Finite Set Model Predictive Control Research Articles

A long-horizon move-blocking based direct power model predictive control for dynamic enhancement of DC microgrids

This research paper presents a novel long-horizon move-blocking based direct power model predictive control (DPMPC) strategy, uniquely designed to enhance the dynamic performance of boost converters in the grid-connected mode within DC microgrids. A precise dynamic model is developed considering a boost converter connected to a DC-link supplying constant power and resistive loads. To predict the system's dynamic behavior over an extended interval and enhance its performance in the presence of constant power loads, the long-horizon based finite control set model predictive control (FCS-MPC) method is introduced. To address the computational complexity associated with the conventional long-horizon DPMPC approach, a move-blocking (MB) strategy is incorporated into FCS-MPC, reducing the computational burden while improving the dynamic performance of the boost converter. Unlike recent studies that utilize DPMPC with short prediction interval, this paper presents evidence that a longer prediction interval is crucial for maintaining system stable and enhancing the dynamic response and also utilizing MB to make the control implementable in the real-time. The simulation results conducted using MATLAB/Simulink demonstrate the effective performance of the proposed strategy across various operating conditions of the boost converter. Experimental results further validate the strategy's capability to enhance the dynamic performance of the boost converter. Importantly, the experimental findings confirm the feasibility of implementing the proposed long-horizon move-blocking DPMPC strategy in real-time applications.

Simplified robust two-step model predictive control for permanent magnet synchronous motor

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.

Cooperative Decision-Making Approach for Multiobjective Finite Control Set Model Predictive Control Without Weighting Parameters

Finite control set model predictive control (FCS-MPC) has gained increasing popularity as an emerging control strategy for electrical drive systems. However, it is still a challenging task to optimize weighting parameters, as multiple objectives are involved in the customized cost function. A cooperative decision-making approach for FCS-MPC is proposed in this article, to solve the optimization problems with manifold control objectives. By splitting the cost function, the optimization problem underlying multi-objective FCS-MPC is separated into a series of decomposed optimization problems. By doing so, the dimension of the decomposed problem is reduced to one. To collect the information for decision-making, an efficient sorting algorithm is applied for each control objective. The theory behind the cooperative decision-making approach is comprehensively analyzed, to validate both the effectiveness and efficiency of the proposed scheme. More specifically, the highlight is the adaptive mechanism on the number of desired candidates, to obtain a decent performance for torque and flux. The candidate which minimizes the switching frequency is selected as the optimal. The proposed scheme is experimentally verified and compared with the existing FCS-MPC without weighting parameters.

Finite Set-Model Predictive Control for Modified quasi Switched Z Source Level 3 Off-board Charger for Active Power Oscillations Damping in Weak Grids

Finite Set-Model Predictive Control for Modified quasi Switched Z Source Level 3 Off-board Charger for Active Power Oscillations Damping in Weak Grids

Non-Weighted Two-Stage Model Predictive Control Strategy Based on Three-Level NPC Inverter

This paper investigates the asynchronous motors driven by a Three-Level Neutral-Point-Clamped Voltage Source Inverter (3L-NPC-VSI) and aims to achieve control without weight factors and reduce torque ripple. It puts forward a non-weighted two-stage Finite-Control-Set Model Predictive Control (FCS-MPC) strategy. First, a hierarchical optimization method is adopted to address the difficulty of setting weight factors in traditional FCS-MPC applications. The method offers stratified designs of three performance indices, voltage jump, common-mode voltage, and current tracking, obviating the need for weight factor setting and reducing the calculation load of predictions. Secondly, to further mitigate torque ripple, an optimal vector or vector combination is implemented at the current control layer by adhering to the principle of minimal current tracking error. During the selection of the optimal vector combination, the first vector of the combination is chosen to be the vector at the end of the present cycle. This ensures that there is at most one switch within each control period, reducing the switching losses of the two-stage FCS-MPC. Lastly, detailed simulation and experimental analyses are conducted to verify the feasibility and effectiveness of the proposed strategy.

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.

A Simplified Algorithm of Finite Set Model Predictive Control for Three-Phase CHB-Based BESSs

A Simplified Algorithm of Finite Set Model Predictive Control for Three-Phase CHB-Based BESSs

FCS-MPC for Three-Level NPC Inverter-Fed SPMSM Drives Without Information of Motor Parameters and DC Capacitor

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.

