values. The additional constraints are more suitable for loads that require minimum distortion in harmonic and offer protection from maximum allowable currents. This approach is valuable especially in electrical vehicle (EV) applications since its result offers more reliable system performance with low total harmonics distortion (THD), low motor torque ripple, and better speed tracking.

Model Predictive Current Control with Asymmetric Stacked Multilevel Inverter and LCL-Filter Based STATCOM

The work presented in this paper deals with a proposal of a new topology of a multilevel inverter to act as a Static synchronous Compensator (STATCOM). The proposed inverter is the five-level Asymmetric Stacked Multi-Level Inverter (ASMLI). One of the essential features of this inverter that distinguishes it from the conventional types is that it achieves the required voltage levels with fewer switching devices, leading to simplifying the control process. Moreover, the work includes using a Finite Control Set Model Predictive Current Control (FCS-MPCC) to control the proposed structure. The FCS-MPCC control strategy performs the finite optimization process at the current sampling instant to provide the optimum switching states to the inverter at the next sampling instant. Therefore, this control strategy allows injecting harmonic current and reactive power compensation to reduce source current distortion and improve the voltage profile and power factor. The optimization mechanism reduces the cost function, which is a function of measuring the network current's deviation from the reference value and how the capacitor voltage deviates from the required values. LCL-filter was used to connect this setup to the grid, and its resonance was actively damped using the multivariable capabilities of the FCS-MPCC. The proposed control framework was simulated using MATLAB/Simulink 9.1 environment and tested in a distorted and healthy network compared to a conventional two-level converter with RL-filter. The STATCOM was used to inject reactive power to raise the source power factor to unity and reduce source current harmonics by injecting harmonic current. The proposed prototype could absorb 70% of source current harmonics, which is nearly 25% better than a conventional inverter, inject an appropriate amount of reactive power, and raise the source power factor to unity in two case scenarios. The performance achieved was promising at steady-state operation and speedy response during transients with balanced capacitors voltages.

Virtual-Vector-Based FCS Model Predictive Current Control with Duty Cycle Optimization for Dual Three-Phase Motors

This paper presents an improved finite control set model predictive current control (FCS-MPCC) based on virtual vectors with duty cycle control for dual three-phase permanent magnet synchronous motor (DTP PMSM) drives. Virtual vectors synthesized by two basic voltage vectors of the inverter are introduced to suppress the harmonic current by the vector space decomposition (VSD) model. And the virtual vectors with the optimal duty cycle can be obtained by duty cycle control using zero vectors to generate a fixed switching frequency. Moreover, a simple scheme of centrosymmetric switching patterns is presented for the realtime implementation. In this way, the performance of current tracking is improved while the computational burden is reduced. Finally, results of simulation analysis and DSP test for the three different strategies are given to verify the validity of the proposed strategy.

Robust adaptive observer-based finite control set model predictive current control for sensorless speed control of surface permanent magnet synchronous motor

The objective of the paper is to present the efficient and dynamic sensorless speed control of a surface permanent magnet synchronous motor (SPMSM) drive at a wide speed range. For high-performance speed sensorless control, a finite control set model predictive current control (FCS-MPCC) algorithm based on a model reference adaptive system (MRAS) is proposed. With the FCS-MPCC algorithm, the inner current control loop is eliminated, and the limitations of the cascaded linear controller are overcome. The proposed speed sensorless control algorithm provides an efficient speed control technique for the SPMSM drive owing to its fast dynamic response and simple principle. The adaptive mechanism is adopted to estimate the rotor shaft speed and position used in FCS-MPCC for dynamic sensorless control. FCS-MPCC uses a square cost function to determine the optimal output voltage vector (VV) from the switching states that give the low cost index. A discrete-time model of a system is used to predict future currents across all the feasible VVs produced by the voltage source inverter. The VV that reduced the cost function is adopted and utilized. Simulation results showed the efficacy of the presented scheme and the viability of the proposed sensorless speed control design under various load conditions at a wide speed operation range.

