Finite Set Model Predictive Control Research Articles

An Ensemble Regulation Principle for Multiobjective Finite-Control-Set Model-Predictive Control of Induction Machine Drives

Finite-control-set model-predictive control (FCS-MPC) has been widely investigated in the electrical drive systems, thanks to its merits of intuitive concept, straightforward implementation, and fast transient response. Owing to the flexible inclusion of constraints, a combination of weighting parameters is derived in the objective function to balance the relationship between the control targets. However, it is a challenging and time-consuming task to optimize a series of weighting parameters. To cope with this issue, this article proposes an FCS-MPC scheme with an ensemble regulation principle for the removal of all the weighting parameters. On the basis of the dimension reduction of the optimization problem, the ensemble regulation principle initially selects the suboptimal solutions for all the control targets. The optimal solution is determined according to a high consistency with the suboptimal solutions via an adaptive mechanism, which not only achieves a decent performance but also avoids a worst case for all the control criteria. The experimental implementation is conducted on a 2.2-kW induction machine platform, which verifies that the proposed scheme outperforms a group of existing weighting factorless FCS-MPC schemes at both the steady state and the transient state.

Low-Complexity Dual-Vector Model Predictive Control for Single-Phase Nine-Level ANPC-Based Converter

This article proposes a dual-vector finite-control-set model predictive control (FCS-MPC) with reduced complexity for a novel nine-level active neutral point clamped (ANPC) converter. This topology considerably reduces the used number of power switches compared to other topologies. Only nine power switches and two flying capacitors (FCs) are used to generate nine voltage levels. The proposed MPC scheme notably reduces the computational burden by directly locating the best two vectors without the need for multiple evaluations of the cost function as in the conventional method. Using one weighting factor in the cost function, three objectives are considered, namely, current tracking, FCs voltage control, and dc-link stabilization, reducing the heavy effort of coordinating weighting factors. Mathematical analyzes were carried out to determine the optimal duration of the selected voltage vectors. While the sequence of the two voltage vectors is identified based on the total harmonic distortion (THD) definition to minimize its value. Compared with standard FCS-MPC, lower steady-state errors, lower THDs, better harmonic distribution, and shorter execution times are achieved. The proposed MPC method is validated and compared with other prior-art control methods through experimental implementation.

A Virtual Voltage Field-Weakening Scheme of Trajectory Correction for PMSM Model Predictive Control

The finite-control-set model predictive control (FCS-MPC) is an effective control method for permanent magnet synchronous motor (PMSM) drives with multiple constraints. However, it suffers from the trajectory deviation in the field-weakening region due to the model mismatch. In addition, the inherent discreteness of FCS-MPC further hinders the continuous prescribed trajectory in the field-weakening region. To address this problem, a field-weakening scheme with virtual voltage is studied to realize trajectory correction through the feedback mechanism. The concept of virtual voltage is proposed and constructed with continuous, excess, and perturbed components. The three components are extracted from the model characteristics, the predicted current error, and the disturbing current, which provide continuity, field-weakening trigger, and parametric immunity for the virtual voltage, respectively. Comparison experiments are conducted on the FPGA+ARM-based PMSM test rig to validate the dynamic performance and trajectory deviation immunity in the field-weakening region.

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.

An Improved Horizon-1 Finite-Control-Set Model-Predictive Current Control Design for an Induction Machine With Integrated Anti-Windup

In this letter, we propose a novel approach to integrating disturbance rejection within a finite-control-set model-predictive-control (FCS-MPC) framework for current/torque control of an induction machine (IM). The technique derived utilizes disturbance estimation, but, by utilizing an input transformation renders the compensation via the constraint set leaving the cost function in a simple form. The method effectively leads to a resonant mode in the controller with an inherent anti-windup mechanism that can be exploited to prevent excessive excitation due to the persistent enforcement of control constraints.

A Novel Deep Reinforcement Learning-Based Current Control Method for Direct Matrix Converters

This paper presents the first approach to a current control problem for the direct matrix converter (DMC), which makes use of the deep reinforcement learning algorithm. The main objective of this paper is to solve the real-time capability issues of traditional control schemes (e.g., finite-set model predictive control) while maintaining feasible control performance. Firstly, a deep Q-network (DQN) algorithm is utilized to train an agent, which learns the optimal control policy through interaction with the DMC system without any plant-specific knowledge. Next, the trained agent is used to make computationally efficient online control decisions since the optimization process has been carried out in the training phase in advance. The novelty of this paper lies in presenting the first proof of concept by means of controlling the load phase currents of the DMC via the DQN algorithm to deal with the excessive computational burden. Finally, simulation and experimental results are given to demonstrate the effectiveness and feasibility of the proposed methodology for DMCs.

Impact of Sequential Model Predictive Control on Induction Motor Performance: Comparison of Converter Topologies

Finite Set Model Predictive Control (FS-MPC) is a widely used technique in power electronic converter applications. One challenge in FS-MPC implementation is selecting appropriate weighting factors, as there is currently no established methodology for finding the best values. An alternative approach is to consider cost functions without weighting factors, as used by the Sequential Model Predictive Control (SMPC). In this paper, the performance of SMPC applied to induction motors is analyzed. The SMPC strategy involves sequentially evaluating simple cost functions by considering a limited number of available switching states for the power electronic converter. This number is the control parameter of the SMPC. The parameter’s domains and a selection criteria based on THD were established in this investigation. The power converter topologies studied include the Voltage Source Inverter (VSI) and the Neutral Point Clamped three-level (3L-NPC). Simulations performed in PLECS software and Hardware-in-the-Loop (HIL) tests using an RT Box for valid parameters satisfy the characteristics of the classical predictive control, such as good control variables tracking and high dynamic response. For a VSI converter, increasing the control parameter results in reduced harmonic distortion, while for an NPC converter, optimal results are achieved with control parameter values within a specific range.

