Model Predictive Direct Torque Control Research Articles

An Enhanced Model Predictive Direct Torque Control of SRM Drive Based on a Novel Modified Switching Strategy for Low Torque Ripple

An Enhanced Model Predictive Direct Torque Control of SRM Drive Based on a Novel Modified Switching Strategy for Low Torque Ripple

Explicit Model Predictive Cascade-Free Direct Force and Torque Control of Quadrotors

High-performance control of quadrotors' force and torques is the basis for achieving excellent trajectory tracking. However, the conventional method indirectly controls the force and torques by controlling four permanent magnet synchronous motors' speeds, resulting in poor dynamic performance and static deviations when pneumatic parameters mismatch. Dedicated to achieving high-dynamic and high-accuracy control of force and torques, an explicit model predictive (EMP) cascade-free direct force and torque control method is proposed. Firstly, defining force current and torque currents, whereby a new structure is proposed. Then, dynamics of the quadrotor and motor are fused to design the linear robust predictive models of force and torques. Accordingly, the EMP controller is constructed to replace the traditional cascaded speed solution module and four speed controllers uniformly. The EMP controller generates the optimal current commands directly from the force and torque commands, realizing direct control without the speed loop. The new structure makes it possible to solve the pneumatic parameter mismatch problem with an auxiliary extended state observer, obtaining offset-free control. The proposed method optimizes the dynamic performance and steady-state accuracy of force and torques and improves the system's robustness. Finally, the proposed method's feasibility is verified by experiments.

Efficiency Improvement of Permanent Magnet Synchronous Motors Using Model Predictive Control Considering Core Loss

The highway cycle is an important consideration in the EV’s new European driving cycles (NEDCs) range, as the steady-state efficiency improvement in such conditions can be greatly beneficial. In the model predictive control (MPC) of the permanent magnet synchronous motors (PMSMs), the predicted next-step feedback reference generated by the equivalent circuit model (ECM) will contribute directly to the voltage vector selection, therefore influencing the performance of the motor control. In the current MPC scheme, when the conventional ECM is applied, it only considers copper loss, and the core loss is usually disregarded. In some circumstances, such as the highway cycle of EVs, the motors are at high speed, the torque is low, and the core loss can be significant in the losses, thus affecting the accuracy of control and the efficiency of the system; hence, the introduction of core loss ECM into the MPC would be beneficial. This paper aims to investigate the steady-state efficiency improvement of a novel ECM of PMSM considering core loss ECM, and the comparison will be based on model predictive direct torque control (MPDTC) using the core loss ECM, which will be compared to MPDTC with the conventional ECM of the PMSM. The results demonstrate the proposed ECM’s efficiency improvement in various conditions, the limitations of the model and the simulation are discussed, and future work is proposed.

Dynamic Performance Enhancement of a Self-Excited Induction Generator Connected to the Grid Using an Effective Control Algorithm

The current study aims to present a comprehensive analysis of a control technique used for improving the dynamic performance of a grid connected self-excited induction generator (SEIG). All components in the control system are modelled and explained in details. The management of SEIG operation is achieved through controlling the machine side converter using a new formulated predictive voltage control scheme (PVC). The proposed (PVC) is compared to field oriented control (FOC), model predictive current control (MPCC) and model predictive direct torque control (MPDTC) systems. MPDTC and MPCC have several drawbacks like high ripple, high load commutation, and using a weighting factor in their cost function, While FOC systems depend on machine parameters for variable estimation factors, need to use PI regulators and co-ordinate transformations, which complicate the system and slow down the dynamic response. The results obtained indicate that the proposed PVC is effective, as the ripples are reduced by 43% when compared to the MPDTC-used one and by 30% when compared to MPCC. Additionally, PVC's time response is less than that of the FOC by 15%, MPCC by 9%, and MPDTC by 4%. Furthermore, PVC produces 38% fewer commutations than MPCC and 40% fewer commutations than MPDTC. Consequently, the generator's efficiency and dependability both increased as a result of its better performance.

