Three-vector Model Research Articles

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.

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.

Diffusion tensor imaging in anisotropic tissues: application of reduced gradient vector schemes in peripheral nerves

BackgroundIn contrast to the brain, fibers within peripheral nerves have distinct monodirectional structure questioning the necessity of complex multidirectional gradient vector schemes for DTI. This proof-of-concept study investigated the diagnostic utility of reduced gradient vector schemes in peripheral nerve DTI.MethodsThree-Tesla magnetic resonance neurography of the tibial nerve using 20-vector DTI (DTI20) was performed in 10 healthy volunteers, 12 patients with type 2 diabetes, and 12 age-matched healthy controls. From the full DTI20 dataset, three reduced datasets including only two or three vectors along the x- and/or y- and z-axes were built to calculate major parameters. The influence of nerve angulation and intraneural connective tissue was assessed. The area under the receiver operating characteristics curve (ROC-AUC) was used for analysis.ResultsSimplified datasets achieved excellent diagnostic accuracy equal to DTI20 (ROC-AUC 0.847–0.868, p ≤ 0.005), but compared to DTI20, the reduced models yielded mostly lower absolute values of DTI scalars: median fractional anisotropy (FA) ≤ 0.12; apparent diffusion coefficient (ADC) ≤ 0.25; axial diffusivity ≤ 0.96, radial diffusivity ≤ 0.07). The precision of FA and ADC with the three-vector model was closest to DTI20. Intraneural connective tissue was negatively correlated with FA and ADC (r ≥ -0.49, p < 0.001). Small deviations of nerve angulation had little effect on FA accuracy.ConclusionsIn peripheral nerves, bulk tissue DTI metrics can be approximated with only three predefined gradient vectors along the scanner’s main axes, yielding similar diagnostic accuracy as a 20-vector DTI, resulting in substantial scan time reduction.Relevance statementDTI bulk tissue parameters of peripheral nerves can be calculated with only three predefined gradient vectors at similar diagnostic performance as a standard DTI but providing a substantial scan time reduction.Key points• In peripheral nerves, DTI parameters can be approximated using only three gradient vectors.• The simplified model achieves a similar diagnostic performance as a standard DTI.• The simplified model allows for a significant acceleration of image acquisition.• This can help to introduce multi-b-value DTI techniques into clinical practice.Graphical

An improved three-vector coordination robust model predictive control for 3P-2L inverters

This paper introduces an improved three-vector robust coordination model predictive control for three-phase two-level (3P-2L) grid-connected inverters. Initially, an improved three-vector model predictive control methodology is suggested that broadens voltage coverage, minimizes current ripple, and employs non-contiguous vector state synthesis. Subsequently, a robust predictive control methodology is devised based on the sliding mode control theory. By optimizing the cost function in the traditional model predictive control, it allows the inverter to sustain stable functionality despite parameter discrepancies. Moreover, a sophisticated coordination controller is implemented that facilitates smooth transition between the proposed improved three-vector model predictive control and robust model predictive control, ensuring effective adaptability in diverse scenarios. Eventually, through simulation and experimentation, the efficacy of the proposed methodology is verified.

Three-Vector Model Predictive Current Control Strategy of PMSM for Marine Electric Propulsion Based on Three-Level Inverter

Three-Vector Model Predictive Current Control Strategy of PMSM for Marine Electric Propulsion Based on Three-Level Inverter

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.

Three-Vector Model Predictive Suspension Force Control for Bearingless Permanent Magnet Slice Motor

Aiming at the defects of signal detection and transmission delay leading to the reduction of control accuracy in the digital control of bearingless permanent magnet slice motor(BPMSM), a three-vector model predictive suspension force control(MPSFC) strategy is proposed. Firstly, the suspension force prediction model of the BPMSM is established and the dead-beat principle is used to calculate two active voltage vectors and a null vector duty cycle. Then, according to the principle of the minimum switching state in one control cycle, simplifying the candidate voltage vector combinations, which effectively reduces the calculation amount and inverter switching frequency. Finally, the simulation and experimental results show that the proposed three-vector MPSFC strategy of the BPMSM can not only effectively improve the control accuracy, but also implement the stable suspension of the rotor. In addition, it has good static and dynamic response characteristics.

An Improved Three-Vector-Based Model Predictive Current Control Method for Surface-Mounted PMSM Drives

The conventional model predictive control method suffers from large torque ripples and harmonic current. Moreover, the modulation range of output is limited, leading to the deterioration of performance. To address these problems, this article proposes an improved three-vector model predictive current control method for surface-mounted permanent magnet synchronous motor (SPMSM) drives. First, virtual vectors are introduced to increase the number of available voltage vectors, in which the voltage vector control set can be extended. Furthermore, a hierarchical multilevel optimization control algorithm is adopted to obtain the main control set and the extended control set. According to two different control sets, the first and second vectors can be obtained by the cost function, respectively, which can realize modulation range extension. Then, the zero vectors are added to adjust the magnitude of the output voltage vector, and the output vector action time is obtained based on the deadbeat principle. The ripples can be minimized, and the modulation range can be extended with the proposed method. Finally, a comparative analysis of both the simulation and experimental tests is investigated. The comparative investigation verifies the effectiveness and feasibility of the proposed method.

