Model Predictive Current Control Method Research Articles

Low-Complexity Model Predictive Control for Series-Winding PMSM with Extended Voltage Vectors

This paper proposes a low-complexity model predictive current control (MPCC) strategy based on extended voltage vectors to enhance the computational efficiency and steady-state performance of three-phase series-winding permanent magnet synchronous motors (TPSW-PMSMs). Compared to conventional MPCC methods, this approach increases the number of candidate voltage vectors in the alpha–beta plane from 8 to 38, thereby achieving better steady-state performance. Specifically, the proposed method reduces the total harmonic distortion (THD) by 59%. To improve computational efficiency, a two-stage filtering strategy is employed, significantly reducing the computational burden. The number of voltage vectors traversed in one control period is reduced from 38 to a maximum of 4, achieving an 89% reduction in traversals. Additionally, to mitigate the impact of zero-sequence currents, zero-sequence current suppression is implemented within the control system for effective compensation. By combining low computational complexity, reliable steady-state performance, and real-time control capabilities, this strategy provides an efficient solution for TPSW-PMSM systems. Simulation results validate the effectiveness of the proposed method.

Model Predictive Current Control for Six-Phase PMSM with Steady-State Performance Improvement

The application of finite control set model predictive control (FCS-MPC) in six-phase permanent magnet synchronous motors (PMSMs) often faces a trade-off between computational burden and accurate voltage vector selection, as well as challenges related to harmonic components and torque generation. This paper introduces an improved model predictive current control (MPCC) method to address these problems. Firstly, 12 virtual voltage vectors are synthesized to improve torque output performance while suppressing harmonic currents. Then, to generate symmetrical switching signals and reduce switching loss, the largest basic vector used to synthesize the virtual vector is replaced by two medium vectors. Secondly, to solve the problem of the increased computational burden caused by the increase in discrete virtual vectors, a two-step vector selection method is proposed. In this method, each part is divided into several parts according to N, and the traditional cost function is also replaced by two-step functions. Different control performances can be achieved according to different values of N. Experimental results show that the proposed control scheme not only achieves stable current quality but also significantly improves steady-state performance throughout the entire speed range.

Research on an improved deadbeat model predictive current control of double three-phase open-winding permanent magnet motor

Model predictive current control has high control accuracy and good control performance, and has been widely applied in the control strategy of dual three-phase permanent magnet synchronous motors. This article proposes an improved deadbeat model predictive current control (DMPCC) method by analyzing the mathematical model of a dual three-phase open-winding permanent magnet synchronous motor. One inverter predicts and calculates the optimal voltage vector through the deadbeat model, while the other inverter calculates the optimal voltage vector through the value function rolling optimization. This method reduces the computational workload of the controller, avoids the delay problem of the traditional model predictive current control (MPCC) method, improves the system's response speed, effectively suppresses the current in the harmonic sub-plane, and reduces torque ripple. The simulation results have verified the accuracy and speed of the above control method.

Research on Decoupling Duty Cycle Optimization Control Method of a Multiport Converter for Dual-Port Direct Drive Wave Power Generation System

Dual-port direct drive wave energy power generation systems (DP-DDWEPGS) have received widespread attention due to their smooth and zero-free output power, compared to single-port direct drive wave energy power generation systems (SP-DDWEPGS) which have the disadvantage of large out-put power fluctuations. To further enhance the performance of the DP-DDWEPGS, optimal power capture control is proposed to achieve maximum power point tracking. Meanwhile, a multiport converter is applied to the DP-DDWEPGS to solve the problem caused by an excessive number of switching devices in the overall system converter. The multiport converter fulfills all the functional requirements of the DP-DDWEPGS while reducing the number of switching devices. However, switch multiplexing of the multiport converter also introduces coupling relationships between each port and the wave force exhibits time-varying characteristics, necessitating advanced control methods with superior fast-tracking capability. Therefore, in this paper, a decoupling duty cycle optimization model predictive control for DP-DDWEPGS is proposed. Based on the characteristics of switching multiplexing, NSC finite control set model predictive control (FCS-MPC) decouples the current prediction and the cost function, reduces the number of candidate voltage vectors in each operation, and shortens the operation time by 70%. To address the issues of high ripple value and increased error due to decoupling in FCS-MPC, duty cycle optimization control is added, greatly reducing the fluctuations in electromagnetic force and power of the permanent magnet linear generator (PMLG). Based on the established simulation model, the feasibility and superiority of the multiport converter and decoupling duty cycle optimization model predictive current control method are verified.

