Model Predictive Torque Control Method Research Articles

Predictive Torque Control of the Vehicle’s Permanent-Magnet Synchronous-Motor Model Based on Multi-Objective Sorting

The permanent-magnet synchronous motor (PMSM), with the advantages of low energy consumption and stable operation, is considered a green power source to replace gasoline engines. Motor control is the core problem of the electric-drive system, so it is important to study the high-performance motor control algorithm. The traditional PMSM control strategy has problems such as torque pulsation, large overshoot, and parameters which are not easy to adjust. This work proposes a new model-predictive torque control (MPTC) based on multi-objective ranking for these issues. The Romberg observer was utilized to accurately estimate motor flux and torque across a wide range of speeds and ensure optimal performance of the MPTC. The optional voltage vectors were classified using graph theory. The model’s cost function was optimized and the control delay caused by hardware processing was compensated by a modified Euler method. A multi-objective ranking method was used to avoid the offline selection of MPTC weight coefficients. Additionally, one ranking method was used to reduce the complexity of the algorithm for multiple objectives. Based on the simulation results, the newly proposed MPTC method, when compared with traditional approaches, reduced the total harmonic distortion from 2.78% to 2.26%. Torque ripple decreased by approximately 58.4%, and the switching frequency was reduced by 3.05%, lowering the inverter’s switching losses. Therefore, the newly proposed MPTC had faster torque response, reduced computation time, and less torque pulsation, which further improved the dynamic performance of the permanent-magnet synchronous motor.

An Improved Model Predictive Torque Control for Switched Reluctance Motors With Candidate Voltage Vectors Optimization

This paper proposes an improved model predictive torque control (MPTC) strategy for switched reluctance motor (SRM) drives with candidate voltage vectors (CVVs) optimization, which can suppress the torque ripple effectively and enhance the system efficiency. This MPTC method is improved by three aspects. Firstly, the flux linkage calculation is removed compared to conventional MPTC. Secondly, the electric cycle of SRM is divided into six sectors based on the ideal torque contribution profile and the commutation region of the motor is redefined. Finally, CVVs of each sector are adopted based on phase torque characteristics and the total number of CVVs is reduced to 2 or 3 at each control period. The cost function is designed for the torque ripple suppression and copper loss minimization by selecting the optimal voltage vector from CVVs. This improved MPTC method avoids mass useless computation compared to conventional MPTC and no longer relies on hysteresis control loops compared to DTC method. In the simulation and experimental verification on a three-phase 12/8 SRM, the proposed MPTC method effectively reduces the torque ripple, improves the torque–ampere ratio and system efficiency, and improves dynamic response compared to DTC method.

A Novel Model Predictive Torque Control of SRMs With Low Measurement Effort

This article presents a low-measurement effort and less storage space but an effective method to reduce the torque ripple. First, a torque-balanced measurement method is presented to obtain the flux-linkage characteristics at four torque-balanced positions. Then, a four-order Fourier series model is proposed to describe the entire flux-linkage and torque characteristics. To reduce the storage space, a Polynomial-Fourier series model is proposed to describe the torque and current model from the rotor position and flux-linkage. Based on the proposed Polynomial-Fourier series model, a novel model predictive torque control (MPTC) is implemented to minimize the torque ripple with flux-linkage-based torque estimation. Experimental results show the proposed method can effectively reduce torque ripple with lower measurement effort and less storage space compared with the traditional MPTC method. Furthermore, the key issues of model predictive control, such as error analysis, weight coefficients selection, and magnetic saturation, are discussed in detail. The proposed method provides a low-effort and convenient way to implement the advanced control of SRMs in the industry application.

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.

Model Predictive Torque Control for a Dual Three-phase PMSM Using Modified Dual Virtual Vector Modulation Method

Single voltage vectors applied in the conventional model predictive torque control (MPTC) for multiphase motors do not only suffer from serious torque and stator flux ripples but also cause the large harmonic current. To address the aforementioned challenges, an MPTC using a modified dual virtual vector modulation method is proposed to improve the operational performance of a dual three-phase permanent magnet synchronous motor. Virtual voltage vectors are synthesized as the candidate control set to restrain the harmonic current. A transformation method is introduced to consider both the stator flux and torque in the duty cycle modulation. The torque and stator flux ripples are simultaneously reduced by addressing the limitations of nonuniform units. Furthermore, the null voltage vector is then inserted to expand the modulation range and improve the steady-state performance. Moreover, the sawtooth carrier is adopted to address the challenge of the asymmetric switch sequence caused by the modified modulation. Finally, the experimental results are presented to verify the effectiveness and superiority of the proposed MPTC method.