Multidisturbance Suppressed Model Predictive Direct Speed Control With Low Pulsation for PMSM Drives

Model predictive direct speed control (MPDSC), with a simple structure and without parameter tuning required, can use only one controller to constrain the speed and current of the motor at the same time. First, a direct voltage vector selection-based three-vector model predictive speed control, simplifying the optimization process, is proposed in this article, which reduces the pulsation of current and speed without increasing the computational burden compared with MPDSC. At the same time, because of the reasonable distribution of voltage vectors in the proposed three-vector method, the quality of the control and modulation has been significantly improved. Second, a multidisturbance Luenberger observer is designed to observe mechanical and electrical disturbances and to make further compensation, which reduced the influence of model mismatch, load torque, and unmodeled dynamics simultaneously and avoided the use of multiple observers, with fewer parameters and easier tuning. The experiment is presented to validate the effectiveness of the proposed method on the 7.5-kW experiment platform.

Robust three-vector model predictive torque and stator flux control for PMSM drives with prediction error compensation

Robust three-vector model predictive torque and stator flux control for PMSM drives with prediction error compensation

Three‐vector model predictive power control for three‐level T‐type rectifier based on dead‐beat control

This paper presents a three-vector model predictive power control with neutral-point voltage balance (TVMPPC-NPVB) and a dead-beat control for three-phase three-level T-type rectifier. Three vectors are adopted to reduce the current ripple, balance the neutral-point voltage and achieve unity power factor on the AC side simultaneously, while the conventional model predictive power control suffers from large current ripple, difficulty in designing the weighting factor and computational burden. In the proposed strategy, two cost functions are derived to select the optimal space vector sequence and reduce the computational burden. The action time of each vector is introduced in detail. The traditional PWM rectifier utilizes PI controller to regulate the output voltage. But PI controller tends to have a long transition time and big variation on output voltage when the load changes suddenly. The proposed dead-beat control is capable of solving these problems caused by conventional PI controller. Both simulation and experimental results are shown to confirm the effectiveness of the proposed method.

An Improved Low Switching Frequency Three-Vector Model Predictive Torque Control Strategy for Permanent Magnet Synchronous Motors

An Improved Low Switching Frequency Three-Vector Model Predictive Torque Control Strategy for Permanent Magnet Synchronous Motors

A Novel Three-Vector-Based Model Predictive Direct Power Control for Three-Phase PWM Rectifier

A novel strategy of three-vector-based model predictive direct power control (MPDPC) is proposed for three-phase Pulse-width Modulation (PWM) rectifier. Under ideal grid conditions, three-vector MPDPC is studied, and a good control effect has been achieved. However, under the unbalanced power grid condition, the traditional control strategy has some problems, such as a high harmonic content of current and large instantaneous power pulsation. A new three-vector model predictive control is proposed based on the new instantaneous power theory, and the objective function is established by instantaneous power error. The duty cycle of the selected vector is calculated by solving the optimal objective function. Under an unbalanced power grid, this paper takes a three-phase PWM rectifier as a research object, and carries out simulation and experimental tests on the traditional and new control strategies. The experimental results show that the new control strategy has lower current harmonics, and eliminates the twice grid-frequency oscillation of the grid in instantaneous power.

Research on Control Strategy of the Electric Power Steering System for All-Terrain Vehicles Based on Model Predictive Current Control

Aiming at the high demand for the torque response speed and torque pulsation of the all-terrain vehicle (ATV) Electric Power Steering (EPS) system, this paper proposes to apply the Model Predictive Current Control to the all-terrain vehicle EPS system. A Novel Three-Vector Model Predictive Current Control (N3V-MPCC) is proposed in this paper to reduce the current ripple and reduce the calculation load. Two effective voltage vectors and a zero vector are selected in the control period through only six times of prediction and application of the Sector Vector Selection method. The duration of each voltage is calculated and output to the Voltage Source Inverter (VSI). Simulation and experimental results show that, compared with PID cascade Field Oriented Control (PI-FOC), N3C-MPCC can effectively reduce the ripple current of the d-axis and the q-axis. In the simulated electric power mode, the q-axis current ripple of the N3V-MPCC is reduced by 66.67%. Experimental results show that the current ripple of the motor is reduced by 60%, and the torque pulsation is reduced by 62.5%. Therefore, N3V-MPCC has a faster current response speed and smooth steering torque.

Improved fast three vector predictive current control of permanent magnet synchronous motor

In order to improve the steady-state performance of the traditional model predictive control and reduce the amount of calculation of the control algorithm, this paper proposes a new fast three-vector predictive current control algorithm, which uses the principle of deadbeat control of the dq-axis current to calculate the reference voltage vector, and obtains the reference voltage vector position through sector judgment, combined with the three-vector model predictive control algorithm, the optimal voltage vector is directly applied to the motor, without the need to predict the behavior of the system under the voltage vector, which greatly reduces the amount of calculation. At the same time, due to the introduction of three vectors, three functions are applied in one cycle. The voltage vector greatly improves the steady-state characteristics of the system. The simulation results show that the proposed new three-vector predictive current control algorithm can reduce the amount of calculation and improve the steady-state characteristics of the system.

