Conventional Finite Control Set Model Predictive Control Research Articles

Dynamic-Linearization-Based Predictive Control of a Voltage-Source Inverter

In pursuit of accurate and fast trajectory tracking of power converters, an explicit model is commonly used in the finite control-set model predictive control (FCS-MPC) framework to predict precise behaviors of controlled variables. In reality, however, the model mismatch is inevitable, which causes the inherent challenges of parameter sensitivity and model uncertainties of the FCS-MPC method. This article proposes a dynamic-linearization-based predictive control architecture to circumvent such model dependence while keeping the attractive features of the conventional FCS-MPC method. By integrating the data-driven feature of the dynamic-linearization approach, the detailed model used in the FCS-MPC controller is replaced by a virtual equivalent data model, creating a data-driven predictive control architecture. The suggested method selects optimal control action solely based on the input-output data, exhibiting strong rejection against parameter variations whilst inheriting the distinctive property of the conventional FCS-MPC method. Finally, the proposed design is validated through comparative simulation and experimental results on a three-level neutral-point-clamped inverter.

Robust Control of Voltage Source Converters: A Tube-Based Model Predictive Approach

Finite control set-model predictive control (FCS-MPC) operates based on the assumption that optimal control responses executed on a prediction model match closely an actual system. Thus, model uncertainties and external disturbances affect the controller’s performance. To weaken the impact of uncertainties, this brief proposes a tube-based FCS-MPC to control the output voltage of a voltage source converter (VSC). The proposed controller is composed of two FCS-MPCs. The first one generates an initial control action and a prediction of the output voltage based on a system, including no uncertainties. This control action and the output voltage are then sent to the second FCS-MPC, together with the output voltage of the uncertainty-involved system, to choose the best switching states of the VSC. Simulation and experimental validations demonstrate the proposed method’s effectiveness and robustness compared to a conventional FCS-MPC.

Multipurpose FCS Model Predictive Control of VSC-Based Microgrids for Islanded and Grid-Connected Operation Modes

This article presents an enhanced control strategy for renewable energy resources connected to the grid through voltage-sourced converters (VSCs) in microgrids. The proposed scheme contains a voltage control loop with the minimum inverter switching, a power-sharing controller with the minimum inverter switching, a negative-sequence current controller, and a loop to identify the control system operation mode. All the controllers are designed using the multipurpose finite control set-model predictive control (FCS-MPC) strategy. Since these controllers use the dynamic current and VSC voltage, they can be applied in grid-connected and island operation modes and transferred between them. The method uses voltage–frequency control instead of power control for VSCs. One inverter controls voltage, and the other controls current. The conventional FCS-MPC is enhanced to reduce the computation power by eightfold. This improvement is significant because the maximum switching frequency is limited in practical implementations. Also, the superiority of the proposed multipurpose control scheme is proved theoretically. Simulation is implemented using MATLAB software and compared with methods in the literature. The simulation demonstrates that the presented control strategy is efficient, authentic, and compatible. The proposed method is also tested and validated in hardware experiments.

Modulated Model Predictive Control of Modular Multilevel Converters Operating in a Wide Frequency Range

The finite control set model predictive control (FCS-MPC), which considers corresponding instantaneous power in the cost function, becomes a promising solution that allows modular multilevel converters (MMCs) to operate in a wide frequency range due to its ability to balance the internal energy. However, FCS-MPC usually suffers from a heavy computational burden. The modulated MPC, which combines modulation techniques with MPC methods, is usually employed to overcome such a challenge. In this paper, we propose an exhaustive active-set method to implement the modulated MPC scheme enabling the MMC to operate over a wide frequency range. With the proposed method, the optimal solution can be obtained by enumerating all possible active sets rather than iterating, so the worst-case computational burden (or calculation time) can be determined. Finally, a comparative study of our proposed method with conventional modulated MPC methods and FCS-MPC has been conducted to demonstrate the effectiveness of the proposed method.

