Finite Control Set – Model Predictive Control Based On Sliding Mode For Bidirectional Power Inverter

This paper proposes a reduced-computation-burden Finite Control Set - Model Predictive Control (FCS-MPC) applied to a dual-converter-based rectifier with a floating DC link. The main goal of this paper is providing a proof of concept of the discussed system employing the proposed FCS-MPC, highlighting its feasibility, simple multivariable control and straightforward implementation. The proposed FCS-MPC reduces the number of tested vectors from the available 64 to only nine, efficiently controlling grid currents and floating DC-link voltage. To evaluate the performance, steady- and transient-state simulations were carried out to compare the proposed FCS-MPC with the conventional PI-based method. The results indicate that FCS-MPC provides a better dynamic response than the PI-based method. However, its total harmonic distortion (THD) at the same sampling frequency is higher, as the PI-based method benefits from a modulation stage that reduces the current ripple. Additionally, the proposed FCS-MPC shows significantly lower switching losses than the PI-based approach. On the other hand, for the same switching frequency, the proposed FCS-MPC presents a somewhat higher, but similar THD and losses values to the PI-based method. Experimental results further validate the feasibility of the proposed FCS-MPC, reinforcing its potential as an efficient alternative to traditional control strategies in dual-converter-based-rectifier.

In this paper, a Lyaupnov-based finite control set model predictive direct torque control for the permanent magnet synchronous machine (PMSM) is proposed. In the proposed control scheme, the finite control set prediction and the Lyapunov theory are combined to minimize the torque ripple. The eight voltage vectors of the two-level converter are utilized as a finite control set for the torque prediction of the PMSM. A cost function considering the torque error, the maximum torque per ampere operation and the current limitation is introduced. Comparing to the conventional finite control set predictive control, the dominant part of the cost function is utilized as a Lyapunov function to estimate the duty cycle of each voltage vector. An optimum voltage can be obtained by the optimum voltage vector from the eight vectors and their duty cycles. A small sampling frequency and a fixed switching frequency can be realized when compared to the conventional finite set model predictive control. In the end, the simulation and experimental results validate the performance of the proposed control scheme.

Model predictive control (MPC) as a nonlinear control method, is being studied for a wide variety of power electronics applications targeting various power ranges. The fast dynamic response, robustness under different load conditions, multi-objective control, and simplicity of implementation are the main features of MPC. However, the implementation of finite control set MPC (FCS-MPC) or direct MPC in its basic form for multilevel converters (MLCs) demonstrates the inability to generate the staircase multilevel output voltage waveform in a voltage sourced inverter (VSI) application. Adding a reference evaluation block to the basic FCS-MPC method can significantly improve its multilevel voltage generation capability. In this paper, an enhanced model predictive nearest level control (EMP-NLC) is proposed and implemented on a 5-Level flying capacitor multilevel converter (FCMC). The proposed method is simulated under different load conditions, while a comparison against existing MPC techniques is also presented. Experimental results verified the efficacy of the proposed controller.

A direct torque control (DTC) with a modified finite set model predictive strategy is proposed in this paper. The eight voltage space vectors of two-level inverters are taken as the finite control set and applied to the model predictive direct torque control of a permanent magnet synchronous motor (PMSM). The duty cycle of each voltage vector in the finite set can be estimated by a cost function, which is designed based on factors including the torque error, maximum torque per ampere (MTPA), and stator current constraints. Lyapunov control theory is introduced in the determination of the weight coefficients of the cost function to guarantee stability, and thus the optimal voltage vector reference value of the inverter is obtained. Compared with the conventional finite control set model predictive control (FCS-MPC) method, the torque ripple is reduced and the robustness of the system is clearly improved. Finally, the simulation and experimental results verify the effectiveness of the proposed control scheme.

Hybrid active neutral-point-clamped (HANPC) inverters have been recently considered as an attractive solution for high-power applications, while their control with multiple objectives remains quite complicated. The model predictive control (MPC) is an optimal control technique for multilevel inverters due to its powerful ability to handle the multiobjective optimization and nonlinear constrains. However, the tuning of weighting factors (WFs) in MPC is quite challenging and requires additional computational resources. For this motivation, this article proposes a deep residual network optimization approach for the dynamical WFs tuning of the finite control set (FCS) MPC in the HANPC inverters. The proposed method utilizes the residual connection to avoid the problem of gradient vanishing, so as to improve the predictive accuracy. To realize the dynamic adjustment of WFs, a look-up table is formulated with the predicted WFs, then this look-up table is integrated into the FCS-MPC. Consequently, the proposed method occupies less logic resources in the MPC algorithm, providing the desired behavior with fast dynamic response. Comparative studies are presented to evaluate the training and prediction performance of the networks. Finally, the experimental validations are conducted on a platform of HANPC-inverter-fed permanent magnet synchronous motor drive.