Weighting factors design in model predictive direct torque control based on cascaded neural network

AbstractAn innovative control strategy is introduced in this paper for model predictive direct torque control (MPDTC) of a permanent magnet synchronous motor (PMSM) inverter, which is based on a cascaded neural network for the automatic selection and online tuning of weighting factors. Specifically, the lower network in the proposed two‐stage cascaded network is trained by utilizing the weighting factors and the corresponding converter parameters under a specific working condition to establish data substitution models under that working condition and determine the fitness function for obtaining the optimal weighting factors. The upper network of the cascaded network is trained using various working conditions and their corresponding optimal weighting factors calculated by the lower network, with the goal of achieving real‐time tuning of the weighting factors under variable working conditions. By employing a data‐driven method to design the weighting factors at each working condition offline, this method reduces online tuning to a single calculation in the upper network and minimizes the online calculation burden. This approach enables the weighting factors to be adjusted in real‐time according to changes in the working conditions, leading to enhanced accuracy of weighting factor optimization. Experimental results demonstrate that the proposed strategy improves the overall performance of the inverters.

Model predictive direct torque control of a three‐phase four‐switch inverter induction motor driving system

AbstractWith the development of power electronics and motor driving system, AC motor speed control or the servo system has been widely explored. Among them, the Induction Motor (IM) driving based on a three‐phase six‐switch inverter has become a mature technology for a wide range of applications. However, in some scenarios, a certain fault‐tolerant ability and further cost savings are required. Under this circumstance, the motor driving system based on the three‐phase four‐switch inverter is proposed. However, it has fewer voltage vectors, with unequal amplitudes, spatial asymmetry, and no zero vector, which will inevitably lead to the deterioration of the control performance. In this study, the operation of the four‐switch inverter IM driving is analysed in detail. The model predictive direct torque control (MPDTC) is proposed to obtain better static and dynamic characteristics. Through the simulation and experimental studies, the control algorithm is verified to drive a 2.2 kW IM. Compared with the conventional DTC, the proposed MPDTC has a rapid dynamic response and greatly reduces the torque and flux ripple in a steady state.

Different Control Techniques of Permanent Magnet Synchronous Motor with Fuzzy Logic for Electric Vehicles: Analysis, Modelling, and Comparison

This paper presents a detailed analysis and comparative study of three torque control methodologies with fuzzy logic, namely direct torque control (DTC), fuzzy direct torque control (FDTC), and model predictive direct torque control (MPDTC), for PMSM control applied to an electric vehicle. The three control strategies are designed and developed to control torque in order to achieve vehicle requirements, such as minimum torque and flux ripples, fast dynamic response, and maximum efficiency. To enhance the performance and efficiency of the overall drive, a bidirectional DC/DC buck-boost converter is connected to the Li-ion battery. In addition, a fuzzy logic controller (FLC) is used in the outer loop to control the speed of the PMSM. As a result, the tuning difficulty of the conventional proportional-integral (PI) controller is avoided and the dynamic speed response is improved. Simulation results obtained from the three control techniques establish that the proposed system via the MPDTC technique reduces the torque ripples, flux ripples, reduces the THD of the PMSM current, and achieves a faster transient response. Additionally, the MPTDC technique enabled the electric vehicle to cover the longest distance, with approximately 110.72 km in a charging cycle. The real-time simulation is developed using the RT LAB simulator, and the obtained results confirm the superiority of the MPDTC technique over conventional DTC and FDTC techniques.

