Finite Set Model Predictive Control Research Articles

Predictive Control Scheme With Adaptive Overmodulation for a Five-Leg VSI Driving Dual PMSMs

A dual permanent magnet synchronous motors (PMSMs) system, driven by a five-leg voltage source inverter (VSI), is considered as a feasible option for low-cost and fault tolerance application. The traditional finite-control-set model predictive control (FCS-MPC) schemes without modulator generate heavy computational burden and larger current ripple. In this article, an improved predictive control method with space vector modulation (SVM) scheme is proposed for independent control of dual-motor system. Firstly, the expected voltage vectors (VVs) of dual motors are obtained with deadbeat (DB) solution, which yields a faster dynamic response. Then, in order to reduce current ripple and eliminate candidate voltage vector traversal process, an improved modulator with adaptive overmodulation is adopted to synthesize the expected VVs. The presented SVM method solves the problem caused by the conflicting switching-states demands of common leg from dual PMSMs. Moreover, an extensive comparison is performed among the proposed control method, the existing model-based control methods, and the field-oriented control (FOC) method with proportional-integral (PI) controller. All methods are experimentally carried out in steady state and transient state. The test results indicate that the proposed control scheme with adaptive overmodulation can effectively reduce current ripple, improve voltage utilization, and preserve good dynamic performance.

Improved Model Predictive Control With Reduced DC-Link Capacitor RMS Current for Back-to-Back Converter-Fed PMSM Drives

A low root mean square (RMS) current flowing through the intermediate DC-link capacitor is always desired in the back-to-back (BTB) voltage source converters (VSCs)-fed motor drive, such that the designed capacitor lifespan can be extended or a lower DC-link size could be the alternative with improved power density. The conventional modulator-based control involves the inherent carrier-based switching operation, and hence the dominant harmonics at carrier frequency multiplier and carrier sideband are introduced into the capacitor current. Therefore, the finite-control-set model predictive control (FCS-MPC) is proposed to optimize the capacitor RMS current. The direct switching pulse generation without using the carrier can give a distinctive capacitor current spectrum and more possible current pulse cancellation between the BTB VSCs can be conveniently achieved through the cost function. Besides, the acceptable grid-current quality and the motor performance are maintained by using a tunable weighting factor. The simulated and experimental results highlight the effectiveness and benefits of the proposed method in terms of 20%-35% capacitor RMS current reduction compared to the synchronous carrier-based space vector pulse-width modulation (SVPWM) scheme and also significant improvements over the distributed predictive manner.

An Impact of Switching Frequency and Model Accuracy on Model Predictive Current Control Performance for Reluctance Synchronous Motors

Abstract The present paper investigates the feasibility of utilizing the simplified prediction model for finite control set model predictive current control (FCS-MPCC) applied to reluctance synchronous motors (RSMs). The FCS-MPCC exhibits torque and current ripples, and a crucial consideration is the reduction of these ripples by increasing the switching frequency. The algorithm’s computational complexity is tied to the accuracy of the adopted model. Two approaches with varying levels of accuracy in predicting current dependencies concerning changes in the inductance of the RSM are investigated. The findings highlight the potential of employing simplified fixed inductance values for efficient control in drive systems, particularly those amenable to high switching frequencies.

A Low-Complexity Double Vector Model Predictive Current Control for Permanent Magnet Synchronous Motors

Compared to the conventional finite control set model predictive control (FCS-MPC), the double vector model predictive current control (DVMPCC) for permanent magnet synchronous motors (PMSMs) has a better steady-state performance without significantly increasing the switching frequency. However, determining optimal vectors with their dwell times requires a high computational burden. A low-complexity DVMPCC in the steady state was proposed in this study to address this problem. Firstly, the operating state of the motor was judged according to the speed error. During steady-state operation, the first optimal active vector was selected from three candidate vectors adjacent or identical to the active vector applied in the previous control period, reducing the number of comparisons by half. Next, the second optimal vector was selected from the other two active vectors, and the zero vector, the second optimal vector with the duty cycle, was determined according to the deadbeat condition of the q-axis current and cost function minimization. Finally, simulation and experimental results proved that the proposed low-complexity DVMPCC for surface-mounted permanent magnet synchronous motors is practical and feasible.

