Predictive Torque Research Articles

Current Sensor FTC Method for MPTC of Three-Phase Induction Motor Drives Without Speed Measurement

In encoderless Model Predictive Torque Control (MPTC) of three-phase Induction Motor (IM) drives, current sensors can face different electrical or mechanical faults in harsh industrial environments. In this research, single-phase current sensor Fault-Tolerant Control (FTC) method for MPTC of three-phase IM drives without speed measurement using a flux linkage observer and Model Reference Adaptive System (MRAS) algorithm is proposed. In the presented FTC method, Third-Difference operators, logic circuit module, flux linkage observer, and MRAS algorithm are utilized for fault detection, fault isolation, estimation of stator currents and fluxes, and speed estimation, respectively. In comparison with the conventional current sensor FTC methods, the proposed method can be utilized for encoderless three-phase IM drives. In order to confirm the usefulness and possibility of the proposed encoderless FTC method, experimental studies are performed for a 0.75 kW three-phase IM drive system in different situations. The achieved results demonstrate good performances of the proposed technique during both normal and faulty situations.

An Efficient Model Predictive Torque Control for Induction Motors With Flexible Duty Ratio Optimization

Efforts to improve the drive performance of classical model predictive torque control (MPTC) for induction motors (IM) have continued for decades. Existing methods often suffer from intensive computations, poor robustness, and limited quality. To resolve these problems, this article proposes an efficient MPTC for IM with flexible duty ratio optimization. In the proposed strategy, candidate voltage vectors are restricted to be in a subset of an area enclosed by voltage limit hexagon, where all the possible two-vector combinations and part of three-vector combinations are included. To quickly determine the optimal input voltage, a tailored duty ratio optimization algorithm based on voltage error minimization is designed. Experiments clearly indicate that the proposed strategy can effectively improve the steady performance with respect to classic MPTC and some other typical methods. More importantly, the proposed strategy can be conveniently extended by increasing the number of three-vector combinations to achieve steady performance close to space vector modulation-based direct torque control (SVM-DTC). It is inspiring to see faster dynamic response together with less torque ripple than SVM-DTC is provided during a large transient due to the rigorously enhanced optimality of input voltage. Finally, the robustness of the proposed scheme is experimentally validated.

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.

Investigation of Torque Ripple Behaviour for Five-Level Inverter-Fed Modified Model Predictive Torque Control-Based PMSM Drive

This paper investigates the minimization of torque ripple in Permanent Magnet Synchronous Motor (PMSM) Drive using the Modified Model Predictive Torque Control (MMPTC) with multilevel inverters. The MMPTC grabs the good steady-state performance as of Model Predictive Current Control (MPCC) and provides direct torque control as in the Model Predictive Torque Control (MPTC). The MMPTC strategy also eliminates weighting factors in the cost function, unlike conventional Model Predictive Torque and Flux control (MPTFC). The torque ripple causes vibrations and noise and produces more heat loss in the drive, which leads to focus on the torque ripple reduction. Among the various torque ripple minimization approaches, the multilevel inverter configuration clutched more attention in electric drives due to rapid development in the semiconductor technology. Analysis of torque ripple behaviour of PMSM drive with MMPTC using two-level (2L), three-level (3L), and five-level (5L) inverters is carried out under different running conditions in MATLAB/SIMULINK software tools and also implemented in real-time with the help of dSPACE1104 controller.

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.

A Desired Voltage Vector Based MPTC Strategy for PMSM With Optimized Switching Pattern

The main challenges for Finite Set-model Predictive Torque and Flux Control (MPTC) include huge calculation amount, large torque ripple, and the selection of weighting factor. To deal with these challenges and achieve the fastest torque response, a novel MPTC strategy with virtual voltage vector-based pattern is proposed in this paper. In each control period, a desired voltage vector is computed based on the maximum torque variation rate. The virtual desired voltage vector is combined by using basic voltage vectors with the minimum switching times. The switching pattern between two adjacent control periods is optimized by using a cost function in realtime. Compared with the traditional MPTC approach, the novel strategy has multifaceted improved performances: faster torque response, less calculation amount, lower switching frequency, reduced torque ripple, higher efficiency and no weighting factor. The proposed method is contrasted with traditional MPTC method and a well-known generalized multiple-vector-based MPC method via experiments.