Finite-Set Model Predictive Current Control of Induction Motors by Direct Use of Total Disturbance

Disturbance rejection strategies are very useful for the robustness improvement of the predictive control method. But they can only be used in the modulated-based predictive control methods such as continuous set model predictive control (CS-MPC) and deadbeat control. This paper presents a robust current prediction model based on total disturbance observer (TDO), which is applicable in the finite set model predictive current control (FS-MPCC). In the proposed method, the disturbance is directly used as a part of the prediction model instead of the disturbance rejection loop. So, the proposed method has two advantages over the disturbance rejection-based CS-MPC schemes. The first advantage is no need for a controller, which is an essential part of the disturbance rejection-based CS-MPC. Therefore, the proposed method is simpler and has fewer control parameters. The second feature is that the proposed model is in the stationary frame. In this way, the frame transformation is avoided in the prediction model. Moreover, to guarantee zero steady-state error in the current prediction model, this paper proposes a complete designing process for TDO based on the convergence analysis. The performance of the proposed control system is evaluated through simulations and experimental tests.

Coast function parameters optimization for DC battery source inverter feeding three-phase inductive load

The commonly reported measures of the predictive accuracy are evaluated in this paper. Absolute, squared, percentage, and integral errors methods are implemented, to reduce the objective function, which employed in model predictive control. These methods are usually investigated for dc source inverter, which controlled by finite set model predictive current control system, with three phase induction motor load. In this paper, the evaluation includes different aspects, accuracy, complexity, system harmonics content, and execution time. A vital criterion in this process is the performance of the inverter, and the matching between the reference and the measured machine currents. The evaluation shows that for one term objective function, absolute and square errors give similar results with less execution time for the absolute error, but if multi terms objective function the square error is better.

Improved Finite Control Set Model Predictive Current Control for Five-Phase VSIs

Model predictive current control (MPCC) is widely studied and applied in five-phase VSIs, and virtual voltage vectors (V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> s) are constructed to improve steady state performance and simplify computation complexity. However, V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> s in five-phase VSIs were initially constructed using one large and one medium voltage vectors, and all existing works employed this method, which goes against the common-mode voltage (CMV) reduction. To address this problem, two improved MPCC (IMPCC) methods are proposed in this article. Firstly, the V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> s are constructed in a new way using four adjacent large vectors, which enables the proposed IMPCC methods to reduce CMV and low-order harmonic currents inherently. Thus, there are no harmonic and CMV terms in the cost function. Secondly, the duty ratio optimization is introduced and dwell time of the optimal V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> is estimated to reduce current ripples. Then, in order to achieve superior steady state performance, two switching patterns are designed, namely the asymmetrical and symmetrical ones. The asymmetrical pattern aims to reduce the switching frequency while the symmetrical one focuses on harmonics suppression. Finally, experimental comparisons between the proposed IMPCC methods and conventional methods are presented. Experimental results have verified that the proposed schemes can mitigate CMV, suppress current harmonics and reduce computation complexity, simultaneously.

Two-Step Continuous-Control Set Model Predictive Current Control Strategy for SPMSM Sensorless Drives

Integration of model predictive current control (MPCC) and the existing position estimation method is an attractive and promising strategy for surface-mounted permanent magnet synchronous machines (SPMSMs) sensorless drives. However, its system control performance is severely weakened by the filter delay and position estimation errors in the position estimation process because the conventional MPCC requires accurate rotor position information. In order to effectively resolve these problems, a two-step continuous-control-set MPCC (TS-CCS-MPCC) strategy is proposed in this paper. By adopting the two-step prediction to complete the machine mathematical model, the predictive currents can be obtained without the estimated rotor position. Meanwhile, a new proportional cost function is developed to obtain the optimal voltage vector. With the proposed control strategy, the necessary position estimation process in the conventional MPCC can be removed, and the MPCC-based sensorless control can be achieved rapidly and steadily, which not only simplifies the whole implementation process but also dramatically improves the system dynamic performance. Besides, the one-beat delay compensation and machine parameter identification are deduced to enhance the control performance of the proposed scheme. Finally, the experiments validate that the proposed sensorless control scheme can achieve excellent control performance for both steady-state and dynamic operations.

