Control Set Model Predictive Torque Research Articles

Continuous Control Set Model Predictive Torque Control With Minimum Current Magnitude Criterion for Synchronous Motor Drives

Continuous Control Set Model Predictive Torque Control With Minimum Current Magnitude Criterion for Synchronous Motor Drives

Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications

In the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively.

Finite Control Set Model Predictive Torque Control Considering Dynamic and Steady‐State Control Performance for SMPMSM Drives

In order to improve the dynamic and steady‐state performance of the finite control set model predictive torque controlled surface‐mounted permanent magnet synchronous motor drive system, a novel finite control set model predictive torque control (FCS‐MPTC) with different dynamic and steady‐state control schemes is proposed in this paper. When the SMPMSM drive system operates at transient state, the optimal basic voltage vector of inverter is the vector that minimizes torque tracking error, the proposed FCS‐MPTC achieves fast dynamic response based on the optimal torque control. When the system operates at steady state, the relative importance of torque to the stator flux amplitude is calculated based on the quantitative analysis of minimum staggered errors between motor torque and stator flux amplitude control objectives, then the priority weighting factors based on the relative importance are determined online adaptively, the optimal control of motor torque and stator flux amplitude is achieved simultaneously. Finally, the comparison of the experimental results of proposed FCS‐MPTC with different control methods is implemented and research conclusion is shown. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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.

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.

Low-Ripple Continuous Control Set Model Predictive Torque Control for Switched Reluctance Machines Based on Equivalent Linear SRM Model

In this article, a low-ripple continuous control set (CCS) model predictive torque control (MPTC) method for switched reluctance machines (SRMs) is proposed. The inherent high nonlinearity of the SRMs makes it difficult to solve the optimization problem in the CCS MPTC algorithm analytically. To address this issue, an equivalent linear SRM model is adopted, and the cost function is also properly modified. Then, with the torque boundary values provided by executing the voltage vectors of an improved switching table, the optimization problem in the CCS MPTC method becomes simple and analytically solvable. The Lagrange multiplier method is employed to solve this optimization problem analytically and generate the optimum torque reference values for the active phases. Based on the estimated torque variation rates, the duty cycles for each phase are calculated. Extensive simulation and experimental tests are carried out in a four-phase 8/6 SRM setup. These testing results reveal that the proposed CCS MPTC method shows much lower torque ripples and current ripples in a wide speed range with a low computational burden than the existing finite control set MPTC methods.

Hybrid Decision Based on DNN and DTC for Model Predictive Torque Control of PMSM

To address the issue of poor real-time performance caused by the heavy computational burden of the finite control set model predictive torque control (MPTC) of a permanent magnet synchronous motor (PMSM), a data-driven control method using a deep neural network (DNN) is proposed in this paper. The DNN can learn the MPTC’s selective laws from its operation data by training offline and then substitute them for voltage vector selection online. Aiming to address the data-driven runaway problems caused by the asymmetry between the dynamic and static training data, a hybrid decision control strategy based on DNN and DTC (direct torque control) is further proposed, which can realize four-quadrant operation with a control effect basically equivalent to MPTC. The proposed strategy has great application potential for use in multi-level inverter and matrix converter driving with multiple candidate voltage vectors or multi-step prediction.

Model-Free Parallel Predictive Torque Control Based on Ultra-Local Model of Permanent Magnet Synchronous Machine

The finite control set model predictive torque control (FCS-MPTC) selects the optimal voltage vector (VV) by the composite cost function composed of torque and flux error, which makes it have a faster dynamic response than conventional control methods. However, the prediction state error caused by machine parameter mismatch and the difficulty in setting the weight factor in the composite cost function seriously restrict the popularization and application of FCS-MPTC. In this paper, a model-free parallel predictive torque control (MF-PPTC) based on an ultra-local (UL) model is proposed to solve above problems. The UL model replaces the machine mathematical model without any machine parameters and only uses the input and output of the system, which greatly improves the robustness of the control system. The nonlinear extended state observer proposed for the unknown part of the system has fast convergence and improves the dynamic performance of the system. In addition, the conventional parallel predictive control structure is optimized to reduce the dynamic adjustment process during the selection of optimal voltage vector. Simulation and experimental comparison between the conventional PPTC and the proposed MF-PPTC verified the superiority of the proposed method.