Model predictive current control method of dual three-phase permanent magnet synchronous motor based on improved SVPWM

Model predictive current control method of dual three-phase permanent magnet synchronous motor based on improved SVPWM

Loss Minimization Model Predictive Current Control for Linear Induction Motors

To further improve the operating efficiency of linear induction motor, this paper proposes a loss minimization model predictive current control method based on governable loss online calculation. First, the method derives the equivalent circuit of linear induction motor containing independent iron loss branches, and establishes the loss model containing iron loss. Second, the expressions of stator d‐axis current with governable losses and secondary flux amplitude are derived from theoretical analysis, and then a given value of stator d‐axis current is derived when the governable losses are minimal. Then, a chain observer is designed to observe the secondary flux and calculate the governable loss and stator d‐axis current under the current operation. Finally, a method is proposed to introduce the stator d‐axis current observation results into the model predictive current control, which ensures the loss suppression effect and improves the dynamic performance at the same time. In addition, this article also compares the loss minimization controllers with or without iron loss. The simulation results are consistent with the theory, and the optimal control of the secondary flux of the linear induction motor can be successfully realized, thus effectively reducing the energy loss of the motor in the dynamic operation process. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Switching Current Predictive Control of a Permanent Magnet Synchronous Motor Based on the Exponential Moving Average Algorithm

To improve the dynamic and steady-state control performance of permanent magnet synchronous motors under the three-vector model predictive current control method, this study proposes a switching current predictive control method based on the exponential moving average algorithm, which evaluates the magnitude of the change of the q-axis current slope in real time to discriminate the motor’s operating conditions and selects the optimal control method for different operating conditions. Meanwhile, the traditional three-vector model predictive current control method is improved by introducing a comparison mechanism for the q-axis current slope to select the second effective voltage vector, avoiding the secondary optimization calculation of the value function and reducing the computational complexity of the traditional method. By comparing the proposed method with the traditional three-vector model predictive current control method, the experimental results prove that the proposed method improves the system’s dynamic response and steady-state performance.

Five-Level Inverter-Fed Model Predictive Current Control for a Five-Phase PMSM Drive

In this paper, a five-level inverter-fed Model Predictive Current Control (MPCC) of Five-Phase Permanent Magnet Synchronous Motor (PMSM) Drive is proposed to enhance the performance of the drive by reducing the torque ripples. The MPCC method provides a good steady-state performance and also this method does not require a weighting factor. Among the various torque ripple minimization techniques, the multilevel inverters attracted more attention in electric drives due to rapid development in semiconductor technology. The performance analysis of a five-level (5L) inverter-fed five-phase PMSM drive is compared with two-level (2L) and three-level (3L) inverter drives under various operational conditions. The effectiveness of the proposed method is analyzed using MATLAB/SIMULINK software and also implemented using a real-time simulator.

Model-free three-vector predictive current control of permanent magnet synchronous motor based on improved sliding mode observer

Abstract To address the complexity of vector selection in the conventional predictive current control strategy and the reduction of control system robustness when the motor parameters change, this paper proposes a finite-set model-free three-vector predictive current control method for permanent magnet synchronous motor based on the improved sliding-mode perturbation observer. According to the mathematical model of a permanent magnet synchronous motor under perturbation, a new hyperlocal model is established, and the conventional vector traversal optimization method is optimized to obtain the necessary basic voltage vector by analyzing the function with one judgment. Meanwhile, the sliding mode perturbation observer is designed using the fast exponential convergence law to observe the unknown part. Finally, by experimentally contrasting the proposed technique with the conventional three-vector model predictive current control, the method suggested in this paper can successfully reduce the current pulsation and inhibit the system perturbation caused by the parameter change, ensuring the steady state performance of the motor, as demonstrated by the experimental comparison with the conventional three-vector model predictive current control method.

Improved Adaptive CCS-MPCC for Distorted Model Parameters Mitigation of IPMSM Drives

In this study, a robust adaptive model continuous-control-set model predictive current control (CCS-MPCC) method is proposed for interior permanent magnet synchronous motors (IPMSMs) considering the existing disturbances, unmodeled dynamics, and parameter variations. The novel aspect of this research is that it merges the improved dynamic, useful properties of model predictive control (MPC), which includes easy implementation and fast dynamic response, together with the most effective criterion of an adaptive controller (i.e., robustness to model uncertainties), that result in enhanced dynamic and steady-state control performance despite unknown and changing disturbances. Unlike the conventional CCS-MPCC approach that heavily depends on the accurate knowledge of the system model to attain improved control performance, the proposed method achieves satisfactory control performance (e.g., fast dynamic response, smaller steady-state error, and low stator current THD) by stabilizing the state errors to approach zero and compensating for variations in the model parameters using the designed feedback control terms and adaptive control terms, respectively. The stability of the proposed control method is ensured by the state errors asymptotically decaying to zero. The proposed approach was simulated and then implemented on a PSIM simulation tool and a prototype IPMSM test bench using TI TMS320F28335 DSP, respectively, to confirm its feasibility.

