Model-free Predictive Current Control Research Articles

Torque-Ripple-Mitigated Model-Free Predictive Current Control With Extended Control Set for PMSMs

Torque-Ripple-Mitigated Model-Free Predictive Current Control With Extended Control Set for PMSMs

Model-Free Predictive Current Control for Dual Three-Phase PMSM Drives With an Optimal Modulation Pattern

Model-Free Predictive Current Control for Dual Three-Phase PMSM Drives With an Optimal Modulation Pattern

Improved Model-Free Predictive Current Control Method for SPMSM With an Overmodulation Scheme

Improved Model-Free Predictive Current Control Method for SPMSM With an Overmodulation Scheme

Model-Free Predictive Current Control Using Extended Affine Ultralocal for PMSM Drives

Model-Free Predictive Current Control Using Extended Affine Ultralocal for PMSM Drives

Double-Vector Model-Free Predictive Current Control Method for Voltage Source Inverters With Sampling Noise Suppression

Model-free predictive current control (MFPCC) methods based on look-up tables (LUTs) have been widely applied in voltage source inverters (VSIs) due to their simple implementation and excellent robustness. However, the performance of the VSIs is affected by the voltage vector used, accuracy of current gradients and sampling noise. In order to improve these, a new double-vector MFPCC method is proposed in this paper. First, a synthetic voltage vector composed of double vectors is applied in each control period to reduce current ripples, where the durations of the double vectors are calculated using predicted current gradients. Next, an extended LUT is designed by using the current gradients of the applied synthetic voltage vectors to update all current gradients of eight basic voltage vectors, so as to increase the accuracy of current gradients. Then, the effect of the sampling noise on MFPCC is analyzed in detail and suppressed by a sliding-mode observer (SMO) for the VSIs. Finally, simulation and experimental results have verified the effectiveness of the proposed method.

Model-Free Predictive Current Control of PMSM Drives Based on Variable Sequence Space Vector Modulation Using an Ultra-Local Model

Model-Free Predictive Current Control of PMSM Drives Based on Variable Sequence Space Vector Modulation Using an Ultra-Local Model

Adaptive Ultralocalized Time-Series for Improved Model-Free Predictive Current Control on PMSM Drives

Adaptive Ultralocalized Time-Series for Improved Model-Free Predictive Current Control on PMSM Drives

Model-Free Predictive Current Control of SPMSM Based on Enhanced Extended State Observer

Conventional model-free predictive current control (MFPCC) using an extended state observer (ESO) based on ultra-local model significantly decreased the sensitivity to partial motor parameters. However, the performance and stability of ESO will be degraded in the face of amplified high-frequency (HF) perturbation caused by inductance parameter mismatch. To solve the above problem, an enhanced ESO-based MFPCC method is proposed in this article. On the basis of constructing the discrete equation of ESO, an accurate and fast calculation of inductance parameter is proposed, which is real-time convergence to the actual value by deadbeat control principle, and fed back to the lumped disturbance and current observation calculation. It effectively eliminates the effects of HF perturbation on ESO. Simultaneously, the inductance and compensated lumped disturbance obtained from ESO are provided to the MFPCC command voltage calculation module, further improving the parameter robustness of MFPCC. The stability of the proposed ESO is ensured by the design of observer gain. Experiments are carried out on 1kW surfaced permanent magnet synchronous machines (SPMSM) test bench. Compared with the conventional ESO-based MFPCC method, the proposed method is superior in dynamic response, steady-state performance, and parameter robustness.

FCS-MPC for Three-Level NPC Inverter-Fed SPMSM Drives Without Information of Motor Parameters and DC Capacitor

The conventional finite control set-model predictive control (FCS-MPC) suffers from poor robustness against model mismatches. This paper presents an improved FCS-MPC with a reconstructed mathematical model to predict current variations without using a lookup table (LUT) or motor parameters. The model coefficients and current variations related to different voltage vectors can be updated during each control period. As a result, the prediction error is significantly reduced at low switching frequency when compared with the prior LUT-based model-free predictive current control (MFPCC). Additionally, the tracking accuracy of the proposed method at high speeds is improved due to the elimination of approximation error. Furthermore, a simple scheme is developed to suppress neutral-point-potential drift without knowledge of the dc-bus capacitor. Simulation and experimental tests, along with comparisons with prior arts carried out on a three-level inverter-fed surface-mounted permanent magnet synchronous motor drive, confirm the superiority of the proposed method.

