Finite Control Set Research Articles

Theoretical and Experimental Comparative Analysis of Finite Control Set Model Predictive Control Strategies

This research paper studies and highlights the features of the most popular finite control set model predictive control (FCS-MPC) strategies available in the state of the art, which are the optimal switching vector (OSV-MPC), modulated model predictive control (M2PC), and optimal switching sequence (OSS-MPC) methods. Thus, these strategies are studied experimentally by analyzing the transient and steady state performance using a grid tie conventional three-phase two-level voltage source inverter (VSI) with inductive output filter in a Typhoon HIL real-time simulator (RTS) with a Texas Instruments F28379D digital signal processor (DSP). Hence, quantitative indicators, such as the maximum tracking error, the mean absolute error, the settling time, the total harmonic distortion, the switching frequency spectrum, the switching pattern, and the computational burden are compared with the aim to deduce the best strategy for each criteria.

An FCS-MPC Strategy for Series APF Based on Deadbeat Direct Compensation

Aiming at the delay caused by the phase-locked loop (PLL) and harmonic detection in the traditional control of a series active power filter (SAPF), a deadbeat direct control (DBDC)-based finite control set model predictive control (FCS-MPC) strategy for the SAPF is proposed in this work. Firstly, a reference voltage generation mechanism based on direct control is established, which avoids the delay in harmonic detection, thus improving the dynamic response performance of the system. Secondly, the reference current generation mechanism suitable for the SAPF is derived and established using the deadbeat control (DBC) in the αβ coordinate system, based on which the FCS-MPC system is constructed. It eliminates the complex coordinate transformation and PLL and effectively compensates for the full-frequency harmonic voltage. Finally, the proposed control strategy is verified by a simulation and an experiment. The results suggest that the proposed control strategy can effectively compensate for the load voltage and suppress harmonic distortion for the temporary swell and sag of the grid voltage, sudden load changes, and harmonic distortion conditions.

Finite Control Set Model Predictive Torque Control With Reduced Computation Burden for PMSM Based on Discrete Space Vector Modulation

This paper proposes a finite control set model predictive torque control (FCS-MPTC) with reduced computation burden for permanent magnet synchronous motor (PMSM). Based on discrete space vector modulation (DSVM), the proposed DSVM-MPTC extends the control set. Therefore, compared to the traditional FCS-MPTC, it greatly reduces the torque ripples and current harmonics while maintaining a constant switching frequency. Furthermore, a new voltage vector optimization strategy is employed to decrease the number of candidate voltage vectors from 38 to 13, the proposed DSVM-MPTC significantly reduces the computation burden without compromising the control performance. The validity of the proposed DSVM-MPTC is verified by comparative experiments.

Direct Speed Control Based on Finite Control Set Model Predictive Control With Voltage Smoother

A direct speed control strategy based on finite control set model predictive control (FCS–MPC) with a voltage smoother is presented herein. In the proposed concept, a finite set of smoothed voltage vectors characterized by an adjustable amplitude and a movable origin is introduced as voltage candidates in the FCS–MPC scheme. The controller predicts the future current and speed and outputs the optimal smoothed voltage using pulse-width modulation. Owing to this control scheme, an abrupt change in the output voltage, which causes a large current ripple, is avoided without additional computational costs. Simulation and experimental results obtained using a permanent magnet synchronous motor fed by a two-level three-phase inverter show that the proposed method effectively reduces the current ripple while achieving a high dynamic drive compared with the conventional FCS–MPC scheme.

Improved Model Predictive Direct Power Control for Parallel Distributed Generation in Grid-Tied Microgrids

This research proposes an improved finite control set direct power model predictive control method (FCS-DPMPC) for grid-tie distributed generation (DG). FCS-DPMPC predicts the system outcomes using the system model. During the next sampling time, a voltage vector is defined using the cost function to minimize the power ripple, consequently allowing flexibility for power regulation. Furthermore, the impact of implementing a one-step delay is studied and compensated through a model forecast pattern. In addition, a new two-step horizon technique has been developed to minimize switching frequency and computation burden. Simulation results for single DG and parallel operated DGs in a grid-tie manner confirm the effectiveness of the suggested control strategy, which signifies that this is an appropriate approach for distributed generation in microgrids.

A Novel Modulation Method Based on Model Prediction Control With Significantly Reduced Switching Loss and Current Zero-Crossing Distortion for Vienna Rectifier

Finite control set model predictive control (FCS-MPC) is widely used in rectifiers due to its fast response. When there are multiple control objectives, the optimal switching sequence is selected by minimizing the cost function with weighting factors. However, the reduction of current ripple caused by output error and switching loss is in conflict to a certain extent for FCS-MPC. In addition, when multiple objectives are controlled, the selection of weighting factor is a serious challenge. Therefore, a modulation method based on continuous control set model predictive control is proposed in this article. Two phases are clamped and one phase is modulated, which not only can significantly reduce the switching loss without increasing the computational burden, but also can control the neutral point voltage without using weighting factors. In addition, when the proposed method is adopted, current zero-crossing distortion can be suppressed over the full range of modulation index. The effectiveness of the proposed method is verified by simulation and experimental results.