Continuous Control Set Model Predictive Current Control of a Microgrid-Connected PWM Inverter

This paper deals with a novel control architecture for pulse width modulation inverters connected to the grid through resonant LCL filters. A continuous-control-set model predictive control scheme is derived for the grid current loop. A state observer, using a moving horizon estimator algorithm, is used for reducing the order of the mathematical model representing the grid. The proposed control architecture allows for reducing the number of required sensors, for eliminating measurement noise and for achieving a good stability against voltage grid disturbances and parameter uncertainties. High performance hardware is used in order to run a quadratic programming solver in real-time, with a control sampling rate of 10 kHz. The effectiveness of the proposed control architecture is validated by means of test-bed experiments demonstrating the feasibility of real-time operation and promising results.

Multivector Predictive Current Control for Five-Phase PM Motor by Using Hybrid Duty Modulation Technology

The finite control set (FCS) model predictive current control (MPCC) is a promising control scheme for the five-phase drives due to the easy-tuning process. However, the deteriorated operational performance and huge computational burden are the real challenges. This article proposes a multivector FCS-MPCC, which can improve the operational performances without increasing computational burden. In the proposed method, the candidate virtual voltage vectors with harmonics-free are employed as the basic control set. Then, each sector is partitioned to generate more available vectors. The duty-cycle modulation technology is used to optimize the amplitude of the candidate vector. Therefore, a better control effect can be obtained. Also, a sector judgment method is proposed to determine the optimal control set, which can reduce the computational burden. By using this method, the optimal vector can be selected accurately without evaluating all the available vectors. Thus, the computational burden can be alleviated. The experimental results are presented to confirm the effectiveness of the proposed multivector FCS-MPCC method.

FPGA-Based Continuous Control Set Model Predictive Current Control for PMSM System Using Multistep Error Tracking Technique

To overcome the shortcomings of the conventional continuous control set model predictive current control (CCS-MPCC), such as large overshoot and poor robustness, an extended surface-mounted permanent magnet synchronous motor (SPMSM) model-based multistep error tracking CCS-MPCC (MSET-CCSMPCC) is proposed in this article. First, a traditional CCS-MPCC is derived based on the conventional SPMSM model and its robustness is analyzed by considering the parameter mismatches. Second, an extended SPMSM model is given by incorporating the lumped disturbances into one disturbance part. Third, a sliding mode differentiator improved fast terminal sliding mode disturbance observer is designed to track the disturbances. Fourth, by compensating the extended SPMSM model for the estimated d- and q-axes disturbances, an extended SPMSM model-based CCS-MPCC (EXM-CCSMPCC) is designed. However, the EXM-CCSMPCC has serious step response overshoot. Fifth, an extended SPMSM model-based single step error tracking CCS-MPCC is presented, whose dynamic response and steady-state performances deteriorate when the overshoot is reduced. Finally, an MSET-CCSMPCC is proposed to reduce the overshoot and improve the robustness while maintaining excellent dynamic and steady-state performances. Experiments are implemented on a field-programmable gate array based hardware system to verify the excellent performances of the proposed method.

Three-vector-based model predictive current control with disturbance feedforward compensation

Finite control set model predictive current control (FCS-MPCC) has attracted the attention of a large number of researchers due to its intuitive idea, fast dynamic response and control objective flexibility. However, FCS-MPCC has a high steady-state current ripple. Three-vector-based model predictive current control (TV-MPCC) is an effective method to improve steady-state performance. However, it results in high motor parameter dependency. In this paper, the effect of motor parameters mismatch in FCS-MPCC has been analyzed. The control performance of FCS-MPCC is impacted when the motor parameters are mismatched. Then based on the disturbance estimation technique, a disturbance feedforward compensation based generalized proportional integral observer is used to reduce the disturbances and improves the robustness performance of the system. Experimental results show that the proposed method effectively improve the robustness of the system.