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.

Data-Driven Finite Control-Set Model Predictive Control for Modular Multilevel Converter

This article investigates a data-driven-based predictive current control (DD-PCC) approach for a modular multilevel converter (MMC) to circumvent the sensitiveness to parameter variation and unmodeled dynamics of a finite control-set model predictive control (FCS-MPC) method. By integrating a model-free adaptive control (MFAC)-based data-driven solution into the FCS-MPC framework, the performance deterioration caused by model uncertainties is suppressed. The design of the suggested controller is only based on input–output measurement data, where neither the parameter information nor the knowledge of detailed MMC models is required, leading to improved robustness against parameter drifts and model uncertainness. Moreover, a simplified cost function formula that takes into account output current tracking and circulating current regulation is constructed to efficiently determine the optimal insertion index of each arm. Finally, simulation and experimental results are obtained to verify the steady-state, dynamics, and robustness performance of the proposed approach.

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.

Finite-time ESO-based cascade-free FCS-MPC for NNPC converter

This article addresses a novel finite control-set model predictive control solution for nested neutral point-clamped converter. More specifically, it is formed by incorporating a finite-time extended state observer design into a cascade-free finite control-set model predictive control architecture. In this architecture, a finite-time extended state observer is developed to estimate the total disturbances, and an integral error term and a dynamic reference design point of view are embedded into the proposed design so as to improve the performance. The main features of this proposal are: first of all, the detailed knowledge of the physical structure of the controlled plant is not required; second, a cascade-free structure is achieved in finite control-set model predictive control solution without weighting factors and steady-state error; and third, the proposed methodology can operate with different systems not just power converters, while keeping the fast transient response. In contrast to existing finite control-set model predictive control approaches, it can be found that our modification not only can track fast to their references but also can have superior robustness performance. Finally, the proposed solution is verified by comprehensive results that prove the theoretical researches for nested neutral point-clamped converter, while meeting the harmonic standards.

Fixed-Switching-Frequency Modulated Model Predictive Control for Islanded AC Microgrid Applications

In this paper, a fixed-switching-frequency modulated model predictive control (M2PC) is established for a two-level three-phase voltage source inverter (VSI) working in an islanded AC microgrid. These small-scale power systems are composed by two or more VSIs which interface DGs, controlling the voltage amplitude and frequency in the system, and simultaneously sharing the load active and reactive power. Generally, these operational characteristics are achieved using hierarchical linear control loops, but with challenging limitations such as slow transient reaction to disturbances and high proneness to be affected by parameter modifications. Model predictive control may solve these issues. Nevertheless, the most used and developed predictive control scheme, the finite-set model predictive control (FS-MPC), presents the drawback of having the harmonic spectrum spread over all the frequencies. This brings issues with coupling between the different hierarchical control levels of the whole microgrid system, and eventually, when designing the filters for main-grid connection. This paper aims to solve these issues by developing the fixed-switching-frequency M2PC working with higher-level control loops for operation in an islanded AC microgrid. These advantages are proved in an AC microgrid configuration where methodology for paralleling multiple M2PC-regulated VSIs is described, with rapid transient response, inherent stability, and fully decentralised operation of individual VSIs, achieving proper load power sharing, eliminating circular currents, and proper waveforms for output currents and capacitor voltages. All these achievements have been confirmed via simulation and experimental verification.

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 general infrastructure for data-driven control design and implementation in tokamaks

A general infrastructure for tokamak controllers based on data-driven neural net models is presented. The paradigm allows for more flexible choices of both the underlying model and the desired controlled variables and targets. The system is implemented and tested on the DIII-D tokamak, enacting simultaneous pressure and temperature control via a finite-set model-predictive controller. Traditional control methods such as proportional–integral–derivative (PID) have proven effective for decoupled control tasks, but scale poorly when trying to achieve more complicated goals such as full state control. This is exactly where model-based controllers succeed.

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

Model predictive control based virtual synchronous generator for parallel-connected three-phase split-source converters in islanded AC Microgrids

Due to high penetration of renewable generation in power systems, and the need to provide the interface between distributed energy resources, the split-source inverter (SSI) provides both the boosting and the conversion capabilities in one single-stage. Also the need for converter-based artificial inertia has become more important. In this paper a model-predictive control (MPC) based on virtual synchronous generator (VSG) algorithm for a parallel-connected three-phase SSI is proposed for conceiving regulation of local voltage and realizing power-sharing of an islanded AC microgrid (MG). A virtual synchronous generator (VSG) is deployed to ensure active-power-sharing and provide inertia-emulation and hence reducing the rate of change of frequency (RoCoF) that results from sudden load change. To accomplish a simple control construction, quick dynamic performance, high stability, and enhanced current limitation, a finite-set MPC (FS-MPC) is used. The analysis and modeling of the proposed technique are presented in detail. A simulation model is used to investigate the proposed system performance.

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.

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.

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.