Low-Complexity Finite Set Model Predictive Control for Split-Capacitor ANPC Inverter With Different Levels Modes and Online Model Update

In this article, an improved finite-control-set model predictive control (FCS-MPC) is presented for an active neutral point clamped (ANPC) topology. The considered converter significantly reduces the required power electronics components compared with other common dc-link converters, where only seven active switches, one bidirectional switch, and two floating capacitors (FCs) are employed to produce nine levels in the phase voltage. The developed FCS-MPC handles three control objectives with only one weighting factor, namely, current control, FC balancing, and NP potential stabilization, which reduces the cumbersome effort required for weighting factors coordination. In addition, the number of iterations required to identify the optimal vector is significantly reduced, which, in turn, reduces the execution time of the algorithm. The proposed control method empowers the considered converter to operate in different modes under the faulty condition of the bidirectional switch without any structure modification, which guarantees continuous operation of the converter while ensuring the balancing of FCs and dc-link capacitors in all operating modes. The sensitivity of the proposed FCS-MPC to parameter mismatch, which is a basic issue of MPC-based techniques, is tackled by employing an extended Kalman filter (EKF) to online estimate the system parameters. The proposed FCS-MPC algorithm is experimentally validated and compared with the conventional FCS-MPC method under different operating conditions.

Research on AC Electronic Load with Energy Recovery Based on Finite Control Set Model Predictive Control

Nowadays, AC electronic loads with energy recovery are widely used in the testing of uninterruptible power supplies and power supply equipment. To tackle the problems of control difficulty, strategy complexity, and poor dynamic performance of AC electronic load with energy recovery of the conventional control strategy, a control strategy of AC electronic load with energy recovery based on Finite Control Set Model Predictive Control (FCS-MPC) is developed. To further reduce the computation burden of the FCS-MPC, a simplified FCS-MPC with transforming the predicted variables and using sector to select expected state is proposed. Through simplified model and equivalent approximation analysis, the transfer function of the system is obtained, and the stability and robustness of the system are analyzed. The performance of the simplified FCS-MPC is compared with space vector control (SVPWM) and conventional FCS-MPC. The results show that the FCS-MPC method performs better dynamic response and this advantage is more obvious when simulating high power loads. The simplified FCS-MPC shows similar control performance to conventional FCS-MPC at less computation burden. The control performance of the system also shows better simulation results.

Direct Model Predictive Control of Synchronous Reluctance Motor Drives

This paper investigates a finite-control set model-predictive control (FCS-MPC) algorithm to enhance the performance of a synchronous reluctance machine drive. Particular emphasis is placed on the definition of the cost function enabling a computationally light implementation while targeting good transient and steady-state performance. In particular, this work proposes the inclusion of an integral term into the cost function to ensure zero steady-state errors thus compensating for any model inaccuracies. A control effort term is also considered in the formulation of the cost function to achieve a high ratio between the sampling frequency and the average switching frequency. After a comprehensive simulation study showing the advantages of the proposed approach over the conventional FCS-MPC for a wide range of operating conditions, several experimental test results are reported. The effectiveness of the proposed control approach, including a detailed analysis of the effect of the load and speed variations, is thus fully verified providing useful guidelines for the design of a direct model predictive controller of synchronous reluctance motor drives.

Improved Model Predictive Current Control With Series Structure for PMSM Drives

In order to improve the control performance of conventional finite-control-set model predictive control (FCS-MPC) method and keep the balance between the steady-state performance and switching frequency of the system, this article proposes an improved MPC method based on the conventional MPC method. First, the reference voltage vector of the next control instant is predicted to determine the candidate voltage vectors of the next two control instants. Then, an optimal vector selection way is proposed, in which candidate voltage vectors selected by the reference voltage vector and designed cost function structure in series. Moreover, the selection process of optimal voltage vector from the candidate voltage vectors is analyzed. Finally, the comparison experiments between the conventional FCS-MPC method and the proposed MPC method are conducted. And experimental results show the validity of the proposed control strategy.

Simplified model predictive current control based on fast vector selection method in a VIENNA rectifier

This paper presents a simplified finite-control-set model predictive control (S-FCS-MPC) based on fast vector selection method in a three-level VIENNA rectifier. This method features a high-power factor, a low input current THD as a well-controlled DC-link voltage with fewer redundant vectors and lower computational load. Moreover, the converter with the proposed control technique exhibits a faster dynamic response in terms of input current and DC-link voltage compared with conventional finite-control-set model predictive control (C-FCS-MPC). In addition, the average switching frequency can be effectively reduced due to fewer switching times in a subset sector using the proposed method, which means fewer switching losses. Finally, the operation principle of the proposed algorithm has been analysed and an execution time comparison between S-FCS-MPC and C-FCS-M has been undertaken. The effectiveness of the proposed control technique has been validated using both simulation and experimental results.

