Model Predictive Torque Control Research Articles

Weightless Model Predictive Control for Permanent Magnet Synchronous Motors with Extended State Observer

Traditional model predictive torque control (MPTC) predicts the torque and flux values for the next time step and selects the voltage vector that minimizes the cost function as the optimal vector to apply to the inverter. This control approach is straightforward and allows for multi-objective control, but it has some issues in terms of the dynamic steady-state performance and parameter robustness. Therefore, this paper proposes a weightless model predictive control method based on an extended state observer (ESO). By designing an improved ESO to observe and compensate for motor parameter disturbances in real time, and employing a novel 2-D switching table and voltage vector sector selection diagram, the method evaluates three out of eight voltage vectors based on the torque and stator flux error signals. This reduces the computational load while increasing the number of candidate voltage vectors. Finally, a cost function without weighting factors is designed to lower the computational complexity. The simulation results show that the proposed new control method effectively reduces the torque and flux ripple and improves the current waveform compared to traditional MPTC.

Three-Vector-Based Model Predictive Torque Control for Dual Three-Phase PMSM With Torque and Flux Ripples Reduction

Three-Vector-Based Model Predictive Torque Control for Dual Three-Phase PMSM With Torque and Flux Ripples Reduction

Continuous Control Set Model Predictive Torque Control With Minimum Current Magnitude Criterion for Synchronous Motor Drives

Continuous Control Set Model Predictive Torque Control With Minimum Current Magnitude Criterion for Synchronous Motor Drives

Advanced Control Solutions for DFIG Wind Turbines: From Vector to Predictive Control

This paper investigates advanced control strategies for a doubly-fed induction generator-based wind turbine (DFIG-WT), focusing on vector control and model predictive control (MPC). Initially, it introduces the DFIG-WT structure and general control methods, then explores vector control for effective decoupled power management of active and reactive power. Despite vector control's efficiency, its limitations in handling constraints are highlighted, leading to the adoption of MPC. The study examines MPC's application in current control for three-phase two-level converters, showing its capability for fast tracking and response. Further, it explores MPC's model predictive torque control, demonstrating its proficiency in handling various requirements through a well-designed cost function. Overall, this research emphasizes MPC's adaptability and efficiency in optimizing DFIG-WT performance under varying operational conditions.

A novel weighting factor determined method of model predictive torque control for permanent magnet synchronous motor

AbstractIn the cost function of model predictive torque control (MPTC) of permanent magnet synchronous motor (PMSM), torque and flux linkage have different dimensions, and the weighting factor needs to be properly adjusted to optimise the control performance. Therefore, based on the discrete mathematical model of PMSM, the torque predictive error and flux predictive error expressions are mapped to the two‐phase static coordinate system, which is equivalent to the distance expressions from the endpoint of the voltage vector to the straight line and to the circle respectively. Because they have the same dimension, they can be directly superimposed to form the cost function proposed, which eliminates the selection process of the weighting factor. Then, the candidate voltage vector is substituted into the cost function in turn, and the optimal voltage vector with the minimum cost function is selected as the voltage input of the next control cycle. Finally, the simulation model and an experimental platform are established to compare the steady‐state and dynamic performance of the geometric solution‐based weighting factor determined method proposed with other MPTC methods.

Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications

In the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively.

Robust Model Predictive Torque Control of Interior PMSM Drives With Kalman-Based Disturbance Observer

Robust Model Predictive Torque Control of Interior PMSM Drives With Kalman-Based Disturbance Observer

Dual three-phase permanent magnet synchronous motors model predictive torque control based on zero common-mode voltage

This article proposes a multi-vector model predictive torque control strategy based on zero common-mode voltage to suppress common-mode voltage and harmonic currents in a dual three-phase permanent magnet synchronous motor. Firstly, 20 zero common-mode voltage vectors are selected from the traditional 64 basic voltage vectors. By synthesizing these 20 vectors into 6 virtual voltage vectors (VVV), control over the harmonic plane is achieved, reducing harmonic currents and eliminating common-mode voltage. Then, the control error of the flux is reduced by increasing the number of vectors to achieve the purpose of reducing the flux ripple. Finally, through duty cycle modulation, multi-vector model predictive torque control extends the modulation range to the entire sector plane. Simulation results confirm that this strategy can effectively suppress harmonic currents and common mode voltage.

An Improved Model-Predictive Torque Control of Switched Reluctance Motor Based on Sector Adaptive Allocation Technology

An Improved Model-Predictive Torque Control of Switched Reluctance Motor Based on Sector Adaptive Allocation Technology

Optimized-Sector-Based Model Predictive Torque Control With Sliding Mode Controller for Switched Reluctance Motor

Optimized-Sector-Based Model Predictive Torque Control With Sliding Mode Controller for Switched Reluctance Motor

Low Complexity Zero-Suboptimal Model Predictive Torque Control for SPMSM Drives Based on Discrete Space Vector Modulation

Low Complexity Zero-Suboptimal Model Predictive Torque Control for SPMSM Drives Based on Discrete Space Vector Modulation

The Simplified Dual-Vector Model Predictive Torque Control for Open-Winding PMSM Driving System with the Variable DC Bus Voltages

The Simplified Dual-Vector Model Predictive Torque Control for Open-Winding PMSM Driving System with the Variable DC Bus Voltages

Robust Model Predictive Torque Control with Online Parameter Identification Based on Improved Differential Evolution Extended Kalman Filter for PMSM

Robust Model Predictive Torque Control with Online Parameter Identification Based on Improved Differential Evolution Extended Kalman Filter for PMSM

An Improved Model Predictive Torque Control for Switched Reluctance Motors Based on Variable Hyperbolic Tangent Function Modeling

An Improved Model Predictive Torque Control for Switched Reluctance Motors Based on Variable Hyperbolic Tangent Function Modeling

Generalized Model Predictive Torque Control of Synchronous Machines

Generalized Model Predictive Torque Control of Synchronous Machines

A Double Vector Model Predictive Torque Control Method Based on Geometrical Solution for SPMSM Drive in Full Modulation Range

A Double Vector Model Predictive Torque Control Method Based on Geometrical Solution for SPMSM Drive in Full Modulation Range

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Model Predictive Torque Control of Switched Reluctance Machines with Torque Sharing Function and PWM Control Signals Based on Linear Polynomial Fitting

Model Predictive Torque Control of Switched Reluctance Machines with Torque Sharing Function and PWM Control Signals Based on Linear Polynomial Fitting

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.

Model Predictive Torque and Force Control for Switched Reluctance Machines Based on Online Optimal Sharing Function

Model Predictive Torque and Force Control for Switched Reluctance Machines Based on Online Optimal Sharing Function