Model-free predictive current control based on improved sliding mode disturbance observer

The motor parameters of permanent magnet synchronous motor (PMSM) can be mismatched in operation due to temperature variations and magnetic saturation. The parameter mismatch leads to a significant decrease in the robustness of traditional deadbeat predictive current control (DPCC), which leads to issues such as current static error and motor vibration noise. In this paper, a model-free predictive current control (MFPCC) method based on an improved sliding mode disturbance observer (SMDO) was proposed. The method estimated the total disturbance of the system using the improved SMDO, which could effectively suppress sliding mode chattering and accelerate the convergence speed of current errors. Subsequently, the predicted control voltage was calculated using a hyper-local model and compensated for the time delay. The experimental results demonstrated that the improved MFPCC-SMDO method generated lower motor noise compared to the traditional DPCC method when the controller’s inductance parameters are mismatched.

Finite control set model-free predictive current control of permanent magnet synchronous motor

This thesis proposes finite control set model-free predictive current control (FCSMFPCC) algorithm of permanent magnet synchronous motor (PMSM). To address issue of degraded performance during motor operation due to parameter mismatch, a model-free predictive current control arithmetic is devised, which utilizes ultra-local model (ULM) of PMSM. This proposition takes sum of known and sealed disturbances of the system as an unknown quantity, and uses sliding mode observer (SMO) to take stock of sealed quantity. To address the issue of operation delay in predictive control, a two-step method is used to make up for the system. When using price function to choose the first-rank voltage vector, vector selection optimization link is added, which effectively simplifies the algorithm while optimizing the inverter switching action. The simulation outcomes highlight the advantages of the described control algorithm in terms of better dynamic response performance and stronger robustness.

Current Harmonics Minimization of Model-Free Predictive Current Control for PWM Rectifiers Based on Hybrid SVM

Current Harmonics Minimization of Model-Free Predictive Current Control for PWM Rectifiers Based on Hybrid SVM

An Improved Model-Free Predictive Current Control for PMSM Under Low-Speed Condition

An Improved Model-Free Predictive Current Control for PMSM Under Low-Speed Condition

An Improved Torque Enhancement Strategy for Dual Three-Phase PMSM Based on Model-Free Predictive Current Control

An Improved Torque Enhancement Strategy for Dual Three-Phase PMSM Based on Model-Free Predictive Current Control

Double-Vector Model-Free Predictive Current Control for PMSMs with Influence Rejection of DC Voltage Mismatch

Double-Vector Model-Free Predictive Current Control for PMSMs with Influence Rejection of DC Voltage Mismatch

Improved Triple-Voltage-Vector Model-Free Predictive Current Control for Synchronous Reluctance Motor Drives

Improved Triple-Voltage-Vector Model-Free Predictive Current Control for Synchronous Reluctance Motor Drives

Model-Free Predictive Current Control of DTP-PMSM Based on Ultra-Local Model Considering Back EMF Harmonics

Model-Free Predictive Current Control of DTP-PMSM Based on Ultra-Local Model Considering Back EMF Harmonics

Model-Free Predictive Current Control of PMSM Using Modified Extended State Observer

Model-Free Predictive Current Control of PMSM Using Modified Extended State Observer

Adaptive Inertia Observer-based Model-Free Predictive Current Control for PMSM Driving System of Electric Vehicles

Adaptive Inertia Observer-based Model-Free Predictive Current Control for PMSM Driving System of Electric Vehicles

Variable Switching Point Model-Free Predictive Current Control Strategy for SRM

Variable Switching Point Model-Free Predictive Current Control Strategy for SRM