Three-Level Inverter-PMSM Model Predictive Current Control Based on the Extended Control Set

In the neutral point clamped (NPC) three-level inverter-permanent magnet synchronous motor system, traditional model predictive current control (MPCC) uses the system predictive model to traverse the 27 basic voltage vectors, to achieve the d-q axis current component and neutral point voltage of the multi-objective optimal control. Finite control set model predictive control predicts the state change of the control target at future moments based on a finite number of switching states of the inverter. The control principle of this method is simple and easy to implement, but the control effectiveness of this control strategy is limited because only one basic vector can be selected as the optimum output per control period. In this paper, a model predictive current control strategy based on an extended control set (ECS-MPCC) is proposed, which can improve the control performance of the system by extending the control set to select multiple vectors in a single control period compared to the traditional strategy. In addition, to address the disadvantage of extending virtual space vectors leading to an increase in computation, this paper proposes a fast search method for optimal vector based on region reduction. The proposed method avoids the optimization process traversing all virtual space vectors, thus enabling a fast search for the optimal vector. The experimental results show that the proposed control strategy has good steady-state and dynamic performance.

A finite control set model predictive controller for single-phase transformerless T-type dynamic voltage restorer

A finite control set model predictive controller for single-phase transformerless T-type dynamic voltage restorer

Full prediction cascade control of permanent magnet toroidal motor with time-varying parameters

This paper presents the modeling and predictive control of permanent magnet toroidal motor (PMTM). Based on the spatial structure characteristics and working principle of toroidal motor, the time-varying inductance parameters are derived. The nonlinear mathematical model of toroidal motor is established in rotating coordinate system. The output fluctuation of the toroidal motor is analyzed. To improve the output performance of toroidal motor, the full predictive cascade speed and current control (F-MPC) strategy for toroidal motor is proposed. For F-MPC of toroidal motor, the finite control set model predictive current control (FCS-MPCC) strategy with delay compensation is adopted in current inner loop, and the deadbeat model predictive speed control (DB-MPSC) strategy is adopted in speed outer loop. Meanwhile, to achieve DB-MPSC, the reduced-order Luenberger observer is designed to obtain load torque in real time. F-MPC is the combination of current-speed prediction and Luenberger observer; it can overcome the limitation of excessive parameter adjustment of traditional cascade PI (Proportional Integral) controller. The simulation results show that compared with FOC (Field Oriented Control) and FCS-MPCC, F-MPC can improve the dynamic response and anti-interference performance of toroidal motor, and it can also reduce the fluctuation of output speed and torque significantly.

Finite Control Set Model-Free Predictive Current Control of PMSM With Two Voltage Vectors Based on Ultralocal Model

Model-free predictive current control (MFPCC) that only needs the input and output data of the system can eliminate the influence of the motor parameters mismatch. In the conventional finite control set MFPCC (FCS-MFPCC), the delay and distortion that exist in the current variations update can affect the future current prediction and the current tracking performance. This article studies an improved current variations updating mechanism to improve the update frequency. Based on the ultralocal model, the current variations corresponding to the different voltage vectors can be updated at every control period. Then, the stator currents can be predicted with the accurate current variations, and the optimal voltage vector can be selected by minimizing the cost function without using motor parameters. Besides, a sliding mode observer is designed to observe the ultralocal model's parameters fast. Furthermore, the two voltage vectors and the optimal duration are introduced to improve the current tracking performance. Meanwhile, in order to guarantee the accuracy of the future current prediction, the current compensation strategy is studied to revise the current estimation. Finally, the effectiveness of the proposed FCS-MFPCC strategy is verified through the simulation and experimental results.

An Improved Parallel Predictive Torque Control for Permanent Magnet Synchronous Motor

A predictive torque control strategy with parallel structure (P-PTC) was recently proposed to overcome the tedious tuning of weighting factor in the conventional predictive torque control (C-PTC). However, P-PTC still has some problems, such as the introduction of other parameters to be tuned and the lack of theoretical support, et al. To solve the above problems, an improved P-PTC without any extra parameters tuning (IP-PTC) is proposed for permanent magnet synchronous motor (PMSM). Firstly, two parallel cost functions are respectively constructed to select three voltage vectors that have the best control effect on electromagnetic torque and stator flux to form two new finite control sets (FCSs). Secondly, three different cases are divided according to the intersection number of these two FSCs. Finally, the corresponding optimal voltage vector selection scheme is proposed for these three cases. C-PTC, P-PTC and the proposed IP-PTC are studied through theory and experiments. The results show that when compared with C-PTC and P-PTC, the proposed IP-PTC has neither weighting factor nor any extra parameters, and it is well supported by theory. Furthermore, the proposed IP-PTC has the best static and dynamic performance among the three.