A novel lookup table based model predictive torque control strategy using matrix converter suitable for permanent magnet synchronous motor fed marine electric propulsion drives

AbstractMatrix converter (MC), an embryonic stage AC–AC converter with dynamic potentialities which is well suitable for the efficient propulsion and better maneuverability of marine vehicles with ensured power quality. The increased number of voltage vectors in MC facilitates a vibrant control over the propulsion motor and also the compactness in MC structure is a desirable aspect. The MC fed finite control set‐model predictive control (FCS‐MPC) is an attractive and effective modern concept of motor control. However, the industrial implementation of MC fed FCS‐MPC is immature due to its necessity of powerful and expensive processors, as it employs more prediction voltage vectors which correspondingly increase the execution duration of the control algorithm. Hence, this paper presents an efficient 12 sector based lookup table for the optimization and reduction of prediction voltage vectors in the FCS‐MPC without complicating the computation processing, which is suitable for the permanent magnet synchronous motor drive embedded in the marine propulsion unit. The effectiveness of the proposed algorithm is analyzed using the MATLAB environment and its steady state, dynamic and transient performances are investigated and validated using the real time OPAL RT lab system in comparison to the conventional methods. The significant idea behind this article is to propose a superior MC fed FCS‐MPC concept for the marine propulsion motors with diminished torque and flux ripples, which can be easily implemented with economically feasible processors.

Deadbeat two-vector model predictive current control for open-winding primary permanent-magnet linear motor

The structure of open-winding motor fed by the dual-inverter can increase the winding terminal voltage of the primary permanent-magnet linear motor (PPMLM). So, it is an effective method to improve the thrust performance of the PPMLM. Based on the detailed derivation of the mathematical model of the open-winding PPMLM and the in-depth analysis of the space voltage vector distribution characteristics of the dual-inverter, a deadbeat two-vector model predictive current control (MPCC) model is proposed in this paper. First, the optimal vector of the inverter 1 is determined by judging the position of the large sector. Second, two optimal vectors of the inverter 2 is determined by judging the position of the small sector. Finally, the duty cycle of the two optimal vectors of the inverter 2 is calculated to realize the proposed deadbeat two-vector MPCC algorithm. Compared with the traditional model predictive torque control (MPTC) and MPCC strategies, the proposed algorithm has the advantages of lower current harmonics (lower 34 % and 44 % under rated conditions, respectively) and better thrust performance (higher 77 % and 61 % under rated conditions, respectively) as well as lower computational complexity. Simulation and experimental results show that the proposed two-vector MPCC has good steady-state and dynamic performances, which can reduce the current harmonics and thrust ripple, and thus improve the motor drive performance compared with the traditional method.

Dynamic Operation Loss Minimization for Permanent Magnet Synchronous Generator Based on Improved Model Predictive Direct Torque Control

During permanent magnet synchronous generator (PMSG) operation, aside from improving the rapid response of torque and reducing the rippling of torque and flux to guarantee the operational reliability, the loss of the PMSG itself cannot be ignored either. Aiming at this problem, an improved model predictive direct torque control (MPDTC) method is proposed, which suppresses the torque ripple and achieves PMSG efficiency optimization. Firstly, based on the conventional MPDTC and the loss model of PMSG, the predicted stator active current is determined, which is utilized for obtaining the predicted torque. Secondly, combined with the generator loss model, the set value of the stator energetic current is obtained; combined with the wind power system maximum power point tracking method, the set value of generator torque is obtained. Thirdly, by converting the torque control error and current control error into a per unit value, the influence of the weight coefficient in the cost function on the control effect is reduced. Finally, the key results verified the effectiveness and superiority of the proposed control scheme by simulation and experiment.