An Angle-Based Virtual Vector Model Predictive Current Control for IPMSM Considering Overmodulation

The conventional model predictive control method suffers from large current and torque harmonics due to the limited voltage vectors. To increase the control performance, in many papers, the methods of extending the predictive control set by adding virtual vectors are proposed. However, constructing virtual vectors will bring extra computational burden for vector optimization. To solve this problem, this paper proposes an angle-based virtual vector model predictive current control (ABVV-MPCC) method for interior permanent magnet synchronous motor (IPMSM). In this method, a hexagonal coordinate system is introduced to define the virtual vectors. After obtaining the composite relationship between the reference voltage vector and the adjacent basic vector, the vector optimization range is reduced from six voltage sectors to one voltage sector. And the optimal voltage vector is directly obtained by searching for the closest candidate voltage vector. After adopting the proposed method, the exhaustive optimization is avoided and the calculation burden is independent from the virtual voltage vectors number. Therefore, virtual voltage vectors number can be set arbitrarily, which greatly reduces current harmonics. Besides, a simple overmodulation dealing method by finding the optimal reachable voltage vector is proposed. And the dynamic performance under overmodulation is increased without adding additional control module.

A Computationally Efficient Model Predictive Control Method for Dual Three-Phase PMSM of Electric Vehicle With Fixed Switching Frequency

Aiming at the problems of large harmonic current, unfixed switching frequency and heavy calculation burden in the model predictive current control (MPCC) methods of dual three-phase permanent magnet synchronous motor (DT-PMSM). This paper presents a computationally efficient MPCC method with a fixed switching frequency. Firstly, the virtual voltage vectors (V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> s) are synthesized by two large vectors and two medium-small vectors. The V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> s effectively reduce the harmonic currents, and the goal of the synthetic V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> is that the harmonic current in the x-y subspace is zero. Therefore, the weighting factor of the harmonic term is eliminated in the cost function. Secondly, an optimal voltage vector selection method is designed to reduce the candidate voltage vectors in the control set and relieves the calculation burden. In addition, to further reduce ripple current and torque ripple, the duty cycle modulation method is introduced to calculate the dwell time of the V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The zero vector is inserted in the middle and both sides of the V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> s to achieve the purpose of fixing the switching frequency. Finally, the effectiveness of the proposed method is verified by experiments.

Nonparametric three-vector model predictive current control for permanent magnet synchronous motor

Multi-vector model predictive control of permanent magnet synchronous motor can effectively overcome the shortcomings of conventional single-vector model predictive control such as large motor current ripples and limited control accuracy, but its control performance is more susceptible to the influence of motor parameter perturbation. To improve the robustness of the multi-vector predictive control algorithm under the motor parameter perturbation, a novel nonparametric three-vector model predictive current control method for permanent magnet synchronous motors is proposed in this paper. Through constructing an ultra-local predictive current model based on the input and output signals of the permanent magnet synchronous motor, the influence of motor parameter perturbation on current prediction is avoided, and a disturbance observer is designed to estimate the non-modeled and perturbed parts of the ultra-local model. In addition, the direct calculation method of vector duty cycles based on current error is introduced to suppress the influence of motor parameter uncertainty on the duty cycle calculation link, which further improves the system robustness. Finally, simulations and experiments of the proposed method are demonstrated to compare with the conventional parametric three-vector model predictive current control, and the results show that the proposed control strategy can effectively suppress the disturbances caused by the motor parameters and ensure the steady-state performance during motor operation.

Generalized Fault-Tolerant Model Predictive Control of Five-Phase PMSM Drives Under Single/Two Open-Switch Faults

To address both single open-switch fault (OSF) and arbitrary two OSFs of a five-phase permanent magnet synchronous motor (PMSM) drive, a generalized fault-tolerant model predictive current control method is proposed in this article. Firstly, the postfault currents under OSFs are analyzed in the stationary frame based on the Fourier method. Then, to maintain torque output capacity and alleviate torque ripple, the optimal reference currents in fault scenarios are deduced with the principle of rotating magnetic motive force undisturbed. Further, the obtained phase currents are transformed into the rotating frame and applied in the cost function for the voltage vector optimization. Thus, the torque under post-fault operations is guaranteed and the torque ripples are suppressed. Finally, various experiments are conducted to validate the feasibility of the generalized fault-tolerant control method of five-phase PMSM drives.

Torque Ripple Reduction of Model Predictive Controlled Five-Phase PMSM Drives With Open-Circuit Faults

In this article, a fault-tolerant model predictive current control method is proposed to improve the fault-tolerant ability of a five-phase permanent synchronous magnet motor drive under single and multiple open-circuit faults operations. On the principle of the optimal current control, general reference currents are derived to suppress torque ripples. To obtain the reference currents, a Fast Fourier Transformation-based current estimation model is established. The torque ripple can be reduced by modifying the reference currents in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d3-q3</i> axis transformed from the estimation model. Finally, experimental results validate the effectiveness of the developed fault-tolerant control strategy and the robustness against operating points and motor parameter mismatches.

