Performance Improvement of Model-Predictive Current Control of Permanent Magnet Synchronous Motor Drives

Zhang, Yongchang; Xu, Donglin; Liu, Jiali; Gao, Suyu; Xu, Wei

doi:10.1109/tia.2017.2690998

Cited by 238 publications

(137 citation statements)

References 27 publications

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“…Overall, the PCC can be divided into three categories: the conventional finite-control-set PCC, the Double VVs, and deadbeat PCC. They have been successfully implemented on controllers of PMSMs [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Unified Predictive Current Control of PMSMs with Parameter Uncertainty

Tang

Dai

2019

Electronics

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Predictive current control (PCC) applied on permanent magnet synchronous motors (PMSMs) has been developed into mainly three methods: the conventional finite-control-set PCC, the double voltage vectors PCC, and deadbeat PCC. However, each approach has its particular calculation way for voltage vectors selection and respective execution duration. This paper, based on the deadbeat idea, presents a unified predictive current control scheme of PMSMs. Under this scheme, the prior three classes are able to be clearly unified into one frame with lower calculation effort. Furthermore, to cope with problem of parameter mismatch in dq-axis current predictive model, a integrated identification method is proposed. Firstly, data selectors are designed to reject abnormal data of sampling signals, and then the interval-varying multi-innovation least squares algorithm is combined with forgetting factor (V-FF-MILS) to approximate the error terms caused by electromagnetic parameters error. The estimated results are online fed to the model of PMSM to enhance its accuracy. Finally, the processor in loop (PIL) simulation results verify that the proposed integrated scheme has advantages in current control of PMSMs with large-scale parameter uncertainty.

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Section: Introductionmentioning

confidence: 99%

“…Combination forms of two VVs have been explored. The authors of [3][4][5] considered one active vector and a successively null vector as a set, thus amplitude of the resultant vector can be adjusted. However, Zhang and Yang [6] showed it has an inferior steady-state performance at high speed.…”

Section: Introductionmentioning

confidence: 99%

Unified Predictive Current Control of PMSMs with Parameter Uncertainty

Tang

Dai

2019

Electronics

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“…By the realization method, MPC used in electric drives can be classified into finite control set MPC (FCS‐MPC) and continuous control set MPC (CCS‐MPC) . FCS‐MPC usually uses all the possible switching states of the power converters to implement the prediction and to calculate the cost function, then relatively optimal control variables can be selected . The digital realization of FCS‐MPC is intuitive and simple; thus, the matrix inverse calculation can be avoided.…”

Section: Introductionmentioning

confidence: 99%

“…to implement the prediction and to calculate the cost function, then relatively optimal control variables can be selected. 3,4 The digital realization of FCS-MPC is intuitive and simple; thus, the matrix inverse calculation can be avoided. The control variables can only be selected among the possible switching states in each control cycle, so the control variables are not absolutely optimal but relative, and the switching frequency of the converter in FCS-MPC is not fixed.…”

mentioning

confidence: 99%

An offset‐free robust model predictive control with incremental model and improved current observer for induction motor

Wang

Zheng

Sun

2019

Int Trans Electr Energ Syst

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Summary Model predictive control (MPC) is becoming a popular topic in the control of AC motor and power electronic converters because of its high dynamic performance and multitarget optimization ability. However, steady‐state error still exists in conventional MPC method because of unavoidable errors of model parameters. In addition, time delay in digital control also affects the performance, and even stability of MPC. In this paper, an offset‐free robust model predictive control (OFR‐MPC) method is proposed for the current control of three‐phase induction motor (IM). An incremental model is adopted to improve the parameter robustness and to realize offset‐free prediction. An improved current observer is designed to realize time‐delay compensation in control variable optimization. In addition, the improved current observer is also acting as a filter to solve the problem of noise amplification effect of the incremental model. The proposed method can also be applied to other AC motors and power electronic converters. Simulations and experiments have been implemented to verify the performance of the proposed OFR‐MPC method.

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“…In the field of motor control, forward Euler approximation method is widely used to obtain the discrete motor prediction model due to its simplicity and low computational burden for single-step prediction applications [7][8][9]. In Reference [10], a modified Euler integration method was adopted to achieve higher model accuracy, which actually transformed the implicit Tustin approximation to an explicit one by combining with forward Euler approximation. In Reference [11], two-order Taylor series expansion was utilized to achieve faster signal propagation from input changes to all controlled states, especially the mechanical speed, and relative degree one could be obtained [12].…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Niu

et al. 2018

Energies

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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.

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Performance Improvement of Model-Predictive Current Control of Permanent Magnet Synchronous Motor Drives

Cited by 238 publications

References 27 publications

Unified Predictive Current Control of PMSMs with Parameter Uncertainty

Unified Predictive Current Control of PMSMs with Parameter Uncertainty

An offset‐free robust model predictive control with incremental model and improved current observer for induction motor

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

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