Continuous Voltage Vector Model-Free Predictive Current Control of Surface Mounted Permanent Magnet Synchronous Motor

Zhou, Yanan; Li, Hongmei; Liu, Rundong; Mao, Jingkui

doi:10.1109/tec.2018.2867218

Cited by 78 publications

(29 citation statements)

References 23 publications

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“…Other techniques involve more accurate results but require extensive or multi-step computations [22]. Recent trends have also considered model-free predictive control [23], considered for current control in Syn-RMs [24], guaranteeing high-performance without concerns on parameter mismatch or nonlinearities. The cost function defines the behaviour of the control action.…”

Section: Recent Developments Of Mpcmentioning

confidence: 99%

Vision, Challenges, and Future Trends of Model Predictive Control in Switched Reluctance Motor Drives

et al. 2021

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Switched Reluctance Motors (SRMs) have become a popular alternative to replace permanent magnet machines in high-performance emerging applications such as automotive and aerospace. However, its market attractiveness is limited by the difficulty in control given its nonlinear behaviour. Model predictive control (MPC) is a promising solution to deal with this problem as per its notable features to deal with complex systems, nonlinearities and constraints. Still, the applications in SRMs are at an early stage compared to other drives. This paper aims to discuss the recent advancements and challenges in MPC for SRMs and a vision of its future developments and applications. The article describes the main difficulties in SRM control and the different approaches adopted to date by MPC to solve them. It also analyzes the control objectives that should still be considered in SRM drives, their particular challenges and how recent MPC developments in other AC drives can be adapted to the SRM case. The paper then proposes a roadmap of future works to achieve a unified and reliable control strategy that boosts SRM to outperform other drives, relating the control objectives to its potential applications.

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Section: Recent Developments Of Mpcmentioning

confidence: 99%

Vision, Challenges, and Future Trends of Model Predictive Control in Switched Reluctance Motor Drives

et al. 2021

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“…In order to relax the dependency on unknown model parameters, the concept of ultra-local model has been applied, where predictions are made using a dynamic model that is continuously adapted based on the input-output behavior of the system [18], [19]. A drawback of this method is the need of tuning several parameters in the prediction model.…”

Section: Introductionmentioning

confidence: 99%

Model-Free Predictive Current Control of a Voltage Source Inverter

et al. 2020

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Conventional model predictive control (MPC) of power converter has been widely applied to power inverters achieving high performance, fast dynamic response, and accurate transient control of power converter. However, the MPC strategy is highly reliant on the accuracy of the inverter model used for the controlled system. Consequently, a parameter or model mismatch between the plant and the controller leads to a sub-optimal performance of MPC. In this paper, a new strategy called model-free predictive control (MF-PC) is proposed to improve such problems. The presented approach is based on a recursive least squares algorithm to identify the parameters of an auto-regressive with exogenous input (ARX) model. The proposed method provides an accurate prediction of the controlled variables without requiring detailed knowledge of the physical system. This new approach and is realized by employing a novel state space identification algorithm into the predictive control structure. The performance of the proposed model-free predictive control method is compared with conventional MPC. The simulation and experimental results show that the proposed method is totally robust against parameters and model changes compared with the conventional model based solutions.

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“…In [40], a current reconstruction algorithm is presented based on the relationship between voltage vectors. In [41], a continuous voltage vector MFPCC is proposed to reduce the current fluctuation, and the optimal voltage vector is obtained by online optimization of vector's phase and amplitude.…”

Section: Introductionmentioning

confidence: 99%

Current Prediction Error Reduction Method of Predictive Current Control for Permanent Magnet Synchronous Motors

et al. 2020

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Predictive current control (PCC) has been widely investigated in motor drive field. The PCC is a model-based control method and the motor prediction model is quite sensitive to motor parameters which may be inaccurately measured and change under different working conditions. In order to reduce the current prediction error of PCC caused by inductance mismatch, a current prediction error reduction method is proposed for permanent magnet synchronous motor (PMSM) by online inductance correction which is based on the relationship between inductance mismatch and current prediction error. Firstly, a proportional regulator is used to obtain the absolute correction value according to the relationship between inductance error and current prediction error. Secondly, the polarity of correction value is judged according to the fluctuation amplitudes of predicted current and actual current. Finally, an adaptive adjustment method for correction period is proposed to ensure the accuracy of inductance correction value. Experimental tests are carried out to verify the effectiveness of proposed method, and results show that the proposed method can obviously reduce current prediction error and improve control performance in practical applications. In addition, the proposed method is suitable for interior and surface mounted PMSMs. INDEX TERMS Current prediction error reduction, online inductance correction, permanent magnet synchronous motor (PMSM), predictive current control (PCC).

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Continuous Voltage Vector Model-Free Predictive Current Control of Surface Mounted Permanent Magnet Synchronous Motor

Cited by 78 publications

References 23 publications

Vision, Challenges, and Future Trends of Model Predictive Control in Switched Reluctance Motor Drives

Vision, Challenges, and Future Trends of Model Predictive Control in Switched Reluctance Motor Drives

Model-Free Predictive Current Control of a Voltage Source Inverter

Current Prediction Error Reduction Method of Predictive Current Control for Permanent Magnet Synchronous Motors

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