Optimization-Based Position Sensorless Finite Control Set Model Predictive Control for IPMSMs

Nalakath, Shamsuddeen; Sun, Yingguang; Preindl, Matthias; Emadi, Ali

doi:10.1109/tpel.2017.2784816

Cited by 59 publications

(20 citation statements)

References 21 publications

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“…A 5 kHz square wave with amplitude of 25V is injected into the estimated d-axis so that ∆I H of about 5 A is generated. Since the sampling noise level of the motor controller is about 0.2 A, the selection of ∆I H satisfies (24). The conventional polarity determination is performed to determine the initial value of theθ e (k) 0 before start-up.…”

Section: A Position Estimation At Zero Speedmentioning

confidence: 99%

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Position Estimation Method of IPMSM in Full Speed Range by Simplified Quadratic Optimization

et al. 2020

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In this paper, a new full-speed-range position estimation method for the interior permanent magnet synchronous machine (IPMSM) is proposed. This method is designed for high power traction motor drives. In such application, variable speed operation from zero to high speed is desired. The objective of this paper is to obtain smooth position estimation in full speed range to guarantee high reliability of the motor control system. Firstly, a quadratic cost function based on the stator voltage equations is constructed. The rotor position in the full speed range is obtained by numerically solving an optimization problem. After obtaining the rotor position, the motor speed is then obtained through a phase-locked loop observer. In such a way, only one variable in the optimization problem has to be solved. Therefore, the numeric calculation burden is greatly reduced. The convexity of the quadratic cost function considering sampling noise as well as parameter mismatch is discussed in detail. Thus, the solvability and accuracy of the estimation result is guaranteed. At last, experiments on a traction motor are conducted to verify the effectiveness of the proposed position estimation method. INDEX TERMS IPMSM, Position sensorless control, Newton method, Full speed range.

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Section: A Position Estimation At Zero Speedmentioning

confidence: 99%

“…To solve the estimation method switching problem, a unified position estimation method for IPMSM in the full speed range is proposed in [23], [24]. This method obtains the rotor position and speed by solving an optimization problem using the Newton method at both low speed and high speed.…”

Section: Introductionmentioning

confidence: 99%

Position Estimation Method of IPMSM in Full Speed Range by Simplified Quadratic Optimization

et al. 2020

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“…However, if the operating range of a control system is small and the involved nonlinearities are smooth, then the control system can be reasonably approximated by a linearized system. MPC algorithm [14][15][16] shows a better steady-state and dynamic performance compared to hysteresis control algorithm and PI control algorithm, and its calculation is relatively simpler compared to nonlinear control. MPC has attracted many relevant researchers to conduct their work on it, and its related technology has become a hot research direction.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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This paper reports the optimal control problem on the interior permanent magnet synchronous motor (IPMSM) systems. The control performance of the traditional model predictive control (MPC) controller is ruined due to the parameter uncertainty and mismatching. In order to solve the problem that the MPC algorithm has a large dependence on system parameters, a method which integrates MPC control method and parameter identification for IPMSM is proposed. In this method, the d-q axis inductances and rotor permanent magnet flux of IPMSM motor are identified by the Adaline neural network algorithm, and then, the identification results are applied to the predictive controller and maximum torque per ampere (MTPA) module. The experimental results show that the optimized MPC control proposed in this paper has a good steady state and robust performance.

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“…where high reliability is of paramount significance, especially for the future manless ones [1], [2]. Due to the outstanding features of interior permanent magnet synchronous motors (IPMSMs), they show good prospects in the fields of EV applications [3]- [5]. In order to effectively control IPMSM in EVs, several current sensors are installed for each motor drive to provide the feedback signals for the micro-controller as illustrated in Fig.1 [6], [7].…”

mentioning

confidence: 99%

Mutual Calibration of Multiple Current Sensors With Accuracy Uncertainties in IPMSM Drives for Electric Vehicles

Chen

et al. 2020

IEEE Trans. Ind. Electron.

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This paper proposes a mutual calibration strategy for multiple current sensors in an electric vehicle motor drive. The motor drive usually consists of three current sensors, i.e., a DC-bus current sensor and two phase current sensors. Due to the aging effect and harsh operating environment, the accuracy uncertainty issue is inevitable in these crucial sensors, which results in poor driving performance. In this paper, the detection voltage injection (DV-Injection) method is proposed for mutual calibration of the aforementioned current sensors. Two opposite basic vectors are set together to detect and eliminate the offset error of the DC-bus current sensor. Then, both the directly measured phase-current values by the phase-current sensors and the indirectly measured values by the DC-bus current sensor are sampled. These values are utilized for mutual calibration of the phase-current sensor offset errors and scaling error differences among all the current sensors. Meanwhile, the DV-Injection process is only applied in the period of calibration process, whereas in the remaining intervals the space vector pulse width modulation (SVPWM) technology is utilized. Finally, the effectiveness of the proposed scheme is verified by simulation study in Matlab/Simulink and experimental results on a 5kW IPMSM motor prototype. Index Terms-Accuracy uncertainty, error compensation, interior permanent magnet synchronous motor (IPMSM), mutual calibration. I. INTRODUCTION LECTRIC vehicles (EVs) are typically the large-scale power applications that operate under harsh conditions,

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Optimization-Based Position Sensorless Finite Control Set Model Predictive Control for IPMSMs

Cited by 59 publications

References 21 publications

Position Estimation Method of IPMSM in Full Speed Range by Simplified Quadratic Optimization

Position Estimation Method of IPMSM in Full Speed Range by Simplified Quadratic Optimization

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

Mutual Calibration of Multiple Current Sensors With Accuracy Uncertainties in IPMSM Drives for Electric Vehicles

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