Effective Multivector-Operated Predictive Current Control of PMSM Drive With Reduced Torque and Flux Ripple

Model predictive current control (MPCC) is a frequently used method for the control of permanent magnet synchronous motor (PMSM). It has faster execution on modern digital platform than the previous generation micro-controllers. Conventional MPCC fails to provide satisfactory steady-state performance as a single voltage vector (VV) is applied during every sampling interval. This article proposes two novel multi-vector operated MPCC methods for reducing torque and flux ripples, as well as the computational burden. The first proposed method uses one active and one null VV; whereas, the second method uses two active and one null VV to further improve the steady-state performance. Proposed methods determine required change in stator-flux (CSF) to reduce/eliminate error in statorcurrent during every sample and VVs nearer to CSF vector are chosen as optimal VVs. The durations of optimal VVs are calculated using the components of CSF along optimal VVs. CSF, obtained using calculated durations of optimal VVs, significantly reduces torque and flux ripples. The proposed methods are effective and less complex compared to other multi-vector operated methods. The proposed methods are compared with existing two-VV and three-VV based MPCC methods and their effectiveness is experimentally verified for decreased torque and stator-flux ripple in addition to a small rise in switching frequency.

Investigation of the wave–photovoltaic-battery​ hybrid power generation platform’s control strategy

This paper proposes a wave–photovoltaic-battery hybrid power generation platform which based on the distributed DC collection and AC inverter grid structure. To be able to simplify the control structure of the power generation system and improve the anti-interference ability of the system, a one-step delay compensation model predictive current control (MPCC) method is proposed for the wave power generation rectifier unit and the perturbation and observation (P&O) fuzzy logic maximum power point tracking (MPPT) algorithm is applied in the photovoltaic power generation unit. Meanwhile, the constant power control method is used to realize the charge and discharge control of the energy storage system to achieve the grid-connected goal of tracking the load power demand. Finally, the MATLAB/Simulink software is applied to simulate the grid-connected operation characteristics of the wave energy power generation unit in continuous power generation modes, as well as the photovoltaic power generation unit at constant temperature and variable irradiance. The results illustrate that the suggested technique for controlling the power generating unit is straightforward and feasible, DC bus voltage stability effect is effective, and the inverter output satisfies the grid-connected operating requirement, which verifies the feasibility of the proposed scheme and can provide some theoretical support for the subsequent high-capacity wave–photovoltaic-battery hybrid power generation platform.

Improved Model Predictive Current Control for Linear Vernier Permanent-Magnet Motor With Efficient Voltage Vectors Selection

In order to relieve the computational burden and reduce the ripples in thrust force and current of linear vernier permanent-magnet motors, this article proposes three-vector-based model predictive current control (MPCC) with efficient voltage vectors selection. The proposed method avoids the traversal of all the possible voltage vectors. Only three nonadjacent active voltage vectors are predicted and evaluated by the cost function. Two active voltage vectors can be precisely determined according to the relationship of three cost function values. The prediction workload can be reduced with the efficient voltage vectors selection. Also, the two active voltage vectors along with the null voltage vector are used to synthesize the final applied voltage vector based on deadbeat principle. The proposed three-vector-based MPCC method extends the modulation area from discrete points to the whole regular hexagon. Thus, the ripples in thrust force and current can be minimized. Theoretical analyses and experimental results are given to verify the effectiveness of the proposed MPCC.

Robust Model Predictive Current Control of PMSM Based on Nonlinear Extended State Observer

The control performance of traditional model predictive control (MPC) usually deteriorates dramatically with the permanent magnet synchronous motor (PMSM) parameter changes in operation, leading to current harmonics and torque ripple. This article proposes a robust model predictive current control (MPCC) method based on a nonlinear extended state observer (NESO) to resolve this problem. The NESO is used to observe the current disturbance part to avoid the influence of motor parameters on the current model. Because this method is not sensitive to changes in motor parameters, the system is robust and has a low current harmonic. Second, the ESO designed in this article based on the nonlinear function has a lower observation error. In addition, this article uses the fixed coefficient method to simplify the design process. A more accurate parameter design method is proposed based on the bounded analysis of observation error. At the same time, the root locus diagram proves that NESO contributes to the stability of the current loop. Finally, hardware and simulation experiments proved that the robust MPCC based on NESO has lower harmonic content and better dynamic performance.

A Model Predictive Current Control Method for Achieving a High Efficiency and High Robustness Boost PFC Converter Operation

A Model Predictive Current Control Method for Achieving a High Efficiency and High Robustness Boost PFC Converter Operation