Finite control set model predictive torque control using sliding model control for induction motors

Finite control set model predictive torque control (FCS-MPTC) has become increasingly prevalent for induction motors (IM) owing to its simple concept, easy incorporation of constraints and strong flexibility. In traditional FCS-MPTC speed controller design, a classical proportional integral (PI) controller is typically chosen to generate the torque reference. However, the PI controller is dependent on system parameters and sensitive to the load torque variation, which seriously affects control performance. In this paper, a model predictive torque control using sliding mode control (MPTC + SMC) for IM is proposed to enhance the robust performance of the drive system. First, the influence of the parameter mismatches for FCS-MPTC is analyzed. Second, the shortcomings of traditional PI controller are derived. Then, the proposed MPTC + SMC method is designed, and the MPTC + PI and MPTC + SMC are compared theoretically. Finally, experimental results demonstrate the correctness and effectiveness of the proposed MPTC + SMC. In comparison with MPTC + PI, MPTC + SMC has the better dynamic performance and stronger robust performance against parameter variations and load disturbance.

Ripple Attenuation for Induction Motor Finite Control Set Model Predictive Torque Control Using Novel Fuzzy Adaptive Techniques

Finite control set model predictive torque control (FCS-MPTC) strategy has been widely used in induction motor (IM) control due to its fast response characteristic. Although the dynamics of the FCS-MPTC method are highly commended, its steady-state performance—ripple deserves attention in the meantime. To improve the steady-state performance of the IM drives, this paper proposes an improved FCS-MPTC strategy, based on a novel fuzzy adaptive speed controller and an adaptive weighting factor, tuning strategy to reduce the speed, torque and flux ripples caused by different factors. Firstly, a discrete predicting plant model (PPM) with a new flux observer is established, laying the ground for achieving an FCS-MPTC algorithm accurately. Secondly, after analyzing the essential factors in establishing a fuzzy adaptive PI controller, with high ripple suppression capacity, an improved three-dimensional controller is designed. Simultaneously, the implementation procedures of the fuzzy adaptive PI controller-based FCS-MPTC are presented. Considering that a weighting factor must be employed in the cost function of an FCS-MPTC method, system ripples increase if the value of the weighting factor is inappropriate. Then, on that basis, a novel fuzzy adaptive theory-based weighting factor tuning strategy is proposed, with the real-time torque and flux performance balanced. Finally, both simulation and hardware-in-loop (HIL) test are conducted on a 1.1 kW IM drive to verify the proposed ripple reduction algorithms.

A Robust Predictive Torque and Flux Control for IPM Motor Drives Without a Cost Function

Finite control set model predictive torque and flux control of ac motor drives commonly selects optimum inverter switching states through a cost function with a required weighting factor. The weighting factor should be well-tuned in accordance with the motor specifications and operating points. Additionally, the control performance of the drive is degraded under the parametric uncertainties. In this article, a predictive torque and flux control method is proposed for interior permanent magnet synchronous motor drives without a cost function. As a result, the tuning of the weighting factor is avoided. Besides, an estimation scheme based on a data-driven model is adopted, which enhances the robustness of the drive against parametric uncertainties. Instead of directly using the motor parameters, the input and output data of the control system, i.e., voltage and current, are used for estimating three new coefficients employed in the predictions. The performance of an interior permanent magnet synchronous motor drive under the proposed control method is evaluated and compared with those under the conventional predictive torque control method and a recently developed predictive torque control method without a weighting factor. The results confirm the effectiveness of the proposed control method and its superiority over the other two methods.

Model predictive torque control of PMSM based on data drive

Aiming at the problem of bad real-time performance caused by the large calculation of the finite control set model predictive torque control(MPTC), a data-driven method for MPTC of the permanent magnet synchronous motor(PMSM) is proposed. The data produced by PMSM-MPTC system are used to train DNN to learn its selective laws and then replace it to select the optimal voltage vector. This paper also analyzes the data-driven out-of-control problems caused by the difference between the steady data and dynamic data of the MPTC and solves the above issue by extending and balancing the data set, which further proves the feasibility of the data-driven MPTC. The simulation experiments show that the torque ripple, stator flux ripple and the system switching frequency of the data-driven MPTC are further reduced, indicating has certain superiority.