Optimizing the Dynamic Performance of a Wind Driven Standalone DFIG Using an Advanced Control Algorithm

The article seeks to improve the dynamic performance of a standalone doubly fed induction generator (DFIG) which driven by a wind turbine, with the help of an effective control approach. The superiority of the designed predictive controller can be confirmed through evaluating the performance of the DFIG under other control algorithm, which is the model predictive direct torque control (MPDTC), model predictive current control (MPCC) as classic types of control. Firstly, the operating principles of the two controllers are described in details. After that, a comprehensive comparison is performed among the dynamic performances of the designed MPDTC, MPCC techniques and the predictive control strategy, so we can easily present the merits and deficiencies of each control scheme to be able to easily select the most appropriate algorithm to be utilized with the DFIG. The comparison is carried out in terms of system simplicity, dynamic response, ripples’ content, number of performed commutations and total harmonic distortion (THD). The results of the comparison prove the effectiveness and validation of our proposed predictive controller; as it achieves the system simplicity, its dynamic response is faster than that of MPDTC and MPCC, it presents a lower content of ripples compared to MPDTC and MPCC. Moreover, it can minimize the computational burden, remarkably. Furthermore, the numerical results are showing a marked reduction in the THD with a percentage of 2.23 % compared to MPDTC and 1.8 % compared to MPCC. For these reasons, it can be said that the formulated controller is the most convenient to be used with the DFIG to achieve the best dynamic performance.

Model Predictive Direct Torque Control and Fuzzy Logic Energy Management for Multi Power Source Electric Vehicles.

This paper proposes a novel Fuzzy-MPDTC control applied to a fuel cell battery electric vehicle whose traction is ensured using a permanent magnet synchronous motor (PMSM). On the traction side, model predictive direct torque control (MPDTC) is used to control PMSM torque, and guarantee minimum torque and current ripples while ensuring satisfactory speed tracking. On the sources side, an energy management strategy (EMS) based on fuzzy logic is proposed, it aims to distribute power over energy sources rationally and satisfy the load power demand. To assess these techniques, a driving cycle under different operating modes, namely cruising, acceleration, idling and regenerative braking is proposed. Real-time simulation is developed using the RT LAB platform and the obtained results match those obtained in numerical simulation using MATLAB/Simulink. The results show a good performance of the whole system, where the proposed MPDTC minimized the torque and flux ripples with 54.54% and 77%, respectively, compared to the conventional DTC and reduced the THD of the PMSM current with 53.37%. Furthermore, the proposed EMS based on fuzzy logic shows good performance and keeps the battery SOC within safe limits under the proposed speed profile and international NYCC driving cycle. These aforementioned results confirm the robustness and effectiveness of the proposed control techniques.

Model Predictive Direct Torque Control of Switched Reluctance Motors for Low-Speed Operation

In this paper, a finite-control-set model predictive direct torque control (FCS-MPDTC) method is proposed to reduce the torque ripple of a segmented switched reluctance motor (SSRM) at the low-speed stage. Firstly, the prediction dynamic model is established, and the phase torque can be predicted by detecting phase current and rotor position signals. Then, the principle of power converter and selection of voltage vectors are introduced and presented. Considering torque ripple reduction, flux tracking performance and copper losses reduction, the cost function with phase torque, the amplitude of flux linkage and phase current, is established to select optimal voltage vector to control the power converter. In addition, the torque sharing function (TSF) is employed to distribute total torque to phase torque for further torque ripple reduction. Finally, the direct torque control (DTC) is selected as the comparison method, performance of MPDTC is verified by simulation and experiment results. It can be found that the proposed MPDTC can achieve lower torque ripple and copper losses, and high robustness.

Predictive Control With Battery Power Sharing Scheme for Dual Open-End-Winding Induction Motor Based Four-Wheel Drive Electric Vehicle

For high-power electric vehicles (EVs), the drive propulsion based on induction motors is emerging as economical alternative. Compared to conventional induction motors, the open-end winding induction motor (OEWIM) requires only half the dc-bus voltage for the given torque. The EV power train based on the dual two-level voltage-source inverter (VSI)-fed OEWIM with isolated dc sources is used in this research. For uniform state-of-charge (SoC) distribution, the power flow from each isolated source needs to be controlled. A two-stage model-predictive direct torque control (MPDTC) scheme is proposed to balance the SoC of batteries by proper selection of the VSI voltage vectors. The proposed MPDTC scheme is free from weighting factor tuning and uses a ranking method to predict the optimal voltage vectors. The superiority of the proposed controller in terms of battery SoC balancing is demonstrated. The performance of the proposed MPDTC EV drive is verified for the FTP75 and HFET driving cycles under different operating conditions, both by simulation and hardware experimental tests.