Zero Common-Mode Voltage Model Predictive Torque Control Based on Virtual Voltage Vectors for the Dual Three-Phase PMSM Drive

For the multiphase motor drive system, current harmonic components in stator windings and the common-mode voltage are the main factors affecting the control performance. In this paper, a novel model predictive torque control (MPTC) considering both harmonic and common-mode voltage suppression is proposed to improve the control performance. First, with the vector space decoupling (VSD) theory, 12 virtual voltage vectors are constructed based on the principle that the amplitudes of the vectors in the x-y harmonic subplace are zero to achieve the harmonic suppression. Then, these 12 virtual vectors are further simplified to six vectors to realize the common-mode voltage suppression, and they are taken as the candidate vectors to be rolling optimized to output the optimal voltage vector. This novel MPTC strategy can reduce the computational burden and avoid the weight factor design for the traditional multi-objective optimization. The effectiveness of this novel MPTC strategy was verified by simulations and experiments in comparison with normal MPTC methods, and it can save over 44% of the execution time of the traditional MPTC method and provide better suppression of the harmonic current components and the common-mode voltage.

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.

Model Predictive Based Direct Torque Control for Induction Motor Drives by Sole-Evaluation of Two Parameter Independence Duty Ratios for Each Voltage Vector

Due to importance of duty cycle based model predictive torque control (MPTC), it has been accepted as an effective and common alternative control strategy for industry. Since its major drawback is great dependency on duty ratio parameter, the robustness and the complexity of control system has been respectively decreased and increased. A duty-ratio set considering limited members can be replaced with continuous and dependent parameters form duty cycle relationship. Hence, this paper proposes a new and applicable discrete duty cycle control strategy to reduce both the torque and flux ripples appeared in MPTC method. In the proposed method, after preselecting around half of all admissible voltage vectors based on the torque sign or speed variation and the stator flux position, two duty ratios have just been selected for the preselected active voltage vectors. These two duty ratios are achieved based on the equivalent voltage concept with independent motor parameter. Then, the model predictive control method is employed to select the optimum active voltage vector and its relevant duty ratio among the reduced feasible voltage vectors which reduces both the flux and torque ripples, especially in low speed ranges. The simulation results have confirmed the performance of the proposed method in order to reduce the torque and flux ripples, and the experimental results have accordingly validated its performance. Since the number of voltage vectors and duty ratios is significantly decreased, the execution time of the proposed method is further reduced, and virtually worked with a constant switching frequency for all speed ranges.

Model Predictive Torque Control of a Hybrid Excited Axial Field Flux-Switching Permanent Magnet Machine

Hybrid excited axial field flux-switching permanent magnet (HEAFFSPM) machine is a novel stator excitation hybrid excited synchronous machine, which combines the advantages of the axial field flux-switching permanent magnet machine and wound field machine. In this paper, two model predictive torque control (MPTC) methods with flux-adjusting strategy implementation for the HEAFFSPM machine, including the MPTC and MPTC with duty cycle control (MPTC-DCC) by optimizing the active voltage vector duration to reduce the torque and flux ripples, are proposed and comparatively investigated. Based on the theoretical analysis, a discrete-time model of the HEAFFSPM machine is established. Considering the flux-enhancing/-weakening strategies, the different multi-objective cost functions are designed. The multiple right weight coefficients are analyzed and chosen to optimize the operating performance of the drive system. The results indicate that the proposed MPTC-DCC method reduces the torque and flux ripples significantly compared with MPTC method, and the drive system has better steady performance. Meanwhile, the load capability in the whole speed region is improved and the constant power operating range is broadened by using the proposed flux-adjusting strategy.

MPTC for PMSMs of EVs With Multi-Motor Driven System Considering Optimal Energy Allocation

This paper presents a compound propulsion system with a high-speed permanent-magnet synchronous motor (PMSM) and two in-wheel motors for electric vehicles (EVs). In this paper, the longitudinal dynamic model of EVs is first presented. Then traction distribution ratio α is introduced to express the traction distribution between the front and the rear axles. Moreover, the function of power consumption concerned with the traction distribution ratio α is established. Therefore, the α that minimizes the power consumption function is selected as the optimal traction distribution ratio. To improve the performance of motor controllers, the model predictive torque control (MPTC) method is employed for high-speed and in-wheel motor drives. Experimental comparison with field-oriented control (FOC) shows the advantages of MPTC in dynamic response. Finally, experimental comparisons and hardware-in-loop (HiL) tests are presented to verify the MPTC method and the proposed energy allocation method, respectively.

