Predictive Torque Control Method Research Articles

Predictive torque control of induction motor for rotor bar faults diagnosis

Unlike DC motors and synchronous motors, they are maintenance-free motors due to the absence of brushes, commutators and slip rings. Induction motors can be operated in polluted and explosive environments as they do not have brushes which can cause sparks. In this paper, the performance of two control techniques, namely direct torque control (DTC) and predictive torque control (PTC), are compared in transient and static states when applied to a faulty induction machine (IM). The current and torque ripples is evaluated in a healthy machine, as well as in the presence of faults, at various speed and load values. During the transient state, the objective is to assess the method that provides the optimal dynamic response, which is achieving the desired speed without any overshooting while during the static state, the objective is to minimize torque ripple and harmonics in the stator current. The Discrete Wavelet Transform (DWT) is used to analyze stator phase current. In addition, the energy eigen value (EEV) analysis has been used to determine the fault severity. The healthy and faulty systems are simulated using Matlab/Simulink for the two control methods. The results show the superiority of the PTC method compared to the DTC. A comparison of the proposed control method with other works reported in the literature is performed to verify the superiority of the proposed strategy.

An Improved Model Predictive Torque Control for PMSM Drives Based on Discrete Space Vector Modulation

In this paper, an improved model predictive torque control (MPTC) method based on discrete space vector modulation (DSVM) is proposed for permanent magnet synchronous motor (PMSM) drives. Aiming at solving the two problems of large torque ripples and high computational complexity in conventional MPTC, the proposed method adopts a second optimization and a new simplified search strategy. The key idea of second optimization is to make the output voltage vector closer to the actual optimal solution. In this case, a more suitable voltage vector is applied in each sampling period. The simplified search strategy reduces the calculation time by cutting down the number of candidate voltage vectors without affecting drives performance. Compared to the conventional MPTC without DSVM and with DSVM, the proposed method can produce superior steady-state performance and lower computational complexity. Simulation and experimental results are presented to validate the effectiveness and feasibility of the proposed method.

A Weighting-Factorless Predictive Torque Control Method for Open-End Winding PMSM with Common DC Bus

The weighting factors (WFs) of conventional predictive torque control (PTC) are difficult to tune, which limits its practical application. When the conventional PTC is applied to the open-end winding permanent magnet synchronous motor (OEW-PMSM) with a common DC bus, there are three different units of variables that require prediction and control. Therefore, it is necessary to introduce two WFs to construct the cost function (CF), which increases the difficulty of realizing the control strategy. Sequential PTC adopts the idea of hierarchical optimization to avoid the introduction of WFs directly. In this paper, the sequential PTC structure is constructed to avoid the introduction of WFs. Then, the number of elements in the finite control set (FCS) of each layer is optimized to reduce torque and zero sequences current (ZSC) ripple, so as to achieve a better control effect. Finally, the effectiveness of the proposed sequential control strategy is proved by simulation.

An Improved Direct Predictive Torque Control for Torque Ripple and Copper Loss Reduction in SRM Drive

This paper introduces a direct predictive torque control (DPTC) method to minimize the torque ripple and copper loss of the switched reluctance motor (SRM). Generally, there is a problem with torque ripple during the SRM’s commutation. In this method, the commutation optimization is provided by a direct predictive torque control algorithm. Firstly, the reachable range of phase torques is predicted and the boundary of two continuous phases is modified. By dividing the torque range directly to determine the torque allocation set, the suppression of torque ripple becomes simple. Secondly, considering the optimization problem of copper loss during commutation, a cost function only related to the phase current is constructed. Further, the minimization of copper loss can be achieved by solving the cost function, and the work of setting weight parameters is not required. Finally, the proposed DPTC method is tested by simulation and experiment in a three-phase 12/8-pole SRM drive system and the results are compared with the existing predictive torque control methods. The results show that the proposed method has less torque ripple and copper loss, which effectively improves the torque control performance.

A Low-Complexity Modulated Model Predictive Torque and Flux Control Strategy for PMSM Drives Without Weighting Factor

This article proposes a modulated model predictive torque and flux control (M2PTFC) method with low complexity for a two-level voltage source inverter (2L-VSI)-fed permanent magnet synchronous motor (PMSM). The proposed strategy aims to reduce the computation burden and simplify the control implementation of the conventional M2PTFC scheme by reducing the number of candidate voltage vectors at every control sample to the minimum (i.e., only two candidates) and eliminating the weighting factor of the cost function and its corresponding tuning procedures. For these purposes, the proposed method devotes different control objectives to the duty modulation and the cost function evaluation processes while executing them sequentially. First, aiming at torque ripple reduction, a duty modulation strategy is proposed based on the analysis of torque deviations produced by different voltage vectors, which ensures proper selection of the two candidates and restricts active voltage vectors (AVVs) that cause high deviations. Then, these candidates are evaluated based on a cost function assigned to achieve the stator flux control objective. The effectiveness of the proposed control scheme is verified through comparative assessment with the conventional M2PTFC and two existing simplified methods by means of simulation and experimental results.