This paper proposes a finite set model predictive control (FS-MPC) based thrust maximization technique for linear induction machines used in linear metros. For modeling of the proposed control method, the end effect is taken into consideration. The proposed control method is used to achieve maximum thrust per ampere and to reduce the thrust ripples. It differs from the FS-MPC methods, where the cost function consists of the thrust and angle errors. The thrust error is calculated from the difference between the reference thrust and the predicted thrust, and the angle error is calculated from the difference between the angle of predicted primary current and the angle of the predicted secondary flux in one side and π/4 on the other side. A comparison between the proposed method and the finite set model predictive direct thrust control (FS-MPDTC) is presented to illustrate the superiority of the proposed method. Both simulation and experimental analysis are conducted to validate the effectiveness of the proposed finite set model predictive direct angle control (FS-MPDAC). A prototype test platform is developed in the laboratory with two 3 kW arc induction motors. The simulation model, experimental test platform, and test results are presented in this paper.

<span>This paper is presented for designing a new controller using the predictive model current and speed control method for the asynchronous motor. This control method is based on traditional predictive controller development to have a cascade structure similar to the rotor flux control (field-oriented control) and direct torque control (DTC). Therefore, this control method will have two control loops. Both inner and outer loop controllers use predictive power. The outer ring is speed control, while the internal circle is stator current control. The inner loop is based on the finite control set – model predictive control (FCS-MPC), while the outer ring to take full advantage of the high dynamic response of the inner circle uses the deadbeat MPC. MATLAB simulation results show that this control method has performance equivalent to traditional controllers while minimizing overshoot and having fast, on-demand response times.</span>

Finite control set model predictive control (FCS-MPC) has been widely studied and applied to the power converters and motor drives. It provides the power electronics system with fast dynamic response, nonlinear system formulation, and flexible objectives and constraints integration. However, its variable switching frequency feature also induces severe concerns on the power loss, the thermal profile, and the filter design. Stemming from these concerns, this article investigates the variable switching frequency characteristics of FCS-MPC on the grid-connected inverters. An intuitive relationship between the switching frequency and the magnitude of the converter output voltage is proposed through the geometry analysis, where the switching frequency is maximized when the converter output voltage is around one-third of the DC bus voltage and decreasing when the output voltage moves away from this value. The impacts of this variable switching frequency property on the power loss and current harmonics are also analyzed. Simulation and experimental results both verify the proposed property. With this intrinsic property, FCS-MPC can autonomously achieve a less-varying temperature profile of power modules and an improved reliability compared with the conventional control strategy.

This article proposes a phase-shifted model predictive control (PS-MPC) strategy to fully exploit the most relevant features of finite control set model predictive control (FCS-MPC) and phase-shifted pulse width modulation (PS-PWM) in a three-level active neutral point clamped (3L-ANPC) converter. Conventional PS-MPC strategies utilize a steady-state control input reference, typically a harmonic-free signal depending on the converter topology. However, this article demonstrates that the PS-MPC strategy applied to the 3L-ANPCconverter requires a control input containing a third-harmonic component with a specific magnitude and phase angle, which improves output current tracking and capacitor voltage balancing. By designing synchronized models for each carrier, the predictive controller optimizes duty cycles independently, ensuring the effective regulation of both control variables while addressing model nonlinearities. The proposed strategy is experimentally validated using a DSP F28379D microcontroller and a three-phase 3L-ANPCconverter using a total of 64 switching states in a grid-connected application. Compared to PS-PWM and FCS-MPC, the proposed approach maintains a fixed switching frequency, improves steady-state tracking, and enhances capacitor voltage balancing with a fast dynamic response. Additionally, it outperforms PS-MPC with control input change penalization (PS-MPC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol\Delta} \boldsymbol{u}$</tex-math></inline-formula>) by reducing spectral dispersion while achieving lower execution time than both FCS-MPC and PS-MPC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol\Delta} \boldsymbol{u}$</tex-math></inline-formula>, making it more suitable for real-time implementation. Such results are also discussed with a video demonstrating the real-time implementation.