Model Predictive Control With Constant Switching Frequency for Three-Level T-Type Inverter-Fed PMSM Drives

Finite control set model predictive control (FCS-MPC) shows superior dynamic performance with simple structure for motor drives. However, conventional FCS-MPC method presents unfixed switching frequency, making it intractable for the design of heat sinks and output filters. To address this issue, this article is devoted to developing a new FCS-MPC strategy with constant switching frequency for permanent magnet synchronous motor (PMSM) drives fed by three-level T-type inverter. The switching frequency regulation is realized by extending the period control technique, where the periods are measured between each rising edge and falling edge of the gate signals. The proposed algorithm can be embedded in the iterative process of the FCS-MPC, which can easily achieve the fixed switching frequency with balanced neutral point potential. The performance of this control strategy is numerically evaluated and experimentally verified by an 11-kW PMSM drive platform.

Virtual Voltage Vector Based Model Predictive Control for a Nine-Phase Open-End Winding PMSM With a Common DC Bus

Finite control set model predictive control (FCS-MPC) is a control strategy with fast response and a simple and flexible structure. However, when the control plant is complicated such as a multiphase electric machine, the application of FCS-MPC faces clear challenges. This article for the first time investigates the FCS-MPC for a nine-phase open-end winding (OW) permanent magnet synchronous machine, which is powered by nine H-bridge inverters with a common dc bus. First, in order to solve the challenge of substantial iterations in the conventional FCS-MPC, the number of control sets is simplified by reconfiguring the high level in switching states. Then, to eliminate the zero-sequence current caused by the common dc bus, the zero common-mode voltage (CMV) vector is selected. Subsequently, duty-ratio optimization is used to further reduce the available vectors. By the abovementioned measures, the number of iterations is reduced from 19 171 to 18. In order to suppress the harmonic current, the virtual voltage vectors (VVs) are designed. Each VV is synthesized by two zero CMV vectors, which can eliminate all the third and fifth harmonics in the output voltage. In addition, to achieve symmetrical pulsewidth modulation pulse sequences, a general pulse generation method for OW drive systems is proposed. Finally, the control performance of different control sets and harmonic weighting factors are evaluated and compared, and the experimental results have verified the effectiveness of the proposed methods.

Low Complexity Finite-Control-Set MPC Based on Discrete Space Vector Modulation for T-Type Three-Phase Three-Level Converters

In this article, a low complexity finite-control-set model predictive control (FCS-MPC) based on the discrete space vector modulation (DSVM) is proposed for T-type three-phase three-level (3P-3L) converters. Different from the conventional FCS-MPC, 48 virtual voltage vectors (VVs) of the converter are constructed by real VVs based on the DSVM. Thus, the performance of 3P-3L converters is significantly improved and the peak amplitude of high-order harmonics concentrates at the sampling frequency. Furthermore, two-stage FCS-MPC based on virtual VVs is proposed to reduce the computation burden. Its first stage selects one of six virtual VVs that minimizes the current tracking error. Then, these candidate VVs located in the same sector as the optimal virtual VV selected in the first stage are evaluated in the second-stage optimization. Thus, the computational efficiency has been greatly improved. To verify the validity of the proposed control method and show its superiority over the conventional FCS-MPC, experimental results are presented.

Finite‐control‐set model predictive control with a constant switching frequency for single‐phase grid‐connected photovoltaic inverter

Taking the advantage of easy implementation in the discrete digital control system, the finite-control-set model predictive control (FCS-MPC) is widely used in multi-phase or multi-level power converters. As the smallest independent operation unit in photovoltaic (PV) distributed power generation systems, the PV inverter has the most direct influence on the power quality. However, the lack of switching states and output voltage levels restricts the development of FCS-MPC in single-phase PV inverters. In order to improve the quality of the PV inverter output current, a constant switching frequency FCS-MPC (CFS-FCS-MPC) method is proposed for single-phase grid-connected PV inverter in this paper, which can thus reduce output current ripples and lower harmonics. Compared with the conventional FCS-MPC method, the proposed CFS-FCS-MPC method synthesizes the switching state with a nonzero and a zero switching state in one sampling period. In addition, an optimum allocation of switching states and execution time for the synthesized switching state is presented. Experimental results have verified the effectiveness of the proposed CSF-FCS-MPC method for the output current, achieving lower current ripples and higher current quality with a constant switching frequency.