Comparison and analysis of predictive control of induction motor without weighting factors

The research and promotion of electric drive systems for new energy transportation equipment is an important link in the realization of low-carbon transportation. Model predictive control is an effective new control strategy for electrical drive systems. Taking the three phase induction motor drive system as the research object, three kinds of model predictive torque control methods are reviewed and analyzed in depth, and a theoretical proof of Pareto optimality of MPC methods without weighting factors is established. Predictive torque control is a classical model predictive control method for induction motor drives. When using it, a trial-and-error method is usually used to set appropriate weighting factors for different control objectives, which strongly depends on the designer’s experience. By changing the structure of the cost function, the sequential model predictive control eliminates the weighting factors and improves the control effect of the stator current. The even-handed model predictive control utilizes the interaction error and considers all the control objectives at the same time, so that the optimization order of the control objectives changes with the working conditions, which further improves the control performance. Through theoretical analysis and experimental test, this paper illustrates the performance differences between traditional predictive torque control, sequential model predictive control and even-handed model predictive control.

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.

Multiscalar Model-Based Predictive Torque Control Without Weighting Factors and Current Sensors for Induction Motor Drives

This article focuses on eliminating the weighting factor in traditional predictive torque control (PTC) and considers the control scheme without current sensors. A current sensorless direct PTC method without the weighting factor for induction motor (IM) drives is proposed. Based on the multiscalar model, the controller directly predicts the torque and its dual quantity, instead of predicting the stator flux and stator current in advance and then indirectly calculating the torque like PTC. Since the torque and its dual quantity (the inner and external products of the flux and stator current) have the same units and dimensions, the design of the cost function does not require a weighting factor. Furthermore, an adaptive virtual current observer is used to reconstruct the stator currents. The estimated currents replace the measured currents to realize current sensorless, which can avoid the measurement noise caused by the current sensors, thus reducing the current and torque ripples. Simulation and experimental results illustrate the effectiveness of the proposed approach under different working conditions.

Computationally Efficient Model Predictive Torque Control of Permanent Magnet Synchronous Machines Using Numerical Techniques

This study presents a computationally efficient model predictive torque control (MPTC) method for permanent magnet synchronous machines (PMSMs). The existing MPTC methods require solving complex optimization problems in iterations; this approach is computationally demanding. In contrast, the proposed MPTC transforms the optimization problem into two subproblems and derives the corresponding solutions explicitly using numerical techniques, which entail linearization of the torque function and current constraint; the proposed approach can significantly reduce the computational burden. Additionally, the proposed MPTC does not involve any control gains and weighting factors; hence, gain tuning is not required for the proposed MPTC. These features are advantageous when implementing torque control. Using a 4-kW PMSM drive, the effectiveness of the proposed MPTC is demonstrated both numerically and experimentally in terms of dynamic performance and computational efficiency. This is realized by comparing the proposed method with the existing methods.

Computational-Efficient Model Predictive Torque Control for Switched Reluctance Machines With Linear-Model-Based Equivalent Transformations

In this article, a novel model predictive torque control (MPTC) method for switched reluctance machines (SRMs) is proposed based on the equivalent linear SRM model and the improved switching table. First, an improved switching table with only six switching states is developed based on the inductance characteristics. The adoption of this improved switching table not only reduces the computational burden by 25% but also improves the torque control performance and system efficiency at high-speed region. However, the look-up-table (LUT) based MPTC methods suffer from occupying numerous storage space. To ease this issue, the simple linear SRM model is utilized. The flux-linkage and torque equivalent transformations are introduced to address the difference between the linear and nonlinear SRM models. With these transformations, the MPTC realized on the linear SRM model is proposed. Compared to 1530 storage units consumption to store three two-dimensional (2-D) LUTs in the LUT-based MPTC methods, the proposed method only occupies 228 storage units. Experimental results on an 8/6 SRM setup verify that the proposed method effectively eliminates the commutation torque ripple with lower execution time, less storage space, and improves the torque control performance at a high-speed range compared with the conventional LUT-based method.