Low-Complexity Model Predictive Torque Control Method Without Weighting Factor for Five-Phase PMSM Based on Hysteresis Comparators

An improved model predictive torque control (MPTC) method without weighting factors is proposed for five-phase permanent magnet synchronous machine (PMSM) drives, which aims to reduce heavy computation complexity, and the low-order current harmonics existing in harmonic subspace in the traditional finite-control-set (FCS) MPTC. In the proposed MPTC scheme, a modified control set consisting of virtual voltage vectors has been defined and adopted with two hysteresis comparators. Duty ratio optimization is also introduced to reduce torque ripples and eliminate weighting factors in the predefined cost function. In addition, a digital delay compensation solution of the proposed MPTC scheme is adopted to improve the control precision. Finally, a series of hardware-in-loop real-time simulation and experimental tests is achieved to compare the performances of the proposed and traditional FCS-MPTC schemes. Simulation and experimental results have verified the effectiveness of the proposed MPTC scheme.

A Simplified Model Predictive Control for a Dual Three-Phase PMSM With Reduced Harmonic Currents

The conventional model predictive control for dual three-phase motors only evaluates the largest voltage vectors to alleviate the computation burden, but at the costs of large current harmonics and torque ripple. This paper presents a simplified model predictive torque control (MPTC) for an asymmetrical dual three-phase permanent magnet synchronous motor (PMSM), which can significantly improve the machine performances. First, the 36 active voltage vectors in the outer three layers are considered with the proposed control method. In order to suppress the stator harmonic currents, the voltage vectors are preselected based on a switching table according to the flux position and torque deviation in the $\alpha \hbox{-} \beta, \text{x--}y$ subspace. Simultaneously, the number of prediction voltage vectors can also be reduced, thus effectively decreasing the computation time. In particular, with the proposed MPTC method, the harmonic currents can be effectively suppressed. Furthermore, the computational burden can be considerably alleviated. In the meantime, the merits of the conventional MPTC such as fast dynamic response and intuitive implementation are preserved. Finally, both simulation and experimental results are performed to verify the validation of the proposed MPTC methodology when comparing with the conventional MPTC for the dual three-phase PMSMs.

Deadbeat Model-Predictive Torque Control With Discrete Space-Vector Modulation for PMSM Drives

This paper proposes an alternative strategy of finite-control-set model-predictive torque control (MPTC) to reduce the computational burden and the torque ripple and decouple the switching frequency from the controller sampling time. An improved discrete space-vector modulation (DSVM) technique is utilized to synthesize a large number of virtual voltage vectors. The deadbeat (DB) technique is used to optimize the voltage vector selection process, avoiding enumerating all the feasible voltage vectors. With this proposed method, only three voltage vectors are tested in each predictive step. Based on the improved DSVM method, the three candidate voltage vectors are calculated by using a novel algebraic way. This new strategy has the benefits of both the MPTC method and the DB method. The effectiveness of the proposed strategy is validated based on a test bench.

낮은 스위칭 주파수로 구동되는 유도전동기의 모델예측토크제어

When medium and large induction motors are driven by 2-level inverters with low switching frequency, induction motors provoke deteriorated performances resulted from large torque ripples, flux ripples, and large current distortion. Model predictive torque control(MPTC) for a fast torque control of induction motors is also suffered from large torque ripples when the induction motors are fed by 2-level inverters that are based on 6 active voltage vectors with low switching frequency restricted. To solve this problem, this paper proposes a new MPTC method based on both a 12 active voltage vector and an optimized duty ratio calculation. The proposed control strategy illustrates its effectiveness under the various operating conditions through simulation works.

Model predictive torque control with an extended prediction horizon for electrical drive systems

This paper presents a model predictive torque control method for electrical drive systems. A two-step prediction horizon is achieved by considering the reduction of the torque ripples. The electromagnetic torque and the stator flux error between predicted values and the references, and an over-current protection are considered in the cost function design. The best voltage vector is selected by minimising the value of the cost function, which aims to achieve a low torque ripple in two intervals. The study is carried out experimentally. The results show that the proposed method achieves good performance in both steady and transient states.