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.

Performance Enhancement of a Hybrid Renewable Energy System Accompanied with Energy Storage Unit Using Effective Control System

The current study aims to present a detailed analysis of a hybrid renewable energy system used for standalone operation. The hybrid system consists of a wind-driven synchronous generator, a photovoltaic solar system, and a battery storage system. The modeling of each system component is presented and described in detail. To achieve optimal energy exploitation, the maximum power point tracking algorithm is adopted. The management of synchronous generator operation is achieved through controlling the machine-side converter using a newly formulated predictive control scheme. To visualize the advantages of the proposed control algorithm, its performance is compared with the other two traditional predictive control approaches, mainly the model predictive direct power control and model predictive direct torque control systems. An effective control scheme is also adopted to manage the power storage in the battery using a bi-directional DC/DC converter. To maintain a balanced power flow between the system units, an energy management strategy is presented. Extensive tests are carried out to evaluate the performance of the hybrid system considering variable wind speed, variable sun irradiation, and variable load profiles. The obtained results for the synchronous generator performance visualize the validity and superiority of the proposed control scheme over the other two classic controllers. The results are also validating the effectiveness of the battery storage control system and confirming the validity of the constructed energy management strategy in achieving the energy balance between the system units.

Improving the Dynamic Performance of a Variable Speed DFIG for Energy Conversion Purposes Using an Effective Control System

The present paper aims to introduce an effective control system which enhances the dynamics of a doubly fed induction generator (DFIG) operating at fixed and variable speeds. To visualize the effectiveness of the formulated control algorithm, the performance of the DFIG is evaluated using other control techniques as well. Each control algorithm is primarily described by showing its operation principles and how it is adapted to manage the DFIG’s operation. The main used control strategies are stator voltage-oriented control (SVOC), model predictive current control (MPCC), model predictive direct torque control (MPDTC), and the formulated predictive voltage control (PVC) algorithm. A detailed comparison is performed between the controllers’ performances, through which the advantages and shortcomings of each method are outlined, and finally, the most effective technique is identified amongst them. The obtained results reveal that the proposed PVC approach possesses multiple advantages such as a faster dynamic response and simpler control structure when compared with SVOC and a faster dynamic response, reduced ripples, and reduced computational burdens when compared with the MPCC and MPDTC approaches. In addition, the robustness of the proposed PVC scheme is confirmed by performing extensive performance evaluation tests considering the parameters’ variation.

Dynamic Performance Enhancement of a Renewable Energy System for Grid Connection and Stand-Alone Operation with Battery Storage

This paper introduces a new formulated control scheme for enhancing the dynamic performance of a wind driven surface permanent magnet synchronous generator. The designed control scheme is based on predictive control theory, in which the shortcomings of previous predictive controllers are avoided. To visualize the effectiveness of the proposed control scheme, the performance of the generator was dynamically evaluated under two different operating regimes: grid connection and standalone operation in which a battery storage system was used to enhance the power delivery to the isolated loads. In addition, a detailed performance comparison between the proposed controller and traditional predictive controllers was carried out. The traditional control topologies used for comparison were the model predictive direct power control, model predictive direct torque control, and model predictive current control. A detailed description of each control scheme is introduced illustrating how it is configured to manage the generator operation. Furthermore, to achieve the optimal exploitation of the wind energy and limit the power in case of exceeding the nominal wind speed, maximum power point tracking and blade pitch angle controls were adopted. A detailed performance comparison effectively outlined the features of each controller, confirming the superiority of the proposed control scheme over other predictive controllers. This fact is illustrated through its simple structure, low ripples, low computation burdens and low current harmonics obtained with the proposed control scheme.