Predictive direct control strategy of unified power quality conditioner based on power angle control

The unified power quality conditioner (UPQC), based on power angle control (PAC), addresses the power distribution issue and enhances equipment utilization between two active power filters (APF) of UPQC by employing conventional linear control algorithms that have complex control structures and exhibit challenges in adjusting controller parameters and control delays. This study proposes a UPQC predictive direct control strategy based on power angle control (PDCS-PAC). We combine the direct control strategy with the finite control set model predictive control (FCS-MPC) to establish the UPQC finite set model predictive direct control system model in the dq coordinate system. This approach simplifies the controller structure and alleviates the control algorithm’s complexity. Based on a detailed analysis of the PAC mechanism, an instantaneous power angle determination method is proposed based on the reactive power equalization technique, and a predictive direct control strategy given the current generation mechanism is constructed for the series and shunt sides of the UPQC, considering the active power balance of the UPQC system and the principle of constant load fundamental voltage amplitude. Finally, we construct a simulation platform and a laboratory prototype to validate the feasibility and efficacy of the proposed control strategy.

Parameter Estimation and Predictive Speed Control of Chopper-Fed Brushed DC Motors

This paper presents an effective speed control method for brushed DC motors fed by a DC chopper using the concept of Finite Control Set-Model Predictive Control (FCS-MPC). As this control algorithm requires the parameters of the controlled object, the estimation of motor parameters is first performed by using two types of data. The first data includes the output speed response corresponding to the step input voltage to obtain the transfer function in the no-load regime. The second data consists of the motor speed and armature current when a load torque is applied to the motor shaft. The discrete-time equation of the motor armature circuit is used to obtain the future values of the armature circuit current and the motor speed. A cost function is defined based on the difference between the reference and predicted motor speed. The optimal switching states of the DC chopper are selected corresponding to the maximum value of the cost function. The performance of the proposed speed control algorithm is validated on an experimental system. The simulation and experimental results obtained show that the MPC controller can outperform the conventional proportional-integral (PI) controller.

Techno-enviro-socio-economic design and finite set model predictive current control of a grid-connected large-scale hybrid solar/wind energy system: A case study of Sokhna Industrial Zone, Egypt

This article offers a cohesive design optimization and control framework of a large-scale grid-connected battery and battery-less hybrid solar/wind system. Primarily, a techno-enviro-socio-economic design optimization and feasibility analysis were performed for eight distinct energy alternatives. Secondly, a finite-set model predictive current control (FS-MPCC) was proposed to improve both stability and dynamic behavior of the optimal energy alternative under various climatic circumstances. Moreover, finite-set model predictive control is furtherly employed to exploit the maximum permissible power from both solar and wind energies. The proposed framework is verified over a new case-study in Sokhna Industrial Zone in Suez, Egypt. The studied load demand combines both industrial and residential load sectors with a daily consumption of 40,500 kWh and of 4-MW peak. The design optimization results revealed that the grid-connected photovoltaic/wind turbines/converter system has superior sustainable and economic performances with the least net present cost ($17,371,980) and energy cost (0.0782 $/kWh). These values were less than those of the base-case system (grid-only) by 17 % and 22 %, respectively. Moreover, the reliability analysis found a tiny and tolerable daily capacity shortage of 5835-kWh despite the five annual grid outages. Besides, around 52 % of the total energy production was acquired from both solar and wind generation systems, which interpreted in a maximum saving in carbon dioxide emissions by 45.8 % compared to the base-case system and offered substantial social benefits in terms of creating nearly 10 jobs. The FS-MPCC results proved a reinforced dynamic behavior for the maximum power tracking profiles of solar and wind systems as well as in the DC-bus voltage profile regarding the over- and undershoot and the steady-state response. Compared to PI controller, the proposed FS-MPCC creates dq-axis voltages and currents with less disturbances and ripples which inject current/voltage to the grid with fewer harmonics and thus increases the generator's lifetime. To sum up, the proposed method features an integrative and compelling case to be used as an efficient tool to implement large-scale renewable energy projects. It could act as a blueprint for energy developers, researchers, investors, and authorities to achieve affordable and sustainable energy access.

Reinforcement Learning-Based Event-Triggered FCS-MPC for Power Converters

This article aims to first focus on an improvement of finite control-set model predictive control strategy for power converters that is based on reinforcement learning event-triggered predictive control architecture with the help of adaptive dynamic programming technique and event-triggered mechanism subject to system uncertainties. Our development, endowed with the merits of reinforcement learning and event-triggered control as well as predictive control solution, is able to alleviate the issues of parametric uncertainties and high switching frequency inherent in the existing scheme, while retaining the merits of the finite control-set model predictive control. Finally, this proposal is experimentally evaluated, where robust performance tests confirm the interest and applicability of the proposed control methodology.

