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

Lin, Cheng-Kai; Yu, Jen‐te; Lai, Yen-Shin; Yu, Hsing–Cheng

doi:10.1109/tie.2016.2527629

Cited by 208 publications

(169 citation statements)

References 23 publications

Supporting

Mentioning

151

Contrasting

Order By: Relevance

“…Considering the mismatch of Lq, i.e., Lqp = Nq·Lqn and Ldp = Ldn, the prediction errors can be obtained from Equations (20) and (21) as: …”

Section: Resultsmentioning

confidence: 99%

“…Based on Equations (20) and (21), the prediction error caused by the inaccuracy of d-and q-axis stator inductances can be further investigated separately. Assuming that the parameter deviation only exists on L d , i.e., L dp = N d ·L dn and L qp = L qn , Equations (20) and (21) can be rewritten as:…”

Section: Parameter Mismatchmentioning

confidence: 99%

“…Therefore, the absolute value of prediction error is evaluated instead. It can be seen from Equation (22) Considering the mismatch of L q , i.e., L qp = N q ·L qn and L dp = L dn , the prediction errors can be obtained from Equations (20) and (21) as:…”

Section: Parameter Mismatchmentioning

confidence: 99%

“…Such parameter mismatches can bring different degrees of prediction error, which may further lead to wrong selection of optimal voltage vector and deteriorate system control performance. Many published papers have proposed methods to solve this problem, including designing model-free prediction algorithm [19,20], identifying parameters online [9,21,22], and constructing observers to estimate the influence of parameter deviations [23][24][25]. However, the model-free predictive control poses relatively high requirements for hardware implementation of the drive system, while the latter two categories are prone to making control algorithms complex, which may be not suitable for the real-time implementation and may even result in instability of the system.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs

Huang

Niu

et al. 2018

Energies

View full text Add to dashboard Cite

Finite-control-set model predictive current control (FCS-MPCC) has been widely investigated in the field of motor control. When the discrete motor prediction model is not obtained accurately, prediction error often occurs, which can result in improper determinations of optimal voltage vectors and can further affect the control performance of motor systems. However, papers evaluating the motor control performance employing FCS-MPCC rarely consider prediction error and its utilization to weaken the influence of inaccurate prediction model. This paper investigates in depth the prediction error caused by three influencing factors from the perspective of model accuracy-discretization method, prediction stepsize, and parameter mismatch. Firstly, the evaluation index, prediction error, is defined and its formulas considering the above three factors are derived based on interior permanent magnet synchronous motor (IPMSM). Then, the theoretical analysis of prediction error is provided. Finally, experimental results of an IPMSM drive system are presented to verify and complement the theoretical analysis. Both the theoretical analysis and experimental results fully elaborate the prediction error, which can offer practical guidelines for the evaluation and improvement of motor control performance, especially for FCS-MPCC in IPMSM applications.

show abstract

“…Considering the mismatch of Lq, i.e., Lqp = Nq·Lqn and Ldp = Ldn, the prediction errors can be obtained from Equations (20) and (21) as: …”

Section: Resultsmentioning

confidence: 99%

Section: Parameter Mismatchmentioning

confidence: 99%

Section: Parameter Mismatchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs

Huang

Niu

et al. 2018

Energies

View full text Add to dashboard Cite

show abstract

“…In this sense, [14], [15] deal with the high speed control of synchronous machines, in general, and SynRMs, in particular, being this an important aspect for next generation high speed EV propulsion systems. In [16]- [18], robust Direct Torque Control (DTC) and predictive control strategies for SynRM are presented, among others.…”

Section: Introductionmentioning

confidence: 99%

PM-Assisted Synchronous Reluctance Machine Flux Weakening Control for EV and HEV Applications

Trancho

Ibarra

Arias

et al. 2018

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

Improved performance and robustness of synchronous reluctance machine control using an advanced sliding mode and direct vector control

Selma,

Bounadja,

Belmadani

et al. 2023

Adv Control Appl

View full text Add to dashboard Cite

This article focuses on improving the control approach for a synchronous reluctance motor (SynRM) drive powered by a two‐level pulse width modulation (PWM) inverter. While classical sliding mode control (SMC) has been extensively used in control system design, it comes with various drawbacks such as pronounced chattering effects, considerable transient state errors, and reduced robustness. These limitations hinder its practical applicability. To enhance the performance of the SynRM, this paper introduces a novel strategy that combines direct vector control (DVC) with advanced sliding mode control (ASMC), here referring to third‐order sliding mode command (TOSMC), for regulating speed and dq‐axis stator currents. The primary objective of this approach is to achieve precise and efficient control while minimizing total harmonic distortion (THD) in current and reducing output torque fluctuations. Notably, this strategy capitalizes on the strengths of TOSMC and DVC. The efficacy of the proposed control scheme is verified through two sets of thorough simulations realized in MATLAB/Simulink environment. The first set of simulations encompasses the load–torque test, where the motor is subjected to two different levels of load torque. The results from these tests showcase the control scheme's performance under varying load conditions. The second set of simulations involves the speed variation test, where intentional changes are applied to the motor's speed. This test assesses the control approach's ability to handle dynamic speed changes effectively. The proposed control strategy is further compared with conventional control methods, including proportional–integral and second‐order sliding mode command (SOSMC) controls. The results consistently demonstrate the superior performance of the novel approach in terms of accurate control, robustness, and overall stability. The combination of DVC and TOSMC offers a promising avenue for achieving enhanced motor control in the presence of load disturbances and speed variations.

show abstract

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

Cited by 208 publications

References 23 publications

Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs

Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs

PM-Assisted Synchronous Reluctance Machine Flux Weakening Control for EV and HEV Applications

Improved performance and robustness of synchronous reluctance machine control using an advanced sliding mode and direct vector control

Contact Info

Product

Resources

About