Model-free Predictive Current Control Research Articles

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

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

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

Model-Free Predictive Current Control of Permanent Magnet Synchronous Motor Based on Estimation of Current Variations

Model-Free Predictive Current Control of Permanent Magnet Synchronous Motor Based on Estimation of Current Variations

An Improved Model-Free Predictive Current Control for PMSM Drives Based on Current Circle Tracking Under Low-Speed Conditions

An Improved Model-Free Predictive Current Control for PMSM Drives Based on Current Circle Tracking Under Low-Speed Conditions

Model-Free Predictive Current Control of Five-Phase PMSM Drives

Model predictive control is highly dependent on accurate models and the parameters of electric motor drives. Multiphase permanent magnet synchronous motors (PMSMs) contain nonlinear parameters and mutual cross-coupling dynamics, resulting in challenges in modeling and parameter acquisition. To lessen the parameter dependence of current predictions, a model-free predictive current control (MFPCC) strategy based on an ultra-local model and motor outputs is proposed for five-phase PMSM drives. The ultra-local model is constructed according to the differential equation of current. The inherent relation between the parameters in the predictive current model and the ultra-local model is analyzed in detail. The unknowns of the ultra-local model are estimated using the motor current and voltage at different time instants without requiring motor parameters or observers. Moreover, space vector modulation technology is employed to minimize the voltage tracking error. Finally, simulations and experiments are conducted to verify the effectiveness of the MFPCC with space vector modulation. The results confirm that the proposed method can effectively eliminate the impact of motor parameters and improve steady-state performance. Moreover, this control strategy demonstrates good robustness against load variations.

Robust three‐voltage‐vector‐based model‐free predictive current control for permanent magnet synchronous motor with ultra‐local model

AbstractA large current ripple may arise in the finite control set model‐free predictive current control (FCS‐MFPCC) strategy because only one voltage vector can be applied during the whole control period. Therefore, an improved three‐vector‐based model‐free predictive current control (MFPCC) strategy to improve the current tracking performance is proposed by increasing the number of the applied voltage vector. Based on the extreme existence theorem for the function of two variables, the optimal voltage vector can be synthesized. Compared with the one‐vector‐based FCS‐model‐based predicted current control (FCS‐MBPCC) and two‐vector‐based FCS‐MBPCC in the time and frequency domains, the maximum tracking error is decreased by 42.17% and 48.58%, and the total harmonic distortion is decreased by 40.97% and 37.56%, respectively. Besides, compared with the previous three‐vector‐based strategy, the computational time of the proposed strategy is reduced from 57.6 to 45.89 , which is reduced by 20.33%. Furthermore, the switching frequency of the proposed method can be decreased by 70.3% compared with the deadbeat MFPCC. The experimental results in the time and frequency domains demonstrate that the proposed three‐vector‐based MFPCC can not only improve the current tracking performance but also improve robustness against parameter mismatches.

Improved cascaded model-free predictive speed control for PMSM speed ripple minimization based on ultra-local model

This paper presents an improved cascaded model-free predictive speed and current control with the periodic and aperiodic disturbances suppression to achieve a smooth speed. The cascaded structure has an external speed loop and an internal current loop, both implemented with model-free predictive control to enhance the robustness of the controller. The current loop is designed based on the finite control set model-free predictive current control (FCS-MFPCC) strategy with an ultra-local model to regulate the stator currents, and the speed loop uses the proposed continuous control set model-free predictive speed control (CCS-MFPSC) to make full use of the excellent dynamic performance of the current-loop controller. To suppress the periodic disturbance that exists in the PMSM system, an improved parallel quasi-resonant controller (QRC) with an error limitation is embedded into the CCS-MFPSC, which can generate the compensated current. Based on the stability condition, the stability of the proposed MFPSC-QRC strategy is directly analyzed in the z-domain. Finally, the effectiveness and feasibility of the proposed cascaded model-free predictive speed and current strategy are validated on a PMSM test platform.

Model-free predictive current control of Syn-RM based on time delay estimation approach

Abstract This paper investigates an optimal model-free control design for a synchronous reluctance motor (Syn-RM) with unknown nonlinear dynamic functions, parameter variations, and disturbances. The idea is to combine a predictive control with a time-delay estimation technique (TDE) in order to successfully deal with the system’s uncertainties and make the Syn-RM control scheme easy to implement in real-time. This model-free control strategy comprises two cascade control loops namely outer and inner loops. The outer loop is designed for the mechanical part of Syn-RM to ensure the convergence of the speed dynamics by using a proportional-integral controller while the inner loop is developed to control the uncertain dynamics of currents via an optimal robust controller. In the proposed current loop, the predictive control is enhanced by the inclusion of ultra-local model theory where dynamic functions and disturbances are estimated by instantaneous input-output measurements of the Syn-RM using the TDE approach. Moreover, a particle swarm optimization (PSO) algorithm is also proposed to find the optimal design parameters to improve the dynamic performances of the closed-loop control system. Numerical validation tests of the proposed TDE-based model-free predictive current control (TDE-MFPCC) method are performed in the simulation environment of the Syn-RM system, and the results show the robustness and the effectiveness of the proposed TDE-MFPCC compared to the conventional model-based PCC.

Autoregressive Moving Average Model-Free Predictive Current Control for PMSM Drives

To eliminate the influence of the parameter mismatches and obtain high model quality, a model-free predictive current control (MF-PCC) strategy based on the autoregressive moving average (ARMA) structure is proposed in this paper and applied to the permanent magnet synchronous motor (PMSM) speed control system. Since the ARMA model group which is a family of mathematical models containing AR, MA and ARMA structures considers operating states within several sampling periods to achieve better model accuracy, the plant is online designed as this type, and its coefficients are estimated according to the sampled data by the normalized least-mean-square (NLMS) algorithm with adaptive normalized step length to achieve improved model quality with reduced calculation burden. Compared with the ultra-local MF-PCC strategy, advantages of better stator current quality and robustness are demonstrated by the experimental results, as well as the reduced calculation burden compared with the recursive least square (RLS) algorithm used to estimate the coefficients.