Revised Sequential Predictive Torque Control with Adaptability under Multiple Operating Conditions for Induction Motors Drives

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.

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.

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.

The Finite control set model predictive control (FCS-MPC) is recently introduced to control inverters without the modulation stage. The absence of the modulation stage gives an unpredictable performance of the control system. In this paper, the performance of FCS-MPC is assessed by comparing with PID control which is based on Scalar Pulse Width Modulation (PWM). The two control techniques are applied for load voltage regulation of the autonomous four-leg voltage source inverter (FLVSI). Practically, the predictive control requires a large number of calculations, resulting in high computation time and delay. In this paper, a new finite control set model predictive voltage control (MPVC) algorithm is proposed to predict the load voltages for 15 switching states instead of 16 switching states for reducing the computation time required for the control algorithm. Moreover, the algorithm is optimized by removing the repeated computations and the delay is compensated using the two-step prediction horizon principle. An accurate discrete-time state-space model of the autonomous FLVSI with output LC-filter is used for predicting the load voltages considering the neutral inductance and damping resistance of the LC filter. A simple PID control scheme with decoupled feedforward voltage and current loops is used in the DQ0 reference frame, while MPVC operates in the ABC reference frame. The simulation and experimental results are used to show the full assessment of the MPVC. The prominent outcomes show the ability of the proposed MPVC algorithm to provide high power quality under unbalanced and non-linear load conditions with high stability and robustness.

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 work introduces the control concept of variable sampling time finite control-set model predictive control (FCS-MPC). The new control concept is introduced in theory based on a review of the conventional FCS-MPC concepts performed with a constant sampling time. Based on the partitioning of the sampling instant in multiple smaller sampling instants it is possible to optimize, besides the switching states, the switching states turn-on times. Therefore, the proposed variable sampling time FCS-MPC sets both: the switching state and the related turn-on times. To utilize the available calculation power for longer sampling instants an adaptation of the control- and prediction horizon to the sampling time is proposed. The theoretical control concepts are applied to the control of a grid connected two-level voltage-source converter where a simple L-type line-filter is used to demonstrate the control performance of the variable sampling time FCS-MPC algorithms in the laboratory environment.

Finite Control Set-Model Predictive Speed Control for Induction Motors with Optimal Duration

Aiming at the problem of difficult allocation of the dq-axis current term weight coefficients of the cost function in the traditional finite control set model predictive current control of a permanent magnet synchronous motor, a finite control set model predictive current control method based on a neural network to optimize the cost function is proposed. In this paper, the neural network is constructed to assign the weights of the cost function by judging the changes of the speed deviation and the speed change rate, and the method improves the dynamic performance of the system and reduces the influence of the unreasonable assignment of the dqaxis weight coefficients on the system. The proposed method is verified by simulation analysis.

Deadbeat control (DBC) has been known for decades due to its conceptual simplicity and good performance. Recently finite control set model predictive control (FCS-MPC) is emerging as a powerful strategy for control of power converters. Different from DBC, which uses a modulator such as space vector modulation (SVM), FCS-MPC directly obtained the optimal switching states of power converters by minimizing a cost function. Several improved versions of FCS-MPC have been proposed to improve the steady state performance by introducing two or three vectors during one control period. This paper investigates the inherent relationship between SVM-DBC and multiple-vector FCS-MPC. It is found that with the aim of tracking error minimization, the two kinds of predictive methods can be unified in one frame, where the vector sequence and duration in FCS-MPC can be easily obtained from SVM-DBC. In other words, under some special cases, FCS-MPC and its improved versions can be considered as special cases of SVM-DBC. This is the main contribution of this paper, because DBC and FCS-MPC are previously studied separately. The theoretical study is confirmed by the analysis on state-of-the-art DBC and FCS-MPC and supported by the selected simulation study and experimental tests from a three-phase two-level voltage source PWM rectifier.

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.

This paper proposes a non-cascade -single loop- Direct Speed Control algorithm for surface mounted Permanent Magnet Synchronous Motor (PMSM) fed by Matrix Converter. The proposed method uses Finite Control Set Model Predictive Control (FCS-MPC) to manipulate system speed and currents simultaneously. Also, for better performance of the predictive method, an observer designed to estimate mechanical torque and other uncertain parameters of the mechanical subsystem as a lumped disturbance. Simulation results using Matlab/Simulink demonstrate the performance of proposed algorithm.

Six-phase machines are increasingly used in safety-critical applications due to their inherent fault-tolerant capabilities. Due to the greater complexity of controlling six-phase machines and the fast dynamics required in safety-critical applications, finite control set model predictive control (FCS-MPC) emerged as an ideal candidate for the control of six-phase machines. However, most of the available FCS-MPC strategies only apply to six-phase machines where the two sets of three-phase windings are star-connected with isolated neutral points (2N). Nevertheless, the 2N configuration does not take full advantage of the machine’s capabilities in terms of fault tolerance. Hence, this paper proposes a predictive current control strategy based on virtual vectors for six-phase permanent magnet synchronous (PMSM) drives with a single isolated neutral point (1N) configuration. The proposed method reduces the current harmonic distortion, decreases the copper losses, and is suitable to operate the six-phase drive in fault-tolerant conditions. The included simulation and experimental results demonstrate the good performance obtained with the proposed strategy.

This paper presents a finite control set model predictive control (FCS-MPC) approach for two induction machines driven by a nine-switch inverter (NSI). In the traditional approach, two separate voltage source inverters are necessary to drive the independent induction motors. In the proposed method, the nine-switch inverter is used to control the separate motors with a reduced number of switching devices compared to traditional method. A robust control strategy that eliminates the interactions between separate mechanical loads is required to achieve a proper independent speed and torque control for two induction machines through the NSI. To ensure the reliability of the machine operation, the indirect-field oriented control-based model predictive control strategy is proposed. The proposed control strategy is experimentally validated across the 3.2 kW SiC-based NSI prototype. The control algorithm is performed on an Altera Cyclone IV Field-programmable gate array. The experimental results demonstrate that the proposed dual-model predictive control method provides a good and robust motor control operation under different loading conditions. Two induction motors are successfully controlled, and the independent speed and torque control are achieved.

Finite control set model predictive control (FCS-MPC) has been widely recognized in the field of electrical drive control during the past decades, due to its merits of quick dynamic response and low switching frequency. However, it is inherently penalized by the high tracking deviations in the steady state as well as exhaustive search among the switching sequences. To cope with this issue, a low-complexity gradient descent based finite control set predictive current control (GD-FCSPCC) combined with backtracking optimized iteration approach is proposed in this paper, aiming to improve the control performance by effectively tracking the reference value. Firstly, FCS-PCC is reformulated as a quadratic programming (QP) problem from a geometric perspective. Consequently, the convexity of QP problem is proved to underlying the gradient descent to minimize the tracking error in an effective manner. Thus, the control objectives are determined by optimizing the deviation between the gradient descent and the stator current derivative in a cascade structure, to reduce the number of enumerated sequences. The procedures are repeated in the iteration periods optimized via a backtracking search method, until the stopping criterion is satisfied. The effectiveness of the proposed GD-FCSPCC is experimentally validated on a 2.2 kW induction machine testbench.

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.