Model Predictive Direct Torque With Fault Tolerance Control for a Permanent Magnet Synchronous Generator Based on Vienna Rectifier

This paper presents the fault tolerance of model predictive direct torque control for a permanent magnet synchronous generator under a faulty Vienna rectifier. The fault applied includes open-switch and short-switch fault in a particular active switching device of the Vienna rectifier. The measured input current is used in the proposed fault diagnosis approach to detect the switch fault’s position without any additional hardware being implemented. Whenever a switching fault occurs at any phase of the Vienna rectifier, the available switching vectors for prediction control are reduced from five to four. The feasibility and effectiveness of the proposed fault tolerance model predictive direct torque control under a faulty Vienna rectifier are demonstrated and investigated through MATLAB/Simulink. The results show that it is feasible for the proposed method to be operated under a short-switch fault with slightly higher total harmonics distortion of the input current but out of control under an open-switch fault.

Model Predictive Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) with Online Parameter Estimation Based on Extended Kalman Filter

Aiming at the torque and flux ripples in the direct torque control and the time-varying parameters for permanent magnet synchronous motor (PMSM), a model predictive direct torque control with online parameter estimation based on the extended Kalman filter for PMSM is designed. By predicting the errors of torque and flux based on the model and the current states of the system, the optimal voltage vector is selected to minimize the error of torque and flux. The stator resistance and inductance are estimated online via EKF to reduce the effect of model error and the current estimation can reduce the error caused by measurement noise. The stability of the EKF is proved in theory. The simulation experiment results show the method can estimate the motor parameters, reduce the torque, and flux ripples and improve the performance of direct torque control for permanent magnet synchronous motor (PMSM).

Evaluation of Electric Dynamic Performance of an Electric Vehicle System Using Different Control Techniques

This paper introduces a comprehensive analysis of the dynamic performance of an electric vehicle system using different control algorithms. The complete mathematical models of the electric vehicle and its motor drive system are described in a systematic manner. Furthermore, the vehicle dynamics are tested with several control topologies to investigate the most appropriate one. The adopted control topologies were the direct torque control (DTC), the model predictive direct torque control (MP DTC), and the predictive voltage control (PVC). The obtained results showed that the dynamics of the electric drive system of the vehicle under the PVC system were enhanced compared to the other control schemes.

Squirrel cage induction motor predictive direct torque control based on multi-step delay compensation

The squirrel cage induction motor direct torque control main problems due to torque and large stator flux pulsation. In this an improved model predictive direct torque control algorithm considering multi-step delay compensation is proposed. At each sampling moment, predict the stator flux linkage and torque at the next moment under each voltage vector. The optimal voltage vector deviation from the stator flux linkage reference value and torque reference value are selected as the minimum objective function. Aiming at the problem of one-shot delay in digital control systems, a multi-step predictive delay compensation measure is studied. Simulation shows that the algorithm can effectively reduce torque and stator flux pulsation, reduce current harmonic distortion, and solve the delay problem in digital systems.

Harmonic Current Suppression of Dual Three-Phase PMSM Based on Model Predictive Direct Torque Control

The traditional direct torque control (DTC) method will produce a larger harmonic current in the x, y subspace when applied to a dual three-phase permanent magnet synchronous machine (DTP-PMSM) because the voltage vector used for DTC is not equal to zero in the x, y harmonic subspace. To mitigate this problem, in this manuscript, a model predictive direct torque control (MPDTC) method is proposed to eliminate the harmonic current in DTP-PMSM. The spatial distribution characteristic DTP-PMSM voltage vector is analyzed; then, the table of vector group can be obtained according to DTC, and each vector group can be combined to obtain the zero-voltage vector in the x, y subspace. According to the cost function, MPDTC selects the vector group and obtains the optimal vector sequence combination to eliminate the harmonic current in the x, y subspace. Furthermore, the MPDTC achieves closed-loop control of harmonic currents in the x, y subspace. The MPDTC can also eliminate harmonic currents caused by other factors. The simulation results show that the method of MPDTC can effectively suppress the harmonic current of DTP-PMSM.