Model predictive control for Vienna rectifier with constant frequency based on inductance parameters online identification

When the finite control set model predictive control (FCS-MPC) is applied to the three-level converter, there are problems such as large current harmonics, high requirements for the computing efficiency of the microcontroller, complex multi-objective optimization and limited output vector switching. In addition,the mismatch of inductance parameter may directly affect the observation accuracy of FCS-MPC. To solve the above problem,a double vector model predictive control strategy with constant frequency strategy based on the inductance parameters on-line identification is presented.First, a single objective cost function based on the direct power is constructed by optimizing the redundant vector which is selected to balance the neutral-point potential, the design of weighting factor is avoided.At the same time, in order to effectively reduce the grid current ripple under single vector modulation, space vector pulse width modulation (SVPWM) is realized by combining with zero vector control mode. And, a parameter identification method based on model reference adaptive system(MARS) is proposed to overcome the effect of model parameter mismatch.Finally, the results show that the proposed F-MPCCF has good steady-state and dynamic performance from the static, transient and neutral-point potential control.

Application of a stabilizing model predictive controller to path following for a car‐like agricultural robot

AbstractThis work addresses the problem of path following for a car‐like agricultural robot by means of a finite control set model predictive control (FCS‐MPC) strategy that considers the control actions in a set composed of a limited amount of elements. Recent results on a stabilizing MPC formulation that replaces the classical control invariant set by a pair of inner‐outer sets are extended to preserve stability properties with different control and prediction horizons and are then used for the aforementioned application. Being particularly simple, the presented approach can explicitly deal with nonlinearities and constraints at the expense of resolution in the vehicle steering system, which in practice does not affect the controller performance as will be shown. In addition to describing the control method, simulations and a comparison with another nonlinear MPC strategy are presented to illustrate the advantages of the proposed scheme.

Temperature Control of Cement Rotary Kiln Sintering Zone Based on FCS-MPC with Soft Constraint of Generalized Triangular Interval

In the new, dry-process method of cement production, the temperature of cement rotary kiln sintering zone is a key factor in ensuring the quality of cement clinker. Based on the auto-regressive with extra inputs model, a finite control set model predictive control with soft constraint of the generalized triangular interval is proposed in this paper for the characteristics of a cement rotary kiln calcination system with multi-variable, multi-time delay, bounded disturbance, and slow dynamic process. Simulation experiments show that the steady-state error of the control algorithm proposed in this paper is smaller with better anti-disturbance performance than that of the traditional reference-trajectory-constrained, predictive control algorithm.

Common-Mode Voltage Reduction Method Based on Variable Sampling Frequency Finite Control Set-Model Predictive Control for PMSM Drive Systems

In this article, a finite control set-model predictive control (FCS-MPC) with variable sampling time is proposed. A zero-voltage vector appears in the dead time between specific voltage vectors, resulting in an unintentionally large common-mode voltage. Herein, a large common-mode voltage was suppressed, and the load current was controlled using a voltage vector combination that did not cause a zero-voltage vector in dead time. Additionally, to improve the total harmonic distortion (THD) of the load current, the intersection of the predicted current and the command current by all the volage vectors (VVs) in the combination is confirmed. The VV where the intersection occurs is selected as the optimal VV. This optimal VV is applied to the point where the predicted current and the reference current intersect. The applicable range of the sampling time should be selected by considering the calculation time and number of switching. Through the proposed FCS-MPC strategy, not only can the common-mode voltage be limited to within ±Vdc/6, but an improved THD can also be obtained compared to the existing method using fixed sampling. The proposed method was verified through PSIM simulation and experimental results.

Operation Assessment of a Hybrid Distribution Transformer Compensating for Voltage and Power Factor Using Predictive Control

Hybrid Distribution Transformers (HDTs) offer a compelling alternative to traditional low-frequency transformers (LFTs), providing auxiliary services in addition to standard functionalities. By integrating LFTs with power converters, HDTs enhance the operational capabilities of the system. The specific configuration in which converters are connected to the transformer allows for the provision of multiple services. This can not only prevent network failures but also extend the lifespan of its components, an outcome that is highly desirable in a distribution grid. This article discusses an HDT developed to mitigate voltage fluctuations in the grid and to decrease the reactive power drawn from the secondary side of traditional LFTs. A finite-control-set model predictive control (FCS-MPC), in conjunction with linear controllers, is utilized for the effective management of the HDT converters. Two separate control loops are established to regulate voltage and reactive power on the secondary side of the transformer. Results from Hardware-in-the-Loop (HIL) testing affirm the proficiency of HDT in reducing grid voltage variations by 15% and in cutting reactive power consumption by up to 94%. The adopted control strategy and topology are demonstrated to be effective in stabilizing voltage and reactive power fluctuations while concurrently facilitating the charging of the converters’ DC link directly from the grid.