Critical dynamics of the nonconserved strongly anisotropic permutation symmetric three-vector model

We explore, employing the renormalization-group theory, the critical scaling behavior of the permutation symmetric three-vector model that obeys nonconserving dynamics and has a relevant anisotropic perturbation which drives the system into a nonequilibrium steady state. We explicitly find the independent critical exponents with corrections up to two loops. They include the static exponents ν and η, the off equilibrium exponent η[over ̃], the dynamic exponent z, and the strong anisotropy exponent Δ. We also express the other anisotropy exponents in terms of these.

Topological transition in a two-dimensional three-vector model: A non-Boltzmann Monte Carlo study.

Two-dimensional three-vector (d=2,n=3) lattice model of a liquid crystal (LC) system with order parameter space (R) described by the fundamental group Π_{1}(R)=Z_{2} was recently investigated based on non-Boltzmann Monte Carlo simulations. Its results indicated that the system did not undergo a topological transition condensing to a low temperature critical state as was reported earlier. Instead, a crossover to a nematic phase was observed, induced by the onset of a competing relevant length scale. This mechanism is further probed here by assigning a more restrictive R symmetry with Π_{1}(R)=Q (the discrete and non-Abelian group of quaternions), thus engaging the three spin degrees in the formation of point topological defects (disclinations). The results reported here indicate that such a choice of symmetry of the Hamiltonian with suitable model parameters leads to a defect-mediated transition to a low-temperature phase with topological order. It is characterized by a line of critical points with quasi-long-range order of its three spin degrees. The associated temperature-dependent power-law exponent decreases progressively and vanishes linearly as temperature tends to zero. The high-temperature disordered phase shows exponential spin correlations and their temperature-dependent lengths exhibit an essential singular divergence as the system approaches the topological transition point. Biaxial LC models have the required R symmetry owing to their tensor orientational orders and are suggested to serve as prototype examples to exhibit topological transition in (d=2,n=3) lattice models.

Robust model predictive current control without dead‐zone compensation

To solve the problems of large steady-state errors, high harmonic components and poor robustness of traditional current model prediction, a novel three-vector model predictive current control for permanent magnet synchronous motor is presented in this study. The vectors operating time is calculated with the combination of current increment and the unconventional modulation method is adopted to generate duty cycle containing dead zone. Adopting the current command correction link, the current deviation caused by inaccuracy parameter is reduced. Proved by experiment, novel control system not only the current steady-state error and current harmonic component are reduced, but also the parameter robustness is enhanced.

Two Phase Transitions in the Two-Dimensional Nematic Three-Vector Model with No Quasi-Long-Range Order: MonteCarlo Simulation of the Density of States.

The presence of stable topological defects in a two-dimensional (d=2) liquid crystal model allowing molecular reorientations in three dimensions (n=3) was largely believed to induce a defect-mediated Berzenskii-Kosterlitz-Thouless-type transition to a low temperature phase with quasi-long-range order. However, earlier MonteCarlo (MC) simulations could not establish certain essential signatures of the transition, suggesting further investigations. We study this model by computing its equilibrium properties through MC simulations, based on the determination of the density of states of the system. Our results show that, on cooling, the high temperature disordered phase deviates from its initial progression towards the topological transition, crossing over to a new fixed point, condensing into a nematic phase with exponential correlations of its director fluctuations. The thermally induced topological kinetic processes continue, however, limited to the length scales set by the nematic director fluctuations, and lead to a second topological transition at a lower temperature. It is argued that in the (d=2, n=3) system with an attractive biquadratic Hamiltonian, the presence of additional molecular degrees of freedom and local Z_{2} symmetry associated with lattice sites together promote the onset of an additional relevant scaling field at matching length scales in the high temperature region, leading to a crossover.

The Weighting of Cues to Upright Following Stroke With and Without a History of Pushing.

Perceived upright depends on three main factors: vision, graviception, and the internal representation of the long axis of the body. We assessed the relative contributions of these factors in individuals with sub-acute and chronic stroke and controls using a novel tool; the Oriented Character Recognition Test (OCHART). We also considered whether individuals who displayed active pushing or had a history of pushing behaviours had different weightings than those with no signs of pushing. Three participants experienced a stroke 6 months prior: eight with a history of pushing. In total, 12 participants served as healthy aged-matched controls. Visual and graviceptive cues were dissociated by orienting the visual background left, right, or upright relative to the body, or by orienting the body left, right, or upright relative to gravity. A three-vector model was used to quantify the weightings of vision, graviception, and the body to the perceptual upright. The control group showed weightings of 13% vision, 25% graviception, and 62% body. Some individuals with stroke showed a similar pattern; others, particularly those with recent stroke, showed different patterns, for example, being unaffected by one of the three factors. The participant with active pushing behaviour displayed an ipsilesional perceptual bias (>30°) and was not affected by visual cues to upright. The results of OCHART may be used to quantify the weightings of multisensory inputs in individuals post-stroke and may help characterize perceptual sources of pushing behaviours.