Nonparametric Predictive Current Control for PMSM

Conventional finite-control set model predictive current control predicts the motor behavior for every switching state and selects the voltage vector that can minimize the cost functions as an optimum voltage vector. To get rid of the model parameter dependency, a finite-control set nonparametric predictive current control (NPCC) was proposed before, which can predict future current behavior using the measured data instead of initial model parameters. However, one of the main issues that are limiting the current and torque ripple performance is a stagnant current update mechanism. To overcome this issue, this article proposes a novel current update mechanism that based on two most recent current variations to reconstruct the permanent magnet synchronous machine model. The proposed nonparametric predictive current control based on the current update mechanism can effectively reduce the torque ripple and enhance current performance in contrast to NPCC. Experiment results are demonstrated to verify the effectiveness of the proposed method under different operating conditions.

Low-Switching-Loss Finite Control Set Model Predictive Current Control for IMs Considering Rotor-Related Inductance Mismatch

This paper develops a high-performance finite control set model predictive current control (FCS-MPCC) method for induction motors (IM) to ensure the system control performance over the low-switching-frequency (LSF) range, where the switching loss is low. The new controller is based on tripartite calculations in each control period and sliding mode (SM) rotor-related inductance observer. In detail, firstly, to solve the problem that the control performance of the traditional FCS-MPCC methods is low when they are used at low frequencies, tripartite calculations are employed in each control period to improve the prediction accuracy. By dividing one control period into three equal parts and executing the prediction algorithm in each subsection, the current estimation errors become lower compared to the conventional single-step Euler-discretization controller. Then, considering that the performance of an FCS-MPCC controller highly relies on the machine parameter values, but it is hard to directly obtain the accurate rotor-related inductances (mutual inductance and rotor inductance), this paper uses an online parameter observer based on the sliding mode (SM) principle to diagnose them in real time. It needs to be mentioned that when discussing the stability of the sigmoid-function-based SM observer, a new technique called estimation-error-limitation is initially adopted in this paper to simplify the analysis process. Finally, the proposed algorithms are verified by simulation, which is conducted on a three-phase IM at LSF situations.

On Offset-Free Continuous Model Predictive Current Control of Permanent Magnet Synchronous Motors

In this work, an offset-free continuous control set model predictive current control (CCS-MPCC) strategy for synchronous machines based on a slack formulation of the Primal-Dual Interior-Point method is proposed. A horizon of two steps is achieved within 100 µs sampling period. To account for robustness against model mismatch and uncertainty, an incremental formulation of the MPC problem is used to ensure zero steady-state tracking error. The proposed controller is compared with the state of the art Field Oriented Control with PI controllers (FOC-PI), with the Deadbeat Model Predictive Current Control (DB-MPCC), and with the latter controller combined with discrete integrators in the feedback loop (DB-MPCC-I). Experimental results on a 0.5 kW PMSM prove that the proposed CCS-MPCC has outperformed the state of the art control techniques typically used to control electrical machines.

Virtual-Vector-Based Model Predictive Current Control of Five-Phase PMSM With Stator Current and Concentrated Disturbance Observer

The finite control set model predictive current control(FCS-MPCC)can significantly improve the dynamic performance of the five-phase permanent magnet synchronous motor (5P-PMSM), but its control performance highly depends on the accuracy of the model parameters. The parameter error compensation based on the first-order sliding mode observer can improve the robustness of FCS-MPCC under Model parameter mismatch. The introduction of the sign function leads to chattering in the observation results. In order to eliminate this chattering, a first-order low-pass filter needs to be added for compensation. However, the introduction of filters will increase system design difficulty and bring system delays, which will affect the compensation effect. Therefore, a second-order sliding mode observer, based on the variable-gain Super-Twisting algorithm, is proposed to realize the parameter error estimation and one-beat delay compensation in this paper. By establishing the Lyapunov function, the stability proof of the proposed observer, and the calculation method of the observer parameters are given. Besides, the third harmonic current can be suppressed, and the computational burden of the control unit can be reduced by controlling the proportion of medium vector and large vector reasonably. According to the deadbeat control, the optimal virtual voltage vector action time in each cycle is obtained, which improves the tracking ability of the reference current. At last, Extensive experimental results validate that i) the proposed observer can significantly improve the robustness of model predictive current control even under parameters mismatch; ii) the proposed control method can effectively enhance the dynamic performance of the 5-phase PMSM.