Finite Control Set Model Predictive Control With Floating Virtual Voltage Vectors for Grid-Connected Voltage Source Converter

This article presents a new algorithm of finite control set model predictive control (FCS-MPC) with floating virtual voltage vectors (VVs) applied to grid-connected voltage source converter. The new solution improves the conventional FCS-MPC and FCS-MPC with virtual VVs, assuring a fixed switching frequency. In comparison with FCS-MPC using virtual VVs, the proposed algorithm ensures a shorter distance between floating virtual VVs and actual voltage vector achieves lower THDi and ripples of the grid current for the same sampling time. The proposed control method has been experimentally validated using a 5-kW prototype. The algorithm was examined and compared in steady and transient states as well as under grid voltage disturbances, such as higher harmonics, dips, and unbalance. The simulation results and experimental measurements confirm that the developed control technique shows a high quality of the grid current (low THDi value), high dynamic performance, and immunity in case of weak grid conditions.

Finite Control Set Model Predictive Control for AC–DC Matrix Converter With Virtual Space Vectors

AC–DC matrix converter is a kind of bidirectional converter features buck-type rectifying and boost-type inverting. Considering the coupling of ac current and dc current, and the requirement on safe commutation, the finite control set model predictive control (FCS-MPC), featuring multiple-objective capability and direct generation of gating signals without pulse width modulation (PWM) scheme, is very suitable for this topology. Also, fast transient can be obtained with the FCS-MPC. However, in conventional FCS-MPC, only one space vector in each sampling period is applied. The performance will be degraded due to the low switching frequency under limited sampling frequency in practice. In this article, an FCS-MPC with virtual space vectors is proposed for a bidirectional ac–dc matrix converter. This article uses several virtual space vectors, each formed by two real space vectors, to improve the control performance without increasing sampling frequency. To further reduce the computation burden, an efficient method to preselect space vectors is proposed. Furthermore, a thorough analysis of the effect of parameter mismatch in the proposed FCS-MPC is conducted to ensure robust performance. Both simulation and experimental results are presented to verify the effectiveness of the proposed control strategy.

Reduced-Sensors-Based Predictive Controller for LC Filtered Four-Leg Inverters

The high number ofsensors, ten in total, required by the traditional finite control set model predictive control (FCS-MPC) strategy of a four-leg inverter with LC output filter increases the complexity and cost of the system and limits the practical application and commercialization. To simplify the implementation, reduce the cost and enhance the reliability, a novel current sensorless predictive load voltage control scheme, with only four voltage sensors and no current sensors, is proposed in this work. A new dynamic model of the LC filtered four-leg inverter is constructed in the stationary ( $\alpha \beta \gamma$ ) reference frame for the proposed load voltage and capacitor current observer. The dual-boundary-layer nonlinear feedback is combined with a linear feedback term to enhance the robustness while retaining the accuracy of the observer. Extensive experiments are performed to confirm the effectiveness of the proposed controller. The results validate that the proposed sensor-less FCS-MPC with only four voltage sensors achieves an equivalent performance to the conventional FCS-MPC with ten sensors.

Modeling and Bifurcation Analysis for Photovoltaic Single-Sourced Trinary Inverter With Auxiliary Floating Capacitors Controlled by Finite-Control-Set Model Predictive Control

Asymmetric cascaded H-bridge (ACHB) trinary inverter has several advantages, such as high efficiency due to high power main H-bridge modulated under a fundamental low frequency, optimal modulation due to nonredundant output voltage levels, and reduction in output filter sizes due to high-quality staircase output waveform. However, ACHB inverter requires prestage isolated dc sources for the individual H-bridges and has inherent control issues of nonregenerative operation, being incompatible with photovoltaic source. This article proposes a single-stage, single-sourced ACHB trinary inverter topology without presources. Then, a control strategy using unity horizon length finite-control-set model predictive control (FCS-MPC) is proposed to modulate the ACHB inverter. Conventional unity horizon length FCS-MPC has presence of undesirable harmonics and chaos in its variable frequency switching waveform due to the minimum achievable error. Therefore, an exact discrete-time modeling for FCS-MPC is newly derived to show the nonlinear effects of power stage and control parameters in the closed-loop dynamics. And then, a bifurcation analysis-based design is proposed to eliminate the undesirable harmonics and chaos operation in the FCS-MPC closed-loop operation. The proposed topology and FCS-MPC controller design is implemented and verified in hardware by using a 2-kW prototype for standalone and grid-connected operation.