Two-Phase Model Predictive Direct Duty-Cycle Control for PMSM With Off-Line Lookup Table

Finite control set model predictive control has poor steady-state performance due to limited voltage vectors. To improve the steady-state performance, multiple vectors are used to synthesize more accurate voltage vectors, but the selection of optimal vectors is always complex. In this paper, a two-phase model predictive direct duty-cycle control based on off-line lookup table (OLT) is constructed. The proposed prediction model converts three-phase duty cycles to two-phase duty cycles, which reduces the number of duty cycles calculations. In addition, to optimize the reference voltage vectors space, a new flux linkage deviation mechanism is proposed where the virtual reference vectors can be determined with low complexity by using OLT. Then, due to further improve the steady-state accuracy and ensure low complexity, a new flux linkage constraint is proposed, which restricts the flux linkage while reducing the torque ripple. More importantly, no weight factor is needed to balance the torque and stator flux. Finally, the process of the multi-stage and multi-duty-cycle calculation is converted into the process of a one-stage and small range single-variable enumeration selection, which significantly reduces calculation cost while improving the steady-state performance. The feasibility and effectiveness ofthe proposed method isverified bytheoryand experiments.

Indirect Matrix Converter Hardware-in-the-Loop Semi-Physical Simulation Based on Latency-Free Decoupling

In the process of hardware-in-the-loop simulations (HILs) of indirect matrix converters (IMCs), solving the mathematical models of complex multiswitching converter topologies has become a major problem. The conventional approach is to split the complex mathematical model into multiple serial subsystems; however, this inevitably produces delays in the simulation steps between different subsystems, leading to numerical oscillations. In this paper, the method of latency-free decoupling is adopted, which has no time-step delay between different subsystems, making each subsystem a parallel operation. This can improve the numerical stability of the simulations and can effectively reduce the step size of the real-time simulation and alleviate the problem of real-time simulation resource consumption. In this paper, we discuss in detail the modeling process of IMC hardware-in-the-loop simulations with Finite Control Set Model Predictive Control (FCS-MPC), and experimentally validate our method using the Speedgoat test platform, resulting in a simulation step size of less than 200 ns. The simulation results are compared with the results of Matlab’s Simpower power system, which allows us to evaluate the accuracy of our model.

FPGA-Based Extended Control Set Model Predictive Current Control with a Simplified Search Strategy for Permanent Magnet Synchronous Motor

The conventional finite control set model predictive current control (FCS-MPCC) suffers from suboptimal steady-state performance, primarily due to the limited selection of only eight basic voltage vectors in each control cycle. To overcome this limitation, the proposed extended control set MPCC (ECS-MPCC) utilizes an control set consisting of 818 selectable vectors, enabling a more refined voltage output and achieving a deadbeat response for current control by minimizing the cost function. To mitigate the computational burden resulting from the substantial increase in voltage vectors, a simplified search strategy is devised, which can be extended to other multi-objective cost functions. Remarkably, based on the inherent parallelism of the algorithm, the ECS-MPCC is implemented on an FPGA, further reducing the overall control time of the current loop to an impressive 0.61 μs. Through simulation and experimental tests on a laboratory PMSM driver, the effectiveness of the proposed ECS-MPCC strategy is validated. The experimental results demonstrate a significant reduction of 79% in the total harmonic distortion of phase currents compared to the conventional FCS-MPCC approach. This improvement underscores the superiority of the ECS-MPCC in enhancing the performance of PMSM drives, thereby illustrating its potential for practical implementation in real-world applications.

Finite Control Set Model Predictive Control (FCS-MPC) for Enhancing the Performance of a Single-Phase Inverter in a Renewable Energy System (RES)

A single-phase five-level T-type topology has been investigated in this article. This topology has emerged as a viable option for renewable energy systems (RES) due to its inherent benefits. The finite control set model predictive control (FCS-MPC) strategy has been implemented to this topology in order to improve the performance and overall reliability of the system. This control strategy empowers the inverter to predict future behavior based on a discrete set of control signals, enabling precise modulation and high-speed response to system dynamics. In the realm of RES, integration of FCS-MPC with multilevel inverters (MLIs) holds great potential to enhance energy conversion efficiency, grid integration, and overall system reliability. The article is structured to present an overview of the evolving landscape of power electronic systems, and the advantages of FCS-MPC. This paper provides a comprehensive analysis of the FCS-MPC control strategy applied to the single-phase five-level T-type topology. The study covers various aspects including the theoretical framework, hardware development, and experimental evaluation. It is obvious from the analysis that this inverter topology is reliable. Several redundant states make it fault-tolerant which helps in maintaining the output voltage at the same level even in the fault conditions. Additionally, the results show that the output load voltage is maintained at the same level irrespective of load change. Also, output load voltage has maintained the high-quality sinusoidal characteristics as the total harmonic distortion (THD) is very low. With all these features, this system is suitable within the framework of RES.