Model predictive control (MPC) presents important advantages in the control of the power converter and drives such as, fast dynamic response and capability to include nonlinear constrains. These have positioned MPC as a powerful and realistic control strategy, however, it also has disadvantages such as variable switching frequency and parameter sensitivity. This paper applied a modulated model predictive speed control that guarantees a fix switching frequency and, thanks to disturbance compensation, robustness to parameters variation. The strategy is validated and compared to finite set model predictive speed control through simulation results.

This study presents an online multiparameter estimation scheme for interior permanent magnet motor drives that exploits the switching ripple of finite control set (FCS) model predictive control (MPC). The combinations consist of two, three, and four parameters are analysed for observability at different operating states. Most of the combinations are rank deficient without persistent excitation (PE) of the system, e.g. by signal injection. This study shows that high frequency current ripples by MPC with FCS are sufficient to create PE in the system. This study also analyses parameter coupling in estimation that results in wrong convergence and propose a decoupling technique. The observability conditions for all the combinations are experimentally validated. Finally, a full parameter estimation along with the decoupling technique is tested at different operating conditions.

This Paper evaluates the integration of fuel cell power system with the grid using finite control set model predictive controller. A mathematical model of the phosphoric acid fuel cell power system is designed. The grid and the fuel cell are modeled in PSCAD simulation environment. Direct power control technique for model predictive controller is used to control the converter. The steady state results are shown in the paper, Also, a single phase fault and three phase fault is applied at the grid side to study the effect of integrating the fuel cell with the grid. The simulation results show model predictive controller can effectively integrate fuel cell with the grid.

In this paper, a simplified finite‐control‐set model predictive control (FCS‐MPC) method with a deadbeat solution is proposed for eight‐switch, three‐phase, neutral‐point‐clamped converters (ESTP‐NPCs). Specifically, in order to improve the computational efficiency, a novel sector distribution scheme is presented by dividing the whole voltage vector space into eight sectors. In the each sector, only three voltage vectors are determined as new candidate finite control sets (FCSs), and then the optimal voltage vector that minimizes the predesigned cost function is selected from the new candidate FCS. Meanwhile, in order to avoid the selection of the weighting factor, a multiobjective optimization strategy based on an average ranking is introduced into the proposed strategy. In addition, the DC‐link capacitor voltage oscillations and deviations are suppressed effectively, and the time delay of control command is compensated. In view of these characteristics, the proposed control strategy (i) has lower computational burden, (ii) has good dynamic performance, (iii) does not need the tuning of weighting factor, and (iv) does not need the modulation method. Finally, compared to the conventional FCS‐MPC, the proposed method is more efficient as demonstrated by simulation results. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

In this article, sensorless finite control set model predictive speed control of induction motor using adaptive full-order observer is proposed. Firstly, finite control set model predictive speed control is introduced. It is composed of direct speed control and model predictive control, which gets over the limitations of cascaded structure and obtains high dynamics response. Due to lack of speed loop, stator flux linkage, rotor speed and stator current are taken as the control targets in the cost function. Then, adaptive full-order observer is used to estimate rotor speed and stator flux linkage to improve the reliability and keep the system cost down. Finally, the simulation results indicate that the proposed method has well dynamic performance in the wide speed range.

This article proposes a new finite control set model predictive control (FCS-MPC) strategy that simultaneously evaluates two targeting control objectives including speed and currents in a single cost function, achieving high-performance single-closed-loop control structure. Besides, aiming at the calculation delay problem of the model predictive control controllers, a novel calculation delay compensation method by predicting the current variation within the delay time is proposed. In this article, an improved machine model that is especially designed for the multiobjective FCS-MPC operation is illustrated at first. Then, a new cost function that can evaluate the tracking performance of speed and d-axis current and the steady-state performance of the q-axis current is developed. Compared with the conventional FCS-MPC approaches, extra speed controllers are not needed so that the proposed control topology becomes simpler. Then, in order to tune the weighting factors for the speed and currents in the cost function, an efficient handling strategy containing two implementation procedures, state variable normalization and balance of state sensitivity to voltage alteration, is developed. Finally, a brand-new computation delay estimation and compensation technique based on the dual sampling within a control period is proposed to reduce the current and torque ripples during the control process. Comparative simulation and experiments conducted on a three-phase 1.5-kW permanent magnet synchronous machine drive system are employed to verify the effectiveness of the proposed techniques.