Data Driven Model Predictive Control for Modular Multilevel Converters With Reduced Computational Complexity

Model predictive control (MPC) has become increasingly popular among researchers for modular multilevel converters (MMCs) due to its ability to incorporate multiobjective control and provide superior dynamic response. However, it is computationally challenging to implement it on MMCs when the number of submodules is increased. This paper proposes a finite control set (FCS) model predictive control (MPC) with reduced computational complexity for modular multilevel converters (MMCs). To accomplish this goal, a reduced order data-driven model is obtained using sparse identification of nonlinear systems (SINDy) by incorporating the input terms in the load current and circulating current dynamics. As a result, the need to use the arm voltages or the submodule capacitor voltages dynamic equations as in the case of a conventional FCS-MPC is eliminated. To improve the output current total harmonic distortion (THD) and reduce the effect of higher switching frequencies caused by the FCS-MPC, an updated cost function is proposed. The effectiveness of the proposed technique is validated by simulation and experimental results.

Application of Finite Control Set–Model Predictive Control for Servo Brake Motion in PMSM Drives

Finite control set-model predictive control (FCS–MPC) has a simple and intuitive optimization procedure. Therefore, FCS–MPC is increasingly being applied to control strategies for electrical drive systems. This article presents a method for servo brake control of a permanent magnet synchronous motor (PMSM) based on FCS–MPC. Accordingly, we propose a reference trajectory introduced in a cost function for brake motions and an alternating procedure with speed control. Moreover, this article clarifies the problem peculiar to servo-brake control using FCS–MPC, i.e., the reduction in tracking performance near the brake position because of the low resolution of the output voltage. In addition to the conventional method, a finite number of smoothed voltages were applied as candidate voltages for FCS–MPC to improve the tracking performance near the brake position. The smoothed voltages can effectively increase the resolution of the output voltage, which results in fewer steady-state errors in angular position tracking during servo brake motion. The simulation and experimental results obtained using a PMSM drive system reveal that the proposed strategy exhibited high performance in tracking the reference target during the operation of servobrakes and the ability to seamlessly alternate between servo brake and motor operations.

Neutral Point Voltage Balancing Using MOPSO based Weighting Factor Tuning for FCS-MPTC of Three Level T-Type VSI Fed IM Drive

This paper proposes designing and implementing an offline optimization of weighting factors associated with the Finite Control Set Model Predictive Torque Control (FCS-MPTC) for a T-Type Multilevel Inverter (MLI) fed three-phase induction motor drive. Since the IM drive driven by T-Type MLI requires neutral point voltage balancing for reliable and acceptable performance, a regularization term with a weighting factor is added to the cost function of FCS-MPTC involving the neutral point voltage balancing. The optimized weighting factors were obtained by applying the Vikor decision-making method on the Pareto front obtained from the Multi Objective Particle Swam Optimization (MOPSO) algorithm. The improvement is shown by comparing the results of the proposed method with the conventional FCS-MPTC applied to the same IM drive. The proposed optimization methodology also showed better dynamic performance, particularly during loading. The results from MATLAB/Simulink environment show significantly improved flux ripple and almost perfect neutral point voltage balancing with the proposed optimized version.

Robust Finite Control Set Model Predictive Current Control for Induction Motor Using Deadbeat Approach in Stationary Frame

The model predictive control increased its prominence in the field of induction machine drives. However, the performance of this strategy depends on the accuracy of the machine model parameters. In order to overcome this deficiency, this paper proposes a robust model predictive current control employing indirect field-oriented control in stationary reference frame using the stator current and the rotor flux vectors as state and stator voltage vector as the input. The control algorithm combines the classical model predictive control with the deadbeat approach in order to calculate the applied stator voltage vector in two components: one element considers the stator current reference, and another employs the disturbances caused due to the parameter errors, which allows to compensate the parameter mismatches in the plant model. The minimized cost function employs the predicted stator voltage vector to select the voltage vector to be applied to the stator terminals of the motor. The control method performance was verified using an experimental test bench analyzing the system steady-state and dynamic actions. In this way, the results corroborate the proposed controller robustness against parametric variations.