An extended-horizon model predictive torque control with computationally efficient implementation for PMSM drives

In this paper, an extended-horizon predictive torque control (PTC) method is proposed with computationally efficient implementation. Predictive control with extended horizon brings important advantages compared to single-horizon methods, such as better steady-state performance, reduced current THD, and lower ripples in torque and stator flux. However, the computational burden is a serious obstacle because the required calculations rise exponentially with the extension of the prediction horizon. This is due to a high number of candidate voltage vectors and also predicting the values of several machine variables for these vectors. Different approaches are employed in this work to make the control method computationally tractable. A voltage vector reduction technique is utilised that significantly decreases the total number of enumerated vectors. Moreover, the stator current prediction is eliminated in the proposed method, based on the inherent features of the PMSM. The proposed method is experimentally implemented and its good performance and advantages are verified.

Performance Analysis of Multi-Level Inverter-Fed Position Sensorless PMSM Drive Using Modified MPTC

In this article, the performance of multilevel inverter-fed position sensorless permanent magnet synchronous motor (PMSM) drive is analyzed using modified model predictive torque control (MPTC) method using two-level and three-level inverters, respectively. The MPTC method helps to control electromagnetic torque directly, which is not possible in model predictive current control, whereas the conventional model predictive torque and flux control method requires weighing factors that are similar to PI controllers used in field-oriented control or direct torque and flux control method. To simplify the control structure, most of the researchers are looking forward to weighing factorless predictive control strategies. The multilevel inverters required complex control algorithms such as space vector modulation. However, the model predictive control methods made it simple by using an embedded control structure. Furthermore, position sensorless control strategies are much needed because speed sensors are much prone to failures. Therefore, in this article, a model reference adaptive system (MRAS)–based speed sensorless approach is used to eliminate the requirement of a position sensor. The proposed multilevel inverter-fed position sensorless PMSM drive using modified MPTC is simulated in MATLAB/SIMULINK environment under different operating conditions such as under no-load, load, and change in speed. The simulation results are validated with the experimental results.

Torque ripple minimization of multi-level inverter fed PMSM drive using modified MPTC

Abstract This paper focuses on investigating the steady-state and dynamic performance of the permanent magnet synchronous motor (PMSM) drive using modified model predictive torque control (MPTC) method with multi-level inverters (MLIs). The MPTC method is simple and easy to implement for motor control as they don’t involve cascaded PI controllers unlike required the conventional controllers, such as field oriented control (FOC) and direct torque and flux control (DTFC). The proposed modified MPTC method eliminates the use of weighing factors and it is a fusion of model predictive current control (MPCC) and MPTC. Initially, the proposed modified MPTC fed PMSM drive is implemented using two-level inverter. It provides better steady-state and dynamic performance, but the ripple contents of torque, flux, and currents are high. In order to reduce the ripple contents and improve the performance of the PMSM drive, a modified MPTC is implemented using three-level inverter. The proposed modified MPTC fed PMSM drive using two-level and three-level inverters are simulated in MATLAB/SIMULINK environment under different operating conditions, such as no-load, loading, speed reversal, step change in speed operations. In order to show the effectiveness and feasibility of the proposed method, it is also validated with the experimental results.

A Low Complexity Two Step Predictive Torque and Flux Control Scheme for PMSM Drive

Nowadays, finite control set model predictive control techniques are widely used for variable speed Permanent Magnet Synchronous Motor (PMSM) drive applications due to its simple, intuitive algorithm and easy inclusion of motor objectives. In conventional predictive torque control (PTC) method, both torque and flux are controlled by a cost function optimization, where it introduces weighting factors. The selection and tuning of weighting factors is difficult since it affects torque and flux performance of PMSM drive. In this article, a two-step PTC for PMSM drive is proposed, where the flux control objective is replaced with the reactive torque component. Therefore, optimal torque and flux controlling of PMSM is achieved with similar units for both control objectives. Hence, it eliminates weighting factor tuning problem and reduces the average torque and flux ripples over a two sample instants. Additionally, the reduced voltage vector strategy is proposed, so that increased computational efficiency is achieved. To validate the proposed methodology, simulations and experimental tests are conducted for three phase PMSM drive. The obtained simulation and experimental results justify an improved torque and flux response with reduced computational burden when compared to existing PTC methods.