Design and FPGA-in-loop based validation of predictive hierarchical control for islanded AC microgrid

This paper proposes a decentralized control approach for the flexible operation of an autonomous AC microgrid (MG). AC MG typically consists of two or more voltage source inverters (VSI), capable of simultaneously regulating the voltage at the point of common coupling (PCC) and meeting the local power demand. The classical linear control techniques attain these functions. However, they have many limitations, such as slow transient response, high sensitivity to the parameter variations, and inability to handle the system nonlinearities. This paper aims to address these issues by presenting an improved finite control set model predictive control (FCS-MPC) approach for inverter-based distributed generation (DG). The proposed scheme tracks the voltage trajectory using cost function (CF) over two-step prediction horizons. The proposed control method is employed for the AC MG having two parallel DGs. Droop control is responsible for the power-sharing between the parallel DGs regardless of impedance mismatch at the distribution level of AC MG. The decentralized secondary control is developed to eliminate the deviation in the voltage and frequency caused due to overlooking the primary control. The proposed control scheme has been validated through extensive simulations and real-time controller hardware in the loop tests using an FPGA ZYBO Z7 board. Moreover, the proposed methodology depicts the enhanced transient response, less computational burden than the classic MPC, and shows robustness to parametric uncertainties in terms of THD than hierarchical linear control. The simulations and experimental results visually represent research outcomes, exhibiting the THD of 0.98 % for different dynamic loads, which is within the limit of IEEE and IEC standards. Furthermore, the proposed controller’s mathematical stability is further supported by analysis based on the Lyapunov stability theory.

Improved hybrid sphere decoding algorithm for long horizon finite control set model predictive control of grid-tied inverter

In this paper, the reference tracking and switching loss minimization problem of grid-tied inverters has been formulated as an integer least square (ILS) problem. It is considered an NP-hard and combinatorial problem due to the binary nature of switching status. A novel modification to the traditional non-recursive sphere decoding algorithm is being proposed in this research to address the implementation of long-horizon control with reduced computational burden. The proposed algorithm demonstrates a significant reduction in computation cost for long horizons resulting in energy minimization. The proposed method exploits the state-space model of the system to predict the sector in the space vector two-dimensional plane in which reference voltage could exist, thereby reducing the search space and time to search for the optimal solution. The proposed algorithm optimizes the utilization of controller resources compared to the conventional approach. The algorithm’s performance has been validated through simulations and Hardware in loop verification, for the performance optimization of a three-phase grid-tied inverter coupled using RL filter.

An Improved FCS-MPC Strategy for Low-Frequency Oscillation Stabilization of PV-Based Microgrids

With the rapid promotion of photovoltaic (PV) power generation, the local consumption of distributed PV power generation at medium and low voltage levels widely forms PV-based microgrids. The continuous increase in the penetration rate of uncertain PV power generation requires it to actively participate in the frequency and voltage regulation of weak microgrid systems. Finite control set-model predictive control (FCS-MPC) for voltage source converter (VSC) shows remarkable advantages in fast dynamic response and robustness compared with traditional cascaded linear control methods. However, the traditional FCS-MPC method for VSC without considering the DC-link voltage dynamic of the PV generator will face oscillations of power and frequency in PV-based microgrids. In this paper, to suppress the low-frequency oscillation issue caused by PV generations, an improved FCS-MPC (I-FCS-MPC) strategy considering the DC-link dynamics of PV generation is proposed, in which the AC side and DC side of the VSC are comprehensively considered to enhance the dynamic performance of the PV-based microgrids. Moreover, a DC-link voltage regulation mechanism is introduced, enabling the PV generators to operate in the maximum power point tracking (MPPT) mode. Finally, experiments are conducted to verify the effectiveness of the proposed control strategy comprehensively.

