Finite control set MPC with high frequency injections for sensorless position and speed estimation of a PMSM

Muzikova, Vendula; Glasberger, Tomas; Šmı́dl, Václav; Peroutka, Zdeněk

doi:10.1109/precede.2015.7395506

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…The unwanted "𝑠𝑖𝑛 2 𝜃 ̃" related terms can be cancelled out by summing up ( 14) and (15), resulting in an error term x e that is proportional to 𝑠𝑖𝑛 2𝜃 ̃, expressed as…”

Section: B Ripple Excitation-based Position Estimationmentioning

confidence: 99%

“…Being one of the saliency-based methods, the principle of the proposed RESC can be explained as, in the correct d-q frame, the d-axis prediction error should be uncorrelated with the q-axis incremental voltage, and vice versa. If correlation exists, that means there is position error, which will be reflected in the product between the prediction error and the perpendicular incremental voltages as either (14) or (15). The error term x e composed of the sum of ( 14) and ( 15) is more linearly correlated with the position error than each single product because the nonlinear terms are cancelled by each other.…”

Section: B Ripple Excitation-based Position Estimationmentioning

confidence: 99%

“…Recently, the finite-control-set model predictive control (FCS-MPC), which directly manipulates the switching states without using PWM [12] [13], is drawing attention in the sensorless IPMSM drives [14]. Although the signal injection is difficult to implement in the FCS-MPC due to the lack of modulators [15] [16], the inherent voltage ripples produced by the discrete switching manner provide a new form of excitation to replace signal injections [17]. [6] has exploited this ripple excitation for the low-speed position estimation, but the assumption of zero speed fails in the high speed.…”

mentioning

confidence: 99%

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Ripple Excitation-Based Adaptive Sensorless Control of IPMSM in Full Speed Range

Chen¹,

Qu²,

Qiu

2023

IEEE Access

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This paper proposes an adaptive sensorless finite-control-set model predictive control (FCS-MPC) method for the interior permanent magnet synchronous motor (IPMSM). The method is feasible in the full speed range from zero speed to the flux-weakening region above rated speed, without the method transition between the low and the high speeds. A ripple excitation-based position estimation method is proposed to extract the rotor-saliency-based position information from the inherent voltage ripples of the FCS-MPC. Therefore, the additional voltage margin consumption, the special sampling timing, or the interference in the fundamental control are all avoided. Furthermore, to alleviate the parameter dependency, an adaptive predictive model with the online estimation of the lumped voltage terms and the equivalent dynamic susceptances is proposed. In this way, the main machine parameters and the unmodeled disturbances in the d-q voltage equations of the IPMSM are all considered. The position deviation due to the cross-coupling effect is also analyzed, and a compensation method is proposed. Finally, the effectiveness of the proposed method is validated by the experimental results and the comparison with existing methods.

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Section: B Ripple Excitation-based Position Estimationmentioning

confidence: 99%

Section: B Ripple Excitation-based Position Estimationmentioning

confidence: 99%

mentioning

confidence: 99%

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Ripple Excitation-Based Adaptive Sensorless Control of IPMSM in Full Speed Range

Chen¹,

Qu²,

Qiu

2023

IEEE Access

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“…MPC is an optimization-based methodology, and based on a system's internal model, it calculates control action for the next interval by minimizing the di erence between the reference value and projected value. MPC has a simple control architecture with quick response and parameter robustness [22,[25][26][27]. As compared to FOC, MPC promises a quicker dynamic response.…”

Section: Introductionmentioning

confidence: 99%

Current Control of Permanent Magnet Synchronous Motors Using Improved Model Predictive Control

Nawaz

Dou

Riaz

et al. 2022

Mathematical Problems in Engineering

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Model predictive control (MPC) is a powerful tool for the control of permanent magnet synchronous motors. However, conventional MPC permits using a single voltage vector during one control interval. This results in higher current distortions and large torque ripples. Sensitivity to control parameters is another issue associated with conventional MPC. The duty cycle suggests using an active vector and a null vector during one sampling interval. The method needs excessive computational and prediction effort. Furthermore, a necessary zero vector as the second vector might not give the optimal results. To overcome the problems of computational burden, this paper proposes that a reference voltage vector can be calculated and used to determine the voltage vector to be used for the next interval. This reduces the computational effort to a minimum. Furthermore, it is proposed that the second vector can either be active or null. To overcome the problem of parameter dependence, an electromotive force is calculated on basis of previous values. Simulations have been carried out to verify the efficacy of the proposed method.

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“…For minimizing the ripples occurring in torque MPC is used as a predictive torque and flux controller [10] for PMSM motor control. Indirect torque control is achieved by means of predictive current control [11][12] provides highly regulated stator current with less ripples in torque. This paper proposed a hybrid source battery and fuel cell for electrical vehicle comprises of VSI driven PMSM motor whose control is governed by predictive current control methodology.…”

Section: Introductionmentioning

confidence: 99%

MPC based Hybrid Battery and Fuel cell powered PMSM drive for Electric Vehicle Applications

G.P¹,

Pandiaraj²

2017

IJMSR

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Permanent magnet synchronous motor is a robust machine for electrical vehicle application which can provide maximum torque at starting with low power and control of such machine is complex. This paper presents a model based predictive methodology for current control for PMSM drive powered through hybrid battery and fuel cell sourced electrical vehicle. Advantage of a MPC controller is it can predict the future changes in the system based on past and present inputs and enhances the dynamic performance of the PMSM control system. MPC controller increases the efficiency of the proposed system by ensuring precise control over output current from the drive. A buck-boost converter is employed to provide optimum dc link performance required for the voltage source inverter fed PMSM motor. The main objective of the buck boost converter is to boost the voltage available from battery and fuel cell even during low state of charge regions for stable operation of the drive. A suitable electrical vehicle model was developed in MATLAB/Simulink environment to validate the advantages of proposed conversion and control system for PMSM drive.

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Finite control set MPC with high frequency injections for sensorless position and speed estimation of a PMSM

Cited by 11 publications

References 14 publications

Ripple Excitation-Based Adaptive Sensorless Control of IPMSM in Full Speed Range

Ripple Excitation-Based Adaptive Sensorless Control of IPMSM in Full Speed Range

Current Control of Permanent Magnet Synchronous Motors Using Improved Model Predictive Control

MPC based Hybrid Battery and Fuel cell powered PMSM drive for Electric Vehicle Applications

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