Robust Model Predictive Speed Control of Induction Motors Using a Constrained Disturbance Observer

This paper proposes an offset-free speed control method for induction motors using a model predictive control and disturbance observer. The proposed method has cascade control loops i.e. inner-loop model predictive torque control and outer-loop robust speed control. The outer-loop robust speed controller is composed of a stabilizing MPC and a discrete-time disturbance observer. The disturbance observer estimates the effects of the parameter uncertainties and the load torque to yield an offset free speed tracking, while taking the torque limit of the motor into account. This speed controller generates the reference torque for the inner-loop torque controller. A loss-minimizing continuous control set model predictive torque controller(CCS-MPTC) considering the current and voltage constraints of the motor is used as the inner-loop torque controller. Experimental results show a significant performance improvement when the torque constraints are considered in the DOB design.

Disturbance Compensation-Based Model Predictive Flux Control of SPMSM With Optimal Duty Cycle

Finite control set model predictive torque control (FCS-MPTC) is receiving more attention for electric drives, due to its intuitive idea, flexibility to incorporate constrains, and fast torque dynamic response. However, traditional FCS-MPTC requires heavy computation effort and has high torque ripple at steady state. The weighting factors of the cost function are hard to optimize to obtain desired performances. Moreover, torque tracking error exists because FCS-MPTC belongs to the open-loop control strategy. This paper proposes disturbance compensation-based model predictive flux control (DCB-MPFC) with optimal duty cycle (ODC) of surface permanent magnet synchronous motor (SPMSM). This paper first proves the uniformity of MPTC, MPFC, and model predictive voltage control (MPVC). The cost function based on the stator flux linkage vector can be converted to the equivalent cost function based on voltage vector. The role of the equivalent cost function is to compare the deadbeat vector with three pairs of vectors, which can reduce computation effort and torque ripple. In addition, the disturbance compensation strategy is proposed to eliminate torque tracking error. The proposed DCB-MPFC is verified effectively by experiments that are compared with traditional algorithm, proportional-integral speed loop-based mode predictive torque control (PI-MPTC).

Predictive direct torque control for permanent-magnet synchronous machines based on duty ratio modulation

In this paper, Two-step model predictive control based on duty ratio modulation of permanent magnet synchronous machine (PMSM) is proposed. There is a large amount of computation in the two-step predictive control. In this paper, a fast vector selection method is proposed to solve the problem of computational complexity. The proposed voltage vector selection method avoids the complete enumeration for testing all feasible voltage vector, and the calculation method of duty are discussed. This control method achieves a constant switching frequency, and the torque fluctuation is smaller, inheriting the advantages of predictive control response speed. To investigate the effectiveness of the proposed method, two-step predictive direct torque control based on duty ratio modulation (TSPTC-DM), comparison of one-step predictive direct torque control based on duty ratio modulation (OSPTC-DM) and finite control set model predictive torque control (FCS-MPTC) is shown.

Active Disturbance-Rejection-Based Speed Control in Model Predictive Control for Induction Machines

Finite set model predictive torque control (FCSMPTC) of induction machines has received widespread attention in recent years due to its fast dynamic response, intuitive concept, and ability to handle nonlinear constraints. However, FCSMPTC essentially belongs to the open-loop control paradigm, and unmatched parameters inevitably cause electromagnetic torque tracking error. In addition, the outer loop (i.e., the speed loop) based on a proportional-integral (PI) regulator cannot achieve optimal control between speed dynamic response and torque tracking error compensation. The traditional control paradigm is abbreviated as PI-MPTC. In order to solve the aforementioned problem, this paper proposes active disturbance-rejection-based model predictive torque control (ADR-MPTC). First, the influence mechanism of mismatched parameters on torque prediction error in PI-MPTC is studied, and then the performance of a traditional PI regulator used to compensate for torque prediction error is analyzed. Second, this paper introduces several parts of the proposed ADR-MPTC, including the design of the torque prediction error observer, nonlinear prediction error compensation strategies, an enhanced predictive torque control, and a simplified full-order flux observer. Finally, PI-MPTC and ADR-MPTC are studied experimentally. The experimental results show that compared with PI-MPTC, ADR-MPTC performs better in dynamic and steady states, and has stronger robustness.