Model Predictive Control of Modular Multilevel Converters Using Quadratic Programming

The finite control set-model predictive control (FCS-MPC) has been adopted as an excellent choice for the applications of multilevel converters during the last two decades for its salient performance. However, in the case of modular multilevel converters (MMCs), a high amount of calculation is always involved in the implementation, making the FCS-MPC less suitable especially for an MMC with a high number of submodules. To cope with the issue, this article proposes an MPC technique for the MMC with a very low calculation cost. In each sampling period, the arm voltage references of each phase are determined analytically by solving a constrained quadratic programming problem formulated from the cost function. Both a rigorous and simplified procedure is provided to solve the optimization problem. Then, the four nearest candidates around the arm voltage references are evaluated, leading to a proper selection of arm voltage levels. Several experimental tests on an MMC prototype are carried out to validate the effectiveness of the proposed method. Results show that compared with the conventional FCS-MPC method which evaluates all voltage-level combinations, the proposed scheme presents an apparent advantage in terms of calculation cost while achieving similar performance.

Reduced-Complexity Model Predictive Control with Online Parameter Assessment for a Grid-Connected Single-Phase Multilevel Inverter

This paper proposes a finite control set model predictive control (FCS-MPC) with a reduced computational burden for a single-phase grid-connected modified packed U-cell multilevel inverter (MPUC-MLI) with two control objectives: reference current tracking and switching frequency minimization. The considered competitive topology consists of two units with six active switches and two DC sources in each unit, allowing the generation of 49 levels in the output voltage, which is considered a significant reduction in the active and passive components compared to the conventional and recently developed topologies of multilevel inverters (MLIs). This topology has 49 different switching states, which means that 49 predictions of the future current and 49 calculations of the cost function are required for each evaluation of the conventional FCS-MPC. Accordingly, the computational load is heavy. Thus, this paper presents two reduced-complexity FCS-MPC methods to reduce the calculation burden. The first technique reduces the computational load almost to half by computing the reference voltage and dividing the states of the MLI into two sets. Based on the reference voltage polarity, one set is defined and evaluated to specify the optimal state, which has a minimal cost function. However, in the second proposed method, only three states of the 49 states are evaluated each iteration, achieving a significant reduction in the execution time and superior control performance compared to the conventional FCS-MPC. A mathematical analysis is conducted based on the reference voltage value to locate the three vectors under evaluation. In the second part of the paper, the sensitivity to parameter variations for the proposed simplified FCS-MPC is investigated and tackled by employing an extended Kalman filter (EKF). In addition, noise related to variable measurement is filtered in the proposed system with the EKF. The simulation investigation was performed using MATLAB/Simulink to validate the system under different operating conditions.

Model Predictive Control of Grid Forming Converters with Enhanced Power Quality

This paper proposes an enhanced finite control set model predictive control (FCS-MPC) strategy for voltage source converter (VSC) with a LC output filter. The proposed control scheme is based on tracking the voltage reference trajectory by using only a single-step prediction within the controller horizon. Besides, the suitability of different frequency control schemes with the proposed scheme to prevent from inherent variable switching behaviour of conventional FCS-MPC is investigated. Based on that, the proposed method targets two major factors influencing power quality in grid forming applications by enhancing the output voltage harmonic distortion and also preventing variable switching behaviour of FCS-MPC. Although compared to multi-step prediction approaches, only a single-step multi-objective cost function to improve computation efficiency is utilized, the introduced control schemes are able to deliver higher power quality compared to its counterpart methods as well. Furthermore, the effect of different applied cost functions on the transient response of the system is studied and investigated for the future use of the VSC in microgrids (MGs). The effectiveness of the proposed scheme was assessed by simulation using MATLAB/SIMULINK and experiment using a 5.5 kVA VSC module and the results were in good agreement.