Model predictive flux control of six-phase permanent magnet synchronous motor with novel virtual voltage vectors

Yao, Ming; Peng, Jingyao; Sun, Xiaodong

doi:10.1007/s00202-022-01515-6

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…VSD transformation simplifies the computation of vectors with high dimensional. The resulting sub-vectors are expressed as (d-q, x-y, 0 1 -0 2 ), and these two-dimensional vectors are orthogonal to each other [14]. A decoupled model for the motor can be derived using the resulting sub-vectors.…”

Section: Mathematical Model Of Six-phase Induction Motormentioning

confidence: 99%

Finite Control Set Model Predictive Control of Six-Phase Induction Motor

Gülbudak

Gökdağ

Akbaba

2022

Preprint

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Model predictive control (MPC) is a closed-loop technique to generate inputs to activate the process so that the outputs of the interest track a certain set point or reference signal. MPC performs this in a closed loop by updating the decision on the value of the input variables based on measurements from the system. MPC uses an explicit process model to predict what the system will do if a specific sequence of inputs is applied in the future. The MPC methods are popular in electrification and ac drive systems due to fast dynamic response. It offers great flexibility in handling multiple control goals and nonlinearities. This paper proposes a novel Finite Control Set MPC method that aims to keep control transitions to a minimum. In the proposed method, it is desired to achieve control targets by using minimum energy. The designed MPC controller meets all requirements for six-phase motor control. The proposed mathematical concept has been proven by experimental results. The experimental results have shown that the proposed method regulates system dynamics in a stable and effective way.

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Section: Mathematical Model Of Six-phase Induction Motormentioning

confidence: 99%

Finite Control Set Model Predictive Control of Six-Phase Induction Motor

Gülbudak

Gökdağ

Akbaba

2022

Preprint

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“…The method is applied to an asymmetrical six-phase IM. Finally, additional auxiliary vectors of two different magnitudes are used in [20] to reduce the torque ripple.…”

Section: Introductionmentioning

confidence: 99%

Online Adaptive Set of Virtual Voltage Vectors for Stator Current Regulation of a Six-Phase Induction Machine Using Finite State Model Predictive Controllers

et al. 2023

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Virtual voltage vectors (VVV) have been used for the control of multi-phase induction machines, where different sub-spaces appear related to the torque production and losses generation. In the literature, several sets of VVV have been used, aiming at reducing harmonic content while maintaining a low computational burden. This paper proposes the use of different sets of VVV to regulate the stator current of multi-phase drives using finite-state model predictive controllers. In the proposal, only one set is active at each control period. This active set is obtained through a preliminary analysis using performance maps. As a result, a method is derived for the online selection using the current operating point. The selection is based on a simple computation from variables usually measured on variable-speed drives. Results are provided for a symmetrical six-phase IM, showing that the proposal improves the closed-loop performance of the multi-phase drive with a low computational cost.

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“…In the actual digital control system, there is a fundamental problem of one-beat delay because of the calculation time of the algorithm and other factors. As a solution to the problem of one-beat delay, many scholars have proposed deadbeat control [20,21]. The authors of [20] proposed a robustness MPCC strategy based on predictive error compensation for the robustness of the system.…”

Section: Introductionmentioning

confidence: 99%

“…As a solution to the problem of one-beat delay, many scholars have proposed deadbeat control [20,21]. The authors of [20] proposed a robustness MPCC strategy based on predictive error compensation for the robustness of the system. In this strategy, the error between the real value of the current and the predicted value of the current is used as feedback to the current prediction.…”

Section: Introductionmentioning

confidence: 99%

Finite Control Set Model-Free Predictive Current Control of a Permanent Magnet Synchronous Motor

Yang

et al. 2022

Energies

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In this paper, a finite control set model-free predictive current control (FCS-MFPCC) of a permanent magnet synchronous motor is presented. The control scheme addresses the problems of large current fluctuation and decline of the motor system performance during parameter perturbation for the traditional finite control set model predictive current control (FCS-MPCC). Firstly, the mathematical model of the motor is analyzed and derived during parameter perturbation, and a new hyperlocal model of the motor is established based on this mathematical model. Secondly, a finite control set model-free predictive current controller is designed based on the new hyperlocal model, and a current error correction factor is introduced to correct the prediction error. Meanwhile, the stability of the observer is demonstrated via the Lyapunov theory. The simulation results show that the proposed control strategy reduces current fluctuation compared with the FCS-MPCC strategy, and the system is robust during parameter perturbation.

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Model predictive flux control of six-phase permanent magnet synchronous motor with novel virtual voltage vectors

Cited by 9 publications

References 36 publications

Finite Control Set Model Predictive Control of Six-Phase Induction Motor

Finite Control Set Model Predictive Control of Six-Phase Induction Motor

Online Adaptive Set of Virtual Voltage Vectors for Stator Current Regulation of a Six-Phase Induction Machine Using Finite State Model Predictive Controllers

Finite Control Set Model-Free Predictive Current Control of a Permanent Magnet Synchronous Motor

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