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.

Indirect predictive torque control for switched reluctance motor in EV application

This paper presents a novel indirect predictive control method to reduce the torque ripple of switched reluctance motor drive in electric vehicle (EV) application. The proposed indirect predictive torque control algorithm includes two parts: torque inverse model and robust predictive current controller. The torque inverse model adopts the form of adding torque error compensator to the simple linear model to realize the accurate mapping from torque to current, which avoids the complex calculation of the traditional torque inverse model. Then the predictive current controller is designed to traverse all candidate switching states and use the switching state of minimizing the cost function as the optimal output. Further, the modeling error, parameter variations and sampling error are equivalent to a total disturbance, which are compensated by the developed disturbance observer to improve the robustness of the predictive control. The proposed predictive control scheme indirectly realizes the instantaneous torque control through the accurate tracking of current, which is easy to implement, and is suitable for driving electric vehicles. Simulation experiments are performed to verify the effectiveness of the proposed predictive control algorithm.

Encoderless Parallel Predictive Torque Control for Induction Machine Using a Robust Model Reference Adaptive System

This paper proposes a robust model reference adaptive system (MRAS) estimator incorporating online parameter identification algorithm for parallel predictive torque control (PPTC) scheme. In contrast to conventional predictive torque control (PTC), the proposed PPTC is designed to facilitate the determination of weighting factor that modifies the relative importance of the control objectives, by simultaneously evaluating the torque and flux tracking error terms. The weighting factor is fine-tuned via an adaptive selecting mechanism, to obtain the attractive feature of reduced torque and current tracking errors. Besides, an encoderless MRAS-based estimator is employed for rotor speed and stator flux estimation to reduce the hardware complexity. However, this proposed encoderless PPTC algorithm still suffers from weak robustness against inevitable disturbances caused by parameter variations, which is an important issue to be further investigated. In this paper, an encoderless MRAS estimator combined with online parameter identification algorithm is proposed as an effective solution to accurately estimate the rotor speed and predicted stator flux-linkage, that improves robustness against mismatched parameters for the proposed encoderless PPTC scheme. The feasibility of the proposed algorithm is verified through the obtained experimental measurements, that achieves precise speed tracking capability as well as reduced torque and current tracking errors.

Improved Fault-Tolerant Model Predictive Torque Control of Five-Phase PMSM by Using Deadbeat Solution

This paper proposes an improved fault-tolerant model predictive torque control (MPTC) for a five-phase permanent magnet synchronous motor based on deadbeat solution, which offers the reduced computation burden, good steady-state performance and simple control structure. Under the open-circuit fault condition, the virtual voltage vectors (VVV) which can restrain the harmonics work as the candidate vectors. The deadbeat solution is adopted to predict the reference voltage vector. The optimal VVV can be selected directly and rapidly according to the sector of the reference voltage vector. Hence the computation burden can be reduced largely because the process of testing all candidate VVVs is avoided. In order to reduce the error between the reference voltage vector and the selected VVV, a null vector is introduced by a simple geometric principle. Then, the duration of the selected VVV can be obtained. Therefore, the steady-state performance is enhanced because of the deadbeat solution and the multi-vectors in each control period. The experiments are presented to verify the proposed fault-tolerant MPTC.

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.

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.