Three Vectors Model Predictive Torque Control Without Weighting Factor Based on Electromagnetic Torque Feedback Compensation

Finite control set-model predictive torque control (FCS-MPTC) depends on the system parameters and the weight coefficients setting. At the same time, since the actual load disturbance is unavoidable, the model parameters are not matched, and there is a torque tracking error. In traditional FCS-MPTC, the outer loop—that is, the speed loop—adopts a classic Proportional Integral (PI) controller, abbreviated as PI-MPTC. The pole placement of the PI controller is usually designed by a plunge-and-test, and it is difficult to achieve optimal dynamic performance and optimal suppression of concentrated disturbances at the same time. Aiming at squirrel cage induction motors, this paper first proposes an outer-loop F-ETFC-MPTC control strategy based on a feed-forward factor for electromagnetic torque feedback compensation (F-ETFC). The electromagnetic torque was imported to the input of the current regulator, which is used as the control input signal of feedback compensation of the speed loop; therefore, the capacity of an anti-load-torque-disturbance of the speed loop was improved. The given speed is quantified by a feed-forward factor into the input of the current regulator, which is used as the feed-forward adjustment control input of the speed controller to improve the dynamic response of the speed loop. The range of the feed-forward factor and feed-back compensation coefficient can be obtained according to the structural analysis of the system, which simplifies the process of parameter design adjustment. At the same time, the multi-objective optimization based on the sorting method replaces the single cost function in traditional control, so that the selection of the voltage vector works without the weight coefficient and can solve complicated calculation problems in traditional control. Finally, according to the relationship between the voltage vector and the switch state, the virtual six groups of three vector voltages can be adjusted in both the direction and amplitude, thereby effectively improving the control performance and reducing the flow rate and torque ripple. The experiment is based on the dSPACE platform, and experimental results verify the feasibility of the proposed F-ETFC-MPTC. Compared with traditional PI-MPTC, the feed-forward factor can effectively improve the stability time of the system by more than 10 percent, electromagnetic torque feedback compensation can improve the anti-load torque disturbance ability of the system by more than 60 percent, and the three-vector voltage method can effectively reduce the disturbance.

Robustness Improvement of FCS-MPTC for Induction Machine Drives Using Disturbance Feedforward Compensation Technique

Finite control set-model predictive torque control (FCS-MPTC) has a fast dynamic response because this algorithm directly selects the optimal voltage vector by its cost function for induction machine drives fed by voltage source inverter (VSI). However, belonging to open-loop control paradigm, the FCS-MPTC has torque tracking error due to inevitable load disturbance and mismatched model parameters in reality. In traditional FCS-MPTC, the outer loop, i.e., speed loop, adopts a classic proportional integral (PI) controller, abbreviated as PI-MPTC. The lumped disturbance is only suppressed by a PI controller. However, pole placement of the PI controller is usually designed by cut-and-trial, which is difficult to simultaneously achieve optimal dynamic performance and optimal suppression of lumped disturbance. In this paper, the FCS-MPTC with mismatched parameters is first analyzed. Second, the deficiencies of the traditional PI controller are introduced. Third, disturbance feedforward compensation-based-model predictive torque control (DFCB-MPTC) of induction machine is proposed to compensate lumped disturbance and improve the performance of the system. Furthermore, a simplified stator flux observer is proposed, whose gain matrix is independent of rotor speed. Experimental results verify the feasibility of the proposed DFCB-MPTC. Compared with traditional PI-MPTC, the proposed DFCB-MPTC has better dynamic performance, steady performance, and stronger robustness.

Algebraic Tuning Guidelines for Model Predictive Torque and Flux Control

Finite control set model predictive torque and flux control uses a scalar weight in its cost function to determine the tradeoff between the torque and flux magnitude tracking error terms. This weight strongly influences the current distortions. It will be shown that an optimal weight can be computed algebraically, which minimizes the current distortions. The resulting predictive torque and flux controller achieves current distortions per switching frequency that are very similar to those of a predictive current controller, provided that the corresponding weight on the switching effort is selected appropriately. To this end, a second analytical expression will be derived.

Finite Control Set Model Predictive Torque Control of Induction Machine With a Robust Adaptive Observer

This paper proposes a finite control set model predictive torque control (PTC) of induction machine with an adaptive observer. Model reference adaptive system (MRAS) can accurately estimate the rotor speed. However, the encoderless PTC method requires not only the estimated rotor speed but also the estimated fluxes. A sliding mode stator voltage model observer is applied as the reference model in the MRAS. Although sliding mode method has chattering problem, it can be largely reduced by optimizing the sliding function and by proper sliding gains designed with H infinity method. The proposed encoderless PTC is experimentally verified in this work. The results show that the method is stable in a wide speed range and has good performance over load disturbance. Moreover, the proposed encoderless strategy has the merits of predictive control: fast dynamics, straightforward structure, and easy implementation for constraints’ inclusion.