Finite control set model predictive control of three-port converter for interfacing a PV–battery energy storage system to a three-phase stand-alone AC system

Abstract This paper proposes a multiport bidirectional non-isolated converter topology that provides advantages in terms of simultaneous multiple operations, single-stage conversion, high power density and reduced power losses due to the lower number of switches. The proposed multiport converter uses a centralized non-linear controller known as a finite control set model predictive controller to manage the flow of power between different ports. It deals with the parallel operation of photovoltaic and battery energy storage systems for stand-alone alternating current (AC) systems. The converter connects the lower voltage battery to the photovoltaic port using a bidirectional buck/boost converter and the photovoltaic port is linked to the stand-alone AC load through a three-phase full-bridge inverter. Each leg of the three-phase converter will act as a bidirectional direct current (DC)/DC converter as well as an inverter simultaneously. Only six switches manage the power transfer between all the connected ports of photovoltaic–battery energy storage system linked to the stand-alone AC load. The proposed multiport converter is mathematically modelled and controlled by a finite control set model predictive controller. The system is validated in simulation (1-kW rating) and experimental environment (200-W rating). The hardware prototype is developed in the laboratory and the controller is implemented on the field-programmable gate array board. Two independent case studies are carried out to validate the efficacy of the system. The first scenario is for a change in solar irradiance, while the second scenario is for a change in the output load.

Improved Sequential Model Predictive Control of Cascaded H‐Bridge Multilevel Converter

The cascaded H‐bridge (CHB) converter can achieve rich functions, such as active rectification, active power filtering, distributed energy storage, etc. The control of the CHB converter is a multiobjective optimization problem. Finite control set model predictive control (FCS‐MPC) is an effective method developed in recent years to solve this problem. However, because of the large amount of calculation, traditional FCS‐MPC cannot be directly used to control the multilevel CHB converter. It is also difficult to tune the weighting factors in traditional FCS‐MPC. To solve these problems, this paper proposes an improved sequential MPC (IS‐MPC) method for the CHB converter. The proposed method divides the control objectives of the CHB converter into different layers according to their priorities and the control degrees of freedom of each layer can be adjusted according to the system state. Simulation and hardware‐in‐the‐loop (HIL) implementation on a three‐phase 7‐level CHB converter shows that the proposed method can effectively control the ac current and submodule voltage of the CHB converter and reduce the switching loss. Moreover, it significantly reduced the calculation burden of traditional FCS‐MPC, and the use of the weighting factors is also avoided. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Augmented Finite Control Set MPC Design Technique for Wide Speed Range Control of Permanent Magnet Synchronous Motor Drives

The effective motion control task is highly influencing the final drive performance in wide-range motor drives applications such as electric vehicles (EVs), washing machines, compressors, elevators and lathe machines. Recently, permanent magnet synchronous motor (PMSM) drive has been widely employed in these applications to achieve desired performances such as high efficiency, high torque requirements with minimum size “occupancy”. The PMSM drive features combining with these modern drive applications are highly nonlinear and the desired operation requirements demand high-performance control schemes with fast system response and robustness features. Linear control schemes are usually implemented to drive the PMSM that entail trade-offs between drive performance and simplicity in control implementation. Therefore, this paper presents the nonlinear control technique based on augmented finite control set (AFCS) model predictive control (MPC) scheme (AFCS-MPC). The proposed AFCS-MPC scheme enhances the standard MPC design by augmenting the nonlinear characteristics of the PMSM drive to achieve wide speed range drive performance under both steady-state and dynamic operating conditions. Comparative performance studies have been conducted and presented to confirm the efficacy of the proposed scheme.

Guiding the Selection of Multi-Vector Model Predictive Control Techniques for Multiphase Drives

A diverse group of so-called multi-vector techniques has recently appeared to enhance the control performance of multiphase drives when a direct control strategy is implemented. With different numbers of switching states and approaches for estimating the application times, each multi-vector solution has its own nature and merits. Previous studies have individually tested each version of the proposed finite-control-set model predictive control (FCS-MPC) strategies using a single experimental setup with specific parameters and, in some cases, using a limited range of operating conditions and focusing exclusively on some control aspects. Although such works provide partial contributions, the control performance is highly affected by the test and rig conditions, being dependent on the machine parameters, the switching frequency and the range of operation. Consequently, it becomes difficult to extract some universal conclusions that guide the control designer on the best alternative for each application. Aiming to enrich the knowledge in this field and provide a broader picture, this work performs a global analysis with different multi-vector techniques, various machine parameters, multiple operating points and a complete set of indices. Experimental results confirm that the selection of the most adequate control strategy is not a trivial task because the degree to which multi-vector techniques are affected by the test conditions is variable and complex. Some tables with a qualitative analysis, based on the extensive empirical tests, contribute with a more complete insight and guide eventual control designers on the decision about the optimal regulation approach to be chosen.