Optimized FCS-MPCC based on disturbance feedback rejection for IPMSMs under demagnetization fault in high-speed trains

This paper proposes an optimized finite control set model predictive current control (FCS-MPCC) strategy based on disturbance feedback rejection control (DFRC) method for interior permanent magnet synchronous motors (IPMSM) employed in high-speed trains. The strategy aims to mitigate the impact of parameter mismatch caused by permanent magnet demagnetization faults. Firstly, a discrete predicting model is established for IPMSMs, considering the mismatch in flux linkage, resistance, and inductance. The analysis is conducted to evaluate the impact of disturbances caused by demagnetization faults on the traditional FCS-MPCC. Secondly, second-order sliding mode disturbances observers (SMDOs) are utilized to detect real-time disturbances under demagnetization faults. The stability analysis of the second-order SMDOs is conducted using a Lyapunov function. Furthermore, disturbance controllers are developed to regulate the disturbances and generate compensating values for the FCS-MPCC. Lastly, experimental validation is performed on two IPMSMs to demonstrate the effectiveness of the proposed DFRC-based FCS-MPCC strategy against parameter mismatch issues caused by demagnetization faults.

Improving Power Quality in Grid-Connected Photovoltaic Systems: A Comparative Analysis of Model Predictive Control in Three-Level and Two-Level Inverters.

The Single-Stage Grid-Connected Solar Photovoltaic (SSGC-SPV) topology has recently gained significant attention, as it offers promising advantages in terms of reducing overall losses and installation costs. We provide a comprehensive overview of the system components, which include the photovoltaic generator, the inverter, the Incremental Conductance Maximum Power Point Tracking (IC-MPPT) algorithm, and the PI regulator for DC bus voltage control. Moreover, this study presents detailed system configurations and control schemes for two types of inverters: 2L-3PVSI and 3L-3PNPC. In order to perform a comparative study between the two structures, we subjected them to the same irradiation profile using the same grid configuration. The Photovoltaic Array (PVA) irradiance is increased instantaneously, in 0.2 s, from 400 W/m2 to 800 W/m2, is kept at 800 W/m2 for 0.2 s, is then gradually decreased from 800 W/m2 to 200 W/m2 in 0.2 s, is then kept at 200 W/m2 for 0.2 s, and is then finally increased to 1000 W/m2 for 0.2 s. We explain the operational principles of these inverters and describe the various switching states involved in generating output voltages. To achieve effective control, we adopt the Finite Set-Model Predictive Control (FS-MPC) algorithm, due to the benefits of excellent dynamic responsiveness and precise current tracking abilities. This algorithm aims to minimise the cost function, while taking into account the dynamic behaviour of both the PV system and the inverter, including any associated delays. To evaluate the performance of the FS-MPC controller, we compare its application in the three-level inverter configuration with the two-level inverter setup. The DC bus voltage is maintained at 615 V using the PI controller. The objective is to achieve a Total Harmonic Distortion (THD) below 5%, with reference to the IEEE standards. The 2L-3PVSI inverter is above the threshold at an irradiance of 200 W/m2. The 3L-3PNPC inverter offers a great THD percentage, meaning improved quality of the power returned to the grid.

Parameter adaptive model predictive control strategy of NPC three-level virtual synchronous generator

The Virtual Synchronous Generator (VSG) emulates the characteristics of a synchronous generator to provide inertia and damping for renewable energy systems. In the case of using the NPC three-level converter structure, traditional control methods require complex dual-loop control and internal PI parameter tuning. Furthermore, although fixed-parameter VSG control can provide inertia and damping when a significant power load is switched in an islanded microgrid, it cannot guarantee frequency regulation performance. To address these issues, this paper proposes an NPC three-level VSG parameter adaptive finite control set model predictive control strategy. This method eliminates the need for dual-loop control and PI parameter tuning. By incorporating angular velocity deviation and its rate of change into adaptive adjustment, a Tracking-Differentiator (TD) is designed to calculate the rate of change of angular velocity. This approach avoids frequent fluctuation of adaptive parameters during load power switching and improves the frequency stability of the microgrid. The effectiveness of the proposed strategy is validated through simulation and experimental verification.

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.

Application of machine learning controller in matrix converter based on model predictive control algorithm

Finite control set model predictive control (FCS-MPC) algorithms are famous in power converter for its easy implementation of constraints with cost function than classical control algortihms. However computation complexity increases when swicthing state is high for converters such as matrix converter, multilevel converters and this impose a serious drawback to compute multi-step prediction horizon MPC algorithm which further increases the computation. To overcome the above said difficulty, machine learning based artificial neural network (ANN) controller for matrix converter is proposed. The training data for ANN controller is derived from MPC algorithm and trained offline with an accuracy of 70.3%. The proposed ANN controller shows a similar and better performance than MPC controller in terms of total harmonic distortion (THD), peak overshoot during dynamic change in reference current and dynamic change in load parameter and less computation with less execution time. Further, ANN controller for matrix converter is tested in OPAL-RT using hardware in-loop (HIL) simulation and showed that it outperforms MPC controller.