Predictive torque control for AC drives: Improvement of parametric robustness using two-degree-of-freedom control

Stumper, Jean-François; Kuehl, Sascha; Kennel, Ralph

doi:10.1109/ecce.2013.6646837

Cited by 17 publications

(11 citation statements)

References 9 publications

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“…There are some predictive control methods in which, instead of directly predicting the control variable by using the MFPC approach, the measurement data is used to correct prediction errors [57] - [60]. The methods are not, however, completely model-free.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The methods are not, however, completely model-free. By using a two-degree-of-freedom control, the robustness of the predictive torque control of PMS motors is improved via feed-forward compensation while maintaining a strong reference tracking performance [57]. A predictive current control method for PMS motors is proposed in which the prediction errors of current components in each sampling period are stored in a look-up table [58].…”

Section: Literature Reviewmentioning

confidence: 99%

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Model-Free Predictive Control of Motor Drives and Power Converters: A Review

et al. 2021

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Predictive control has emerged as a promising control method in a variety of technological fields. Model predictive control, as one of the subdivisions of this control method, has found a growing number of applications in power electronics and motor drives. In practical implementations, model predictive control faces performance degradation of the controlled plant due to its dependency on a model. There are considerable numbers of review papers that are devoted to the different points of view of predictive control. However, the existing literature lacks a review study that addresses the solutions for parameter dependency of the model predictive control method. Recently, model-free predictive control has been used in drives and power electronics as a solution for dealing with the model-dependency of the model predictive control method. There are many papers that have used such methods. In this paper, a classification is proposed for the different implementation types of model-free predictive control or similar methods that address model parameter uncertainties. Additionally, a comparison between the methods is also presented.INDEX TERMS Control systems, estimation, inverters, motor drives, power conversion, power electronics, predictive control.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Model-Free Predictive Control of Motor Drives and Power Converters: A Review

et al. 2021

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“…Instead of adopting observer compensation, a modified PTC method with weighting factor was proposed in [32], which can reduce the torque ripple at the presence of parametric uncertainty by improving the prediction accurateness. In [33], an extension of the PTC method was proposed by using twodegree-of-freedom control, which can make PMSM drive system more robust in the presence of parameter mismatch. However, only the case of inductance parameter mismatch was considered in [32,33], and the flux linkage parameter mismatch will also seriously affect the performance of the PTC method.…”

Section: Introductionmentioning

confidence: 99%

“…In [33], an extension of the PTC method was proposed by using twodegree-of-freedom control, which can make PMSM drive system more robust in the presence of parameter mismatch. However, only the case of inductance parameter mismatch was considered in [32,33], and the flux linkage parameter mismatch will also seriously affect the performance of the PTC method. In this paper, a R-PTC techniques for the N*3-phase PMSM is proposed to enhance robustness against parameters mismatch and operate without the weighting factor of cost function.…”

Section: Introductionmentioning

confidence: 99%

Robust Predictive Torque Control of N*3-Phase PMSM for High-Power Traction Application

Huang

et al. 2020

IEEE Trans. Power Electron.

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The permanent magnet synchronous motor (PMSM) has become a core component of electromechanical energy conversion in the modern industrial field. In order to expand the application of the PMSM in the field of high power traction, a robust predictive torque control (R-PTC) strategy for the Nsegment three-phase PMSM (N*3-phase PMSM) is proposed in this paper. Firstly, the output characteristics of the N*3-phase PMSM are illustrated with the finite element analysis method, and the mathematical model is established. Then, the six-segment three-phase PMSM predictive control system driven by six parallel inverters is designed to generate the required torque. Further, the influence of the parameter mismatch on the predicted torque and stator flux is taken into consideration based on the conventional predictive torque control (PTC). Finally, a novel R-PTC method with the proportional controller is developed for the N*3-phase PMSM, which can effectively improve accuracy and robustness of predictive control performance under parameters mismatch. Simulation and experimental results verify that, compared with the conventional PTC, the proposed R-PTC method can make the predicted stator flux and torque value accurately track its reference values while achieving lower stator flux and torque ripple.

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“…In recent years, the RBF neural network and predictive control have more applications in AC servo system. The advantages of predictive control are the diversity of the predictive model, rolling optimization scheduling and online calibration adaptability [3][4] . RBF neural network control is simple, and its weighting coefficient can be real-time adjustment [5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

Design for AC servo position loop based on RBF neural network predictive control

Zhang¹,

Gong²,

Chen³

2015

Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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Aiming at wide variations in loads and moment of inertia of the machine tool position servo system, the position loop controller is designed based on RBF neural network control and predictive control. The mathematical model of AC PMSM is established. The predictive model is designed based on controlled autoregressive integral moving average model, obtained the predictive vector and the reference trajectory. The RBF neural network structure is established, tuning PID algorithm is designed. A new control strategy which combined with predictive control and RBF neural network PID control is obtained. Compared with the torque fluctuation, anti-interference ability and tracking properties of the traditional PID control, predictive control ensures that the system tracking performance and RBF neural network control can adjust PID parameters on-line, which guarantees the robustness in the external disturbance and parameter perturbation. The simulation results demonstrate that the RBF neural network predictive controller can guarantee the static and dynamic performance of the system.

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Predictive torque control for AC drives: Improvement of parametric robustness using two-degree-of-freedom control

Cited by 17 publications

References 9 publications

Model-Free Predictive Control of Motor Drives and Power Converters: A Review

Model-Free Predictive Control of Motor Drives and Power Converters: A Review

Robust Predictive Torque Control of N*3-Phase PMSM for High-Power Traction Application

Design for AC servo position loop based on RBF neural network predictive control

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