Predictive Current Control of Sensorless Linear Permanent Magnet Synchronous Motor

Wang, He; Wu, Tao; Guo, Youguang; Lei, Gang; Wang, Xinmei

doi:10.3390/en16020628

Cited by 4 publications

(6 citation statements)

References 27 publications

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“…where, f 1 (k), f 2 (k), g 0 (k) and g 1 (k) are the system parameters to be identified, y(k + n) is the output of the system, and u(k + n) is the input of the system. For the convenience of control, the g 1 (k)u(k − 1) term is discarded within the allowable error range, we can get the characteristic model of PMSM by Equation (8) as Equation (9):…”

Section: Characteristic Model Of Pmsmmentioning

confidence: 99%

“…Figure 2 shows the overall speed adaptive control system structure [24]. (9), online identification of charac tic parameters can be carried out according to the input and output values. In this p the gradient method is adopted for identification.…”

Section: Speed Adaptive Control Schemementioning

confidence: 99%

“…Fuzzy control does not require an accurate mathematical model and has strong robustness [7,8], but it relies more on control experience and expert knowledge. Although model predictive control and active disturbance rejection control can improve the speed control accuracy and dynamic performance of the system [9][10][11][12], the algorithms are complex and not easy to implement quickly. In [13], a backstepping sliding mode control based on a recurrent radial basis function network (RBFN) for a PMSM is presented, with a novel combination of the backstepping method and sliding mode control, eliminating the chattering effectively without losing the precision.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Linear Golden Section Speed Adaptive Control of Permanent Magnet Synchronous Motor Based on Model Design

et al. 2022

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Permanent magnet synchronous motor (PMSM) is a multi-variable, strongly coupled, nonlinear complex system. It is usually difficult to establish an accurate mathematical model, and the introduction of new complex algorithms will increase the difficulty of embedded code development. In order to solve this problem, we establish the characteristic model of permanent magnet synchronous motor in this paper, and the speed control scheme of the linear golden-section adaptive control and integral compensation, which is adopted. Finally, using the model-based design (MBD) method, how to build the simulink embedded code automatic generation model is introduced in detail, and then we complete the PMSM speed control physical verification experiment. Simulation and experimental results show that compared with traditional proportional-integral-derivative (PID) control, the speed control accuracy of PMSM is improved about 3.8 times. Meanwhile, the development method based on the model design can increase the PMSM control system physical verification, and then improve the development efficiency.

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Section: Characteristic Model Of Pmsmmentioning

confidence: 99%

Section: Speed Adaptive Control Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Linear Golden Section Speed Adaptive Control of Permanent Magnet Synchronous Motor Based on Model Design

et al. 2022

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“…Instead, refs. [16][17][18][19][20] advocate for the design of observers that function to estimated rotor position. In [16], an adaptive full-order observer is adopted to smoothly start a free-running motor without any position sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Even in the quadrotor system, observers like finite-time extended state observer and adaptive neural network observer are adopted to estimate the position information [18]. In addition, the sliding mode observer (SMO) and the full-order state observer are utilized in the sensorless control of [19,20], respectively. Nonetheless, it is worth mentioning that these sensorless control methods are dependent on the motor parameters, which fail to remain constant in all conditions owing to the variable operation environment [21,22].…”

Section: Introductionmentioning

confidence: 99%

Parameter-Free Model Predictive Current Control for PMSM Based on Current Variation Estimation without Position Sensor

Luo,

Yu,

Ren

et al. 2023

Energies

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To remove parameter dependence in existing sensorless control strategies, a parameter-free model predictive current control is proposed for permanent magnet synchronous motor without any position sensor. First, the current variation during one sampling period is analyzed and divided into two elements: natural attenuation and forced response. Second, recursive least squares algorithm is utilized to estimate the future current variation so that the model predictive current control can be successfully executed paying no attention to motor parameters. Meanwhile, the position information is obtained by the arc tangent function according to the estimated forced response of current variation. At last, experimental results verify that the estimation errors of rotor position are reduced to around 0.1 rad with smaller current prediction error even at low speed where no motor parameters are required.

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Enhancing Performance of Permanent Magnet Motor Drives through Equivalent Circuit Models Considering Core Loss

Guo,

Yu,

et al. 2024

Energies

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Permanent magnet motors (PMMs) have emerged as key components in numerous industrial applications due to their high efficiency, compact size, and robust performance characteristics. However, to attain optimal performance in PMM drives, accurately predicting and mitigating core losses is paramount. This paper aims to provide a comprehensive review of advancements and methodologies for enhancing the performance of PMM drives by integrating equivalent circuit models (ECMs) that account for core losses. Firstly, the significance of core losses in motor drives is underscored, alongside a survey of research endeavors dedicated to core loss reduction. Notably, emphasis is placed on mathematical models offering both swift computation and reasonable accuracy. Subsequently, the paper delves into the development of ECMs, focusing on approaches adept at capturing core loss effects across diverse operating conditions. Moreover, this paper explores the utilization of these improved ECMs in the design and control of PMMs to achieve enhanced performance. By integrating core loss considerations into design and control strategies, PMM drives can optimize efficiency, torque production, and overall system performance. In summary, this paper may consolidate the current state-of-the-art techniques for enhancing PMM performance through the integration of core-loss-aware ECMs. It highlights key research directions and opportunities for further advancements in this critical area, aiming to foster the development of more efficient and reliable PMM-based systems for a wide range of industrial applications.

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Predictive Current Control of Sensorless Linear Permanent Magnet Synchronous Motor

Cited by 4 publications

References 27 publications

Linear Golden Section Speed Adaptive Control of Permanent Magnet Synchronous Motor Based on Model Design

Linear Golden Section Speed Adaptive Control of Permanent Magnet Synchronous Motor Based on Model Design

Parameter-Free Model Predictive Current Control for PMSM Based on Current Variation Estimation without Position Sensor

Enhancing Performance of Permanent Magnet Motor Drives through Equivalent Circuit Models Considering Core Loss

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