Research on Model Predictive Control of IPMSM Based on Adaline Neural Network Parameter Identification

Wang, Lihui; Tan, Guojun; Meng, Jie

doi:10.3390/en12244803

Cited by 16 publications

(13 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Permanent magnet synchronous motor (PMSM) has been widely used in the field of high-performance power transmission and renewable energy generation [1] owing to the advantages of simple structure, reliable operation, high power density and high efficiency. Different control algorithms of PMSM are studied for achieving different control objectives, such as field oriented control (FOC) [2], direct torque control (DTC) [3,4], fuzzy control [5], nonlinear control [6] and model predictive control (MPC) [7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…In [14], a new cost function in the form of proportional integral is proposed, which not only reduces the current steady-state error, but also reduces the parameter sensitivity. In [15], a parameter identification algorithm is proposed to get the actual parameter value. The prediction accuracy is improved with the updated parameter in real time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches

Lyu

Gao

et al. 2021

Energies

Self Cite

View full text Add to dashboard Cite

The control performance of the finite control set model predictive current control (FCS-MPCC) for the interior permanent magnet synchronous machine (IPMSM) depends on the accuracy of the mathematical model. A novel robust model predictive current control method based on error compensation is proposed in order to reduce the parameter sensitivity and improve the current control robustness. In this method, the equivalent parameters are obtained from the known voltage and current information at the past time and the error between the predicted current and the actual current at the present time, which is utilized in the two-step prediction process to compensate the parameter mismatch error. Finally, the optimal voltage vector is selected by the cost function. The proposed method is compared with the traditional model predictive current control method through experiments. The experimental results show the effectiveness of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches

Lyu

Gao

et al. 2021

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…The optimization problem is solved on the basis of the specified cost function in order to determine the best sequence of the control signal. Different MPC concepts can be found in the literature [5,[9][10][11][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. The algorithm can be divided, taking into account the prediction horizon length.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches

2020

View full text Add to dashboard Cite

In the paper a comparative study of the two control structures based on MPC (Model Predictive Control) for an electrical drive system with an induction motor are presented. As opposed to the classical approach, in which DFOC (Direct Field Oriented Control) with four controllers is considered, in the current study only one MPC controller is utilized. The proposed control structures have a cascade free structure that consists of a vector of electromagnetic (torque, flux) and mechanical (speed) states of the system. The first investigated framework is based on the finite-set MPC. A short horizon predictive window is selected. The continuous set MPC is used in the second framework. In this case the predictive horizon contains several samples. The computational complexity of the algorithm is reduced by applying its explicit version. Different implementation aspects of both MPC structures, for instance the model used in prediction, complexity of the control algorithms, and their properties together with the noise level are analyzed. The effectiveness of the proposed approach is validated by some experimental tests.

show abstract

“…The effective wind speed horizon has been forecasted with higher level of correctness as shown in the work of Madhiarasan [ 18 ]. Some works leveraged the Adaline neural network approach in various forecasting tasks such as in power filter optimization [ 19 ] and interior permanent magnet synchronous motor (IPMSM) parameter prediction [ 20 ]. Both work of Sujith and Padma [ 19 ] and Wang et al [ 20 ] utilized an Adaline neural network as a classifier for the parameters involved in industrial control problem.…”

Section: Introductionmentioning

confidence: 99%

“…Some works leveraged the Adaline neural network approach in various forecasting tasks such as in power filter optimization [ 19 ] and interior permanent magnet synchronous motor (IPMSM) parameter prediction [ 20 ]. Both work of Sujith and Padma [ 19 ] and Wang et al [ 20 ] utilized an Adaline neural network as a classifier for the parameters involved in industrial control problem. The deep convolutional neural network (DCNN) is a variant of powerful ANN, with multi-layer hidden neurons that play important role for the data prediction.…”

Section: Introductionmentioning

confidence: 99%

Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation

Mohd Jamaludin,

Mohd Kasihmuddin,

Md Ismail

et al. 2020

Entropy

View full text Add to dashboard Cite

An effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on the relationship between factors contributing to systematic recruitment, the artificial neural network and logic mining approach can be adopted as a data extraction model. In this work, an energy based k satisfiability reverse analysis incorporating a Hopfield neural network is proposed to extract the relationship between the factors in an electronic (E) recruitment data set. The attributes of E recruitment data set are represented in the form of k satisfiability logical representation. We proposed the logical representation to 2-satisfiability and 3-satisfiability representation, which are regarded as a systematic logical representation. The E recruitment data set is obtained from an insurance agency in Malaysia, with the aim of extracting the relationship of dominant attributes that contribute to positive recruitment among the potential candidates. Thus, our approach is evaluated according to correctness, robustness and accuracy of the induced logic obtained, corresponding to the E recruitment data. According to the experimental simulations with different number of neurons, the findings indicated the effectiveness and robustness of energy based k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward positive recruitment in the insurance agency in Malaysia.

show abstract

Research on Model Predictive Control of IPMSM Based on Adaline Neural Network Parameter Identification

Cited by 16 publications

References 36 publications

An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches

An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches

Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches

Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation

Contact Info

Product

Resources

About