Robustness Improvement of Predictive Current Control Using Prediction Error Correction for Permanent-Magnet Synchronous Machines

Siami, Mohsen; Khaburi, Davood Arab; Abbaszadeh, Alireza; Rodríguez, José

doi:10.1109/tie.2016.2521734

Cited by 209 publications

(104 citation statements)

References 24 publications

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“…In order to quantitatively examine the prediction error under different steady-state operating conditions, the root mean square (RMS) value of prediction error is calculated as [32]: In experiments, it is not practical to evaluate the prediction error in only one or several control periods. In order to quantitatively examine the prediction error under different steady-state operating conditions, the root mean square (RMS) value of prediction error is calculated as [32]: (27) where PE base is the RMS prediction error under basic reference setting of three influencing factors-prediction model obtained by one-order Taylor series expansion, prediction stepsize of 100 µs, and actual values of L d and L q shown in Table 1. All experiments conducted in the following three sections are based on the control scheme shown in Figure 1, and the reference values of i d and i q are 0 A and 4 A, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Basically, the elimination of prediction error can be simply achieved by adding prediction error in the previous control period to the present period as shown in Reference [27]. The improvement of control performance can be seen but is still limited due to the fixed weighting coefficient [27]. The weighting coefficient should be automatically adjusted according to the operating conditions, adopted discretization method and prediction stepsize, and degree of parametric mismatch.…”

Section: Discussionmentioning

confidence: 99%

“…The temperature within motor changes much slower than the stator current, so the stator inductances are easier to change and their changing periods are shorter than that of stator resistance and permanent magnet flux linkage. Secondly, the mismatch of stator inductances can yield greater influence on prediction accuracy and motor control performance compared with other parameters, which has been presented in several works [27,30,31]. Therefore, this paper mainly focuses on the prediction error analysis caused by stator inductances mismatch.…”

Section: Parameter Mismatchmentioning

confidence: 99%

“…In Reference [26], the prediction error between the models with accurate parameters and with parametric mismatches was studied by simulation based on a generalized voltage-source converter. In Reference [27], the weighted prediction errors between predicted and measured current values in previous control interval were utilized to compensate for the prediction error in present prediction process; this can improve the robustness of the surface permanent magnet synchronous motor (SPMSM) system and has quite a simple structure. The study showed the feasibility and potentiality of utilizing prediction error to improve the control performance when employing finite-control-set model predictive current control (FCS-MPCC) algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The study showed the feasibility and potentiality of utilizing prediction error to improve the control performance when employing finite-control-set model predictive current control (FCS-MPCC) algorithm. However, the analysis of prediction error is still limited in both References [26,27], especially in terms of IPMSM, which is not good for further utilization of prediction error.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Parameter Mismatchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Huang

Niu

et al. 2018

Energies

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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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Source current quality improvement of finite control set model predictive control‐based matrix converter under distorted source voltage conditions

Ortatepe

Karaarslan

2020

Int Trans Electr Energ Syst

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Background: MC has many advantages such as no need for high-value filters, capability of bi-directional energy conversion and no need for DC bus. However, distortion in the source voltage, such as noise or harmonics may be reflected directly on the load side due to the lack of any storage elements. Purpose: This paper proposes an effective method for finite control set model predictive control (FCS-MPC) implemented in matrix converter (MC) to overcome these possible drawbacks and to improve source current quality under distorted source voltage conditions. Method: Proposed approach based on instantaneous unity power factor (IUPF) strategy is applied to compensate for disturbance in the source voltage.In order to achieve this, phase-locked loop (PLL)-based virtual source voltage is generated at the same frequency as the source voltage to obtain sinusoidal reference current to be used in FCS-MPC. Design: Proposed method is performed using TMS320F28335 Digital Signal Processor (DSP) that is a 32-bit floating-point processor operating at 150MHz. Results:Experimental results show that it is possible to obtain sinusoidal current waveforms on both sides despite the distortions in source voltage or harmonics due to the proposed virtual source voltage strategy. Conclusion:The results obtained from the experimental set-up confirm the performance of the proposed method.

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Robustness Improvement of Predictive Current Control Using Prediction Error Correction for Permanent-Magnet Synchronous Machines

Cited by 209 publications

References 24 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

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

Source current quality improvement of finite control set model predictive control‐based matrix converter under distorted source voltage conditions

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