A Simple Multistep Solution for Model Predictive Control in Multiphase Electric Drives

The multiphase electric drive field of research is rather young, and most of the efforts in this area have been focused on appropriately extending different techniques that were already popular in three-phase systems. This is the case of finite-control set model predictive control (FCS-MPC), which has been a hot topic since 2009. Despite the numerous contributions on FCS-MPC for multiphase systems, all of them assume a single-step horizon in the predictions. This paper shows the first attempt to include multistep predictions within the FCS-MPC that performs the current regulation. The extension from the three-phase case is not direct at all because the number of voltage vectors and subspaces is higher, this adding further complexity and eventually leading to issues related to the secondary current regulation. A smart selection of different subsets of voltage vectors is used to ease the implementation, and a modified cost function is developed to jeopardize the high instantaneous voltage values that significantly worsens the current quality at steady state. Experimental results confirm that the use of multistep FCS-MPC is feasible in multiphase electric drives, showing at the same time that some measures must be taken to minimize the distortion in the low-impedance secondary subspaces.

An Optimized Model Predictive Control Method With Fixed Switching Frequency for Eliminating Common-Mode Voltage of Five-Level Converters

In this paper, an optimized model predictive control (MPC) method with fixed switching frequency (FSF) is proposed to eliminate the common-mode voltage (CMV) and balance the capacitor voltage. Firstly, the zero CMV vector diagram is divided by finite control set MPC (FCS-MPC) to obtain the candidate vectors effectively. Then, in order to control the capacitor voltage and avoid the great computational burden originated from the redundant switching combinations, a piecewise control scheme is proposed to determine the required ones directly. The sequence generated by the selected vectors is further optimized for the purpose of saving switching transitions according to the internal and external parts of the vector diagram. Finally, the duty ratios are evaluated based on the results of the FCS-MPC in dividing sectors so as to avoid any extra complex calculation. The experimental evaluation has been carried out to validate the feasibility and effectiveness of the proposed method.

Adaptive Virtual Impedance Control with MPC’s Cost Function for DG Inverters in a Microgrid with Mismatched Feeder Impedances for Future Energy Communities

More and more distributed generations (DGs), such as wind, PV or battery bank sources, are connected to electric systems or customer loads. However, the locations of these DGs are based on the highest energy that can be potentially harvested for electric power generation. Therefore, these locations create different line impedances based on the distance from the DGs to the loads or the point of common coupling (PCC). This paper presents an adaptive virtual impedance (AVI) in the predictive control scheme in order to ensure power sharing accuracy and voltage stability at the PCC in a microgrid network. The reference voltage from mismatched feeder impedances was modified by utilizing the suggested AVI-based predictive control for creating equal power sharing between the DGs in order to avoid overburdening any individual DG with low-rated power. The AVI strategy used droop control as the input control for generating equal power sharing, while the AVI output was used as the reference voltage for the finite control set–model predictive control (FCS-MPC) for creating a minimum voltage error deviation for the cost function (CF) for the inverter’s vector switching pattern in order to improve voltage stability at the PCC. The proposed AVI-based controller was tested using two DG inverter circuits in a decentralized control mode with different values of line impedance and rated power. The performance of the suggested controller was compared via MATLAB/Simulink with that of a controller based on static virtual impedance (SVI) in terms of efficiency of power sharing and voltage stability at the PCC. From the results, it was found that (1) the voltage transient magnitude for the AVI-based controller was reduced within less than 0.02 s, and the voltage at the PCC was maintained with about 0.9% error which is the least as compared with those for the SVI-based controller and (2) equal power sharing between the DGs increased during the change in the load demand when using the AVI-based controller as compared with using the SVI-based controller. The proposed controller was capable of giving more accurate power sharing between the DGs, as well as maintaining the voltage at the PCC, which makes it suitable for the power generation of consumer loads based on DG locations for future energy communities.