Weighting-Factorless Predictive Torque Control Scheme for Dual Inverter fed Open-End-Winding PMSM With Single DC Source

The conventional predictive torque control (PTC) of open-end-winding permanent magnet synchronous motor drive with single dc source deals with the problems of complex weighting factor tuning, higher ripples in zero sequence current (ZSC) and increased flux and torque ripples. To mitigate the above drawbacks, an effective weighting-factorless PTC scheme is proposed. Initially, the ZSC objective of cost function is evaluated with three types of zero sequence voltages (ZSV) of the system that are available. The magnitude of ZSV corresponding to the minimized ZSC error is selected. Six or seven candidate voltage vectors are arranged in accordance with optimized ZSV. As the ZSC imposes high impact on torque and current performance, the selected candidate voltage vectors can be used to meet the torque and flux objective. To further eliminate the flux weighting-factor, an improved incremental merit-based scheme is proposed in this article. The weighting-factors tuning is avoided in the proposed PTC method which results in reduced ZSC and improved current performance in addition to the reduced ripples in flux and torque as well as lower computational burden. The effectiveness of the proposed PTC method is experimentally verified and compared with the existing PTC methods.

A Novel Model Predictive Direct Torque Control Method for Improving Steady-State Performance of the Synchronous Reluctance Motor

Due to the particularity of the synchronous reluctance motor (SynRM) structure, a novel high-performance model predictive torque control (MPTC) method was proposed to reduce the high torque ripple and improve the performance and efficiency of the motor. First, the precise parameters of the SynRM reflecting the magnetic saturation characteristics were calculated using finite element analysis (FEA) data, and the torque and flux linkage maximum torque per ampere (MTPA) trajectory was derived by considering the saturation characteristics. Then, an MPTC model of a SynRM with duty cycle control was established, the MTPA trajectory stored in a look-up table was introduced into the control model, and the duration of the active voltage vector in one control cycle was calculated by evaluating the torque error. Finally, an experimental platform based on a SynRM prototype was built, and various performance comparison experiments were carried out for the proposed MPTC method. The experimental results show that the proposed method could reduce the torque ripple of the motor, the performance of the motor was significantly improved under various working conditions, and its correctness and effectiveness were verified.

Computationally efficient predictive torque control for induction motor drives based on flux positional errors and extended Kalman filter

This paper proposes an improved model based predictive torque control (MPTC) method based on positional error between reference and estimated stator flux vectors. The main advantages of the proposed method are: the improved computational efficiency which requires minimum hardware resources and weighting-factor-free cost function. Weighting factor is removed by using modified reference transformation, which converts torque reference into equivalent stator flux reference. Improvement in computational performance is achieved by using decreased number of voltage vectors for prediction. An admissibility criterion based on flux positional errors is introduced to reduce the number of voltage vectors. The computational time saved is utilised to incorporate extended Kalman filter for better estimation of flux and torque. The validity of the proposed method is tested on a two-level three-phase inverter fed induction motor drive with dSpace DS1104 as controller board. The dynamic response and computational cost of the proposed method is compared to other established MPTC methods. The superiority of the proposed technique is confirmed by experimental results, which show an average of 32% reduction in computational time when compared to conventional MPTC while comparable dynamic response in terms of torque ripple, flux ripple and load current harmonics, is also maintained.

Three-Vector-Based Model Predictive Torque Control for a Permanent Magnet Synchronous Motor of EVs

This article presents an optimal control strategy for a permanent magnet synchronous motor (PMSM) drive using three voltage vectors. First, in order to simultaneously control torque and flux excellently, three voltage vectors including two active vectors and one zero voltage vector are selected. Second, the duration of the three voltage vectors in one period is calculated by the principle of simultaneous deadbeat control of torque and flux. Moreover, the cost function which eliminates the weight coefficient is proposed to reduce the amount of calculation. Finally, the proposed method is compared with the one- and two-vector-based model predictive torque control (MPTC) methods both in simulation and experiment. It is found that the proposed three-vector-based MPTC can obtain better performance such as smaller torque ripple and current total harmonic distortion (THD) both in steady and dynamic states.

A new model predictive torque control strategy for permanent magnet synchronous hub motor of EVs

This paper presents an optimal control strategy for a permanent magnet synchronous hub motor (PMSHM) of EVs drive using three voltage vectors. First, in order to simultaneously control torque and flux excellently, three voltage vectors including two active vectors and one zero voltage vector are selected. Second, the duration of the three voltage vectors in one period is calculated by the principle of simultaneous deadbeat control of torque and flux. Moreover, the cost function which eliminates the weight coefficient is proposed to reduce the amount of calculation. Finally, the proposed method is compared with the one- and two-vector-based model predictive torque control (MPTC) methods both in simulation and experiment. It is found that the proposed threevector-based MPTC can obtain better performance such as smaller torque ripple and current total harmonic distortion (THD) both in steady and dynamic state.

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.