Model Predictive Control based Direct Matrix Converter fed Permanent Magnet Synchronous Machine drives for Traction and Electric Mobility Applications

There have been extensive research works going on electric mobility but most of these work and the existing electric mobility systems are battery-based DC systems. In some applications of electric mobility like traction and advanced technologies like electromagnetic induction charging, AC fed systems are employed due to the innate qualities of AC power transmission. Almost all the electric mobility systems we use are AC induction or permanent magnet machines. The conventional electric mobility systems including traction having AC as their energy source use two-stage conversion i.e. A fixed AC is converted to a fixed or variable DC link using a rectifier and finally, an inverter provides a variable AC in terms of frequency and magnitude according to the control algorithm. The two-stage conversion has its pros and cons but Matrix Converter (MC) will be a suitable and efficient alternative for AC fed AC motor drives. In the case of traction and other electric mobility applications, the load torque demand plays a significant role. The predictive control technique provides a suitable solution for these kinds of special drive applications due to their selective parameter control ability. Implementation of predictive control using a matrix converter is more effective than the conventional inverter fed drives, owing to the increased viability of matrix converter switching configurations. This paper discusses the mathematical implementation and comparison of Predictive Current Control (PCC) and Predictive Torque Control (PTC) with and without weighing factor for AC fed electric mobility applications. The efficacy of both the model predictive control techniques in concern of execution time, steady-state, transient, and dynamic conditions are analysed and validated along with the influence of diverse control variables in the cost function.

A computationally efficient torque and flux ripple reduction by active vector optimization approach using direct torque control–based model predictive torque control for matrix converter–fed permanent magnet synchronous motor

The Matrix Converter–fed Finite Control Set–Model Predictive Control is an efficient drive control approach that exhibits numerous advantageous features. However, it is computationally expensive as it employs all the available matrix converter voltage vectors for the prediction and estimation. The computational complexity increases further with respect to the inclusion of additional control objectives in the cost function which degrades the potentiality of this technique. This paper proposes two computationally effective switching tables for simplifying the calculation process and optimizing the matrix converter active prediction vectors. Here, three prediction active vectors are selected out of 18 vectors by considering the torque and flux errors of the permanent magnet synchronous motor. In addition, the voltage vector location segments are modified into 12 sectors to boost the torque dynamic control. The performance superiority of the proposed concept is analyzed using the MATLAB/Simulink software and the real-time validation is conducted by implementing in the real-time OPAL-RT lab setup.

Improve Performance of Induction Motor Drive using Weighting Factor approach-based Gravitational Search Algorithm

ABSTRACT The weighting factor adjustment limits the extensive application of conventional model predictive torque control in practice. In this paper, a model predictive torque control strategy with gravitational search algorithm (GSA) is proposed. The detail of an adaptive torque variable is introduced to control the flux linkage of the induction motor, and a modified value function is constructed by using electromagnetic torque and image torque. The proposed model uses two control variables to replace the torque and flux variables in the conventional model predictive torque control and improve the control efficiency. The simulation and experimental results show that the proposed method achieves a fast dynamic performance compared to the conventional model and reduces the average switching frequency. Besides, the system has high response speed, high accuracy, stability, and a certain application value.

Predictive direct torque control of a rotor-side matrix converter driven grid connected DFIG with torque and flux ripples minimisation criterion

Predictive direct torque control of a rotor-side matrix converter driven grid connected DFIG with torque and flux ripples minimisation criterion

Sliding Model-Based Predictive Torque Control of Induction Motor for Electric Vehicle

This article presents a sliding model-based predictive torque control (S-PTC) of an induction motor for the electric vehicle application. In this work, a battery-operated bidirectional converter fed induction machine-driven power train system is considered. In the conventional predictive torque control (C-PTC), only the machine model is used for the prediction of stator flux, current, and electromagnetic torque. Unlike the C-PTC, the presented control scheme offers a nonlinear switching feedback term in the prediction equation to overcome the model uncertainties, disturbances, and variation in motor parameters. The gains of the feedback terms are selected based on the Lyapunov stability criterion. Adaptive full order observer is used for the estimation of speed and rotor flux. This method is implemented and the speed response and torque ripple of the motor are analyzed for various operating modes of the vehicle. The robustness of the control is verified for variation in motor parameters and a comparison is shown to justify the achievement of objectives through S-PTC than with C-PTC. The control algorithm for the developed system is verified experimentally through the laboratory prototype.