Research on Mixed Logic Dynamic Modeling and Finite Control Set Model Predictive Control of Multi-Inverter Parallel System

Parallel connection of multiple inverters is an important means to solve the expansion, reserve and protection of distributed power generation, such as photovoltaics. In view of the shortcomings of traditional droop control methods such as weak anti-interference ability, low tracking accuracy of inverter output voltage and serious circulation phenomenon, a finite control set model predictive control (FCS-MPC) strategy of microgrid multi-inverter parallel system based on Mixed Logical Dynamical (MLD) modeling is proposed. Firstly, the MLD modeling method is introduced logical variables, combining discrete events and continuous events to form an overall differential equation, which makes the modeling more accurate. Then a predictive controller is designed based on the model, and constraints are added to the objective function, which can not only solve the real-time changes of the control system by online optimization, but also effectively obtain a higher tracking accuracy of the inverter output voltage and lower total harmonic distortion rate (Total Harmonics Distortion, THD); and suppress the circulating current between the inverters, to obtain a good dynamic response. Finally, the simulation is carried out on MATLAB/Simulink to verify the correctness of the model and the rationality of the proposed strategy. This paper aims to provide guidance for the design and optimal control of multi-inverter parallel systems.

Research on Control Strategy of Solid State Transformer Based on Improved MPC Method

As the core equipment of smart grid, solid-state transformer (SST) needs to have good power quality regulation capability under grid side faults and load side faults. To ensure that the high-voltage side converter works at unity power factor and smooth DC voltage. At the same time, the three-phase output voltages need to have high steady-state accuracy and strong anti-interference capability. In this paper, an improved model predictive control scheme was proposed. First, a theoretical modeling analysis of the high and low voltage side of a solid-state transformer was performed. Then, power and voltage prediction models are developed for the high-voltage side converter and low-voltage side converter of the SST, respectively. Three voltage vectors are selected for prediction by the cost function, and the equivalent voltage vector in the two-phase stationary coordinate system is synthesized and modulated to control the converter. Finally, simulation was conducted to compare and analyze various operating conditions of SST under different control schemes. The control effect of solid-state transformer under improved model predictive control is better than traditional finite control set model predictive control and proportional integral control, which can better regulate power quality.

Study on Single-Loop FCS-MPC for DC-Based DFIG System

Doubly fed induction generator (DFIG) system suffers from complex control structure, slow dynamic response speed and cumbersome parameter design due to its traditional cascaded dual-loop control strategy. A single-loop finite control set model predictive control (SLMPC) is proposed in the paper for DC-based DFIG in DC grid, which simplifies its control structure and parameter design, and enhances system dynamic response. There are three improved aspects. First, the single-loop control structure is proposed to eliminate intermediate link in dual-loop cascaded structure, and enhance system dynamic response. Second, system reduced-order discretization algorithm is proposed by differential and integral discretization method to reduce finite control set model predictive control strategy (FCS-MPC) design difficulty. Third, cost function with nonlinear additional current limiting function is designed to protect system from overcurrent effectively. Finally, the feasibility of proposed strategy is verified by simulations and experiments.

Long Prediction Horizon FCS-MPC for Power Converters and Drives

Finite control set model predictive control (FCS-MPC) is a salient control method for power conversion systems that has recently enjoyed remarkable popularity. Several studies highlight the performance benefits that long prediction horizons achieve in terms of closed-loop stability, harmonic distortions, and switching losses. However, the practical implementation is not straightforward due to its inherently high computational burden. To overcome this obstacle, the control problem can be formulated as an integer least-squares optimization problem, which is equivalent to the closest point search or closest vector problem in lattices. Different techniques have been proposed in the literature to solve it, with the sphere decoding algorithm (SDA) standing out as the most popular choice to address the long prediction horizon FCS-MPC. However, the state of the art in this field offers solutions beyond the conventional SDA that will be described in this article alongside future trends and challenges in the topic.

Neural Network Based Predictive Current Controllers for Three Phase Inverter

With Predictive Current Controllers, system behavior or current reference is predicted. In this way, it is aimed to prevent hardware and software related delays. In this study, new Artificial Neural Network (ANN) based Predictive Current Controllers are designed using four different methods for voltage source inverters. The training of the networks is done offline using the data obtained from the simulation results for different parameters in the Matlab environment. In the first proposed method, a static ANN based current controller is designed and trained using the data obtained from the Finite Control Set Model Predictive Control (FCS-MPC) method. Then, a feed-forward reference current predictor ANN (PRefNN) is designed to make sinusoidal reference current prediction in the other three methods. The other proposed predictive ANN methods are trained by taking the data offline from the inverter system in which PRefNN and the classical current controllers (Hysteresis, PR, and PI) are used. In this way, three different predictive current controllers named as Hysteresis based predictive ANN (Hist-PNN), PR based predictive ANN (PR-PNN), and PI based predictive ANN (PI-PNN) are designed. Classical current control methods have been given predictive properties with these three different network structures. And also, it is improved the performance of classical methods against parameter changes and noises. A three phase 5kVA inverter circuit with a 7MBP50RJ120 IPM module in the power stage and STM32f407 as a controller is designed for the experimental setup. The methods are tested in simulation and validated in the experimental setup.