Predictive Current Trajectory Control for PMSM at Voltage Limit

Pei, Genji; Li, Liyi; Gao, Xiaonan; Liu, Jiaxi; Kennel, Ralph

doi:10.1109/access.2019.2962742

Cited by 26 publications

(13 citation statements)

References 30 publications

(31 reference statements)

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“…The FSMPC is chosen as the current control strategy to track the MEA trajectory. Whereas, the FSMPC design on the digital processor requires the discrete-time model of the IPMSM [28,29]. Hence, the continuous-time IPMSM model can be into the discrete-time model with the forward Euler method, defined as follows.…”

Section: Finite-set Model Predictive Controlmentioning

confidence: 99%

“…The FSMPC is chosen as the current control strategy to track the MEA trajectory. Whereas, the FSMPC design on the digital processor requires the discrete‐time model of the IPMSM [28,29]. Hence, the continuous‐time IPMSM model can be into the discrete‐time model with the forward Euler method, defined as follows.

x (k + 1) = A x (k) + B u (k) + C

where

A = [\begin{matrix} subarray \\ center 1 - \frac{R_{s} T_{s}}{L_{d}} & center \frac{w_{e} L_{q} T_{s}}{L_{d}} \\ center - \frac{w_{e} L_{d} T_{s}}{L_{q}} & array 1 - \frac{R_{s} T_{s}}{L_{q}} \end{matrix}] B = [\begin{matrix} subarraycenter \\ array \frac{T_{s}}{L_{d}} & array 0 \\ 0 & \frac{T_{s}}{L_{q}} \end{matrix}]

C = [\begin{matrix} subarray \\ 0 \\ array - \frac{w_{e} φ_{f} T_{s}}{L_{q}} \end{matrix}] u = [\begin{matrix} subarray \\ array u_{d} \\ array u_{q} \end{matrix}] x = [\begin{matrix} subarray \\ array i_{d} \\ array i_{q} \end{matrix}]

…”

Section: Control Systemmentioning

confidence: 99%

See 1 more Smart Citation

High‐Efficiency Drive for IPMSM Considering Copper Loss and Iron Loss

Zhang

2021

IEEJ Transactions Elec Engng

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This paper presents a maximum efficiency algorithm (MEA), aiming at minimizing both copper loss and iron loss, for the interior permanent magnet synchronous motor (IPMSM) drive system. The equivalent circuit model is utilized to reach the optimal dq-current function with the optimal process. Besides, the current-voltage limitation algorithm for the MEA is proposed to solve the current limitation and the voltage limitation issues. The feasible region for the MEA is the intersection of the maximum current region and the maximum voltage region. With the IPMSM test platform, the validated experiments and efficiency tests were implemented to evaluate the proposed strategy. The experiments indicate the MEA operation is effective and feasible to improve the efficiency of the IPMSM. Besides, the feasible operating region of the IPMSM can be extended by the MEA. With the advantage of improving efficiency and extending the operating region, the MEA is suitable for applying the IPMSM drive system.

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Section: Finite-set Model Predictive Controlmentioning

confidence: 99%

x (k + 1) = A x (k) + B u (k) + C

where

A = [\begin{matrix} subarray \\ center 1 - \frac{R_{s} T_{s}}{L_{d}} & center \frac{w_{e} L_{q} T_{s}}{L_{d}} \\ center - \frac{w_{e} L_{d} T_{s}}{L_{q}} & array 1 - \frac{R_{s} T_{s}}{L_{q}} \end{matrix}] B = [\begin{matrix} subarraycenter \\ array \frac{T_{s}}{L_{d}} & array 0 \\ 0 & \frac{T_{s}}{L_{q}} \end{matrix}]

C = [\begin{matrix} subarray \\ 0 \\ array - \frac{w_{e} φ_{f} T_{s}}{L_{q}} \end{matrix}] u = [\begin{matrix} subarray \\ array u_{d} \\ array u_{q} \end{matrix}] x = [\begin{matrix} subarray \\ array i_{d} \\ array i_{q} \end{matrix}]

…”

Section: Control Systemmentioning

confidence: 99%

High‐Efficiency Drive for IPMSM Considering Copper Loss and Iron Loss

Zhang

2021

IEEJ Transactions Elec Engng

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“…The conventional dynamic model of the SPMSM in the rotating d-q reference frame [1] [3] can be described as Fig. 1 shows the conventional equivalent circuit model for SPMSM drives in the rotor reference frame.…”

Section: A Dynamic Model For Spmsm Drivesmentioning

confidence: 99%

“…Among various types of ac motors, the permanent magnet synchronous motors (PMSMs) are increasingly used in the industrial applications (e.g., electric vehicles (EVs), machine tools, industrial robots, home appliances, etc.) owing to their outstanding advantages such as high efficiency, compact size, low maintenance cost, high power density, high reliability, and high torque-to-inertia ratio [1] [3]. In the surfacemounted PMSMs (SPMSMs), it is necessary to reduce the torque ripples that cause undesirable acoustic noises, torsional vibrations, and shaft failures [4] [6].…”

Section: Introductionmentioning

confidence: 99%

Improved Torque Ripple Minimization Technique With Enhanced Efficiency for Surface- Mounted PMSM Drives

et al. 2020

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This paper proposes an improved torque ripple minimization (TRM) method using modelbased loss minimization (LM) control to reduce the torque ripples and enhance the efficiency for surfacemounted permanent magnet synchronous motor (SPMSM) drives. The conventional SPMSM drives usually achieve the maximum torque per ampere (MTPA) control by applying the zero d-axis current (ZDC) and ignore an actual core loss resistance in the controller design. Unlike the conventional MTPA method, the proposed TRM selects the appropriate d-axis current value by making the derivative of the electromagnetic torque equal to zero in the SPMSM drives. Consequently, the proposed method can improve the torque ripples and efficiency compared to the conventional MTPA. Using the proportional-integral (PI) controllers, comparative experimental verifications between the proposed model-based TRM, the conventional modelbased MTPA, and the conventional model-based LM are performed through a prototype SPMSM drive with a TI TMS320F28335 digital signal processor (DSP). INDEX TERMS Core loss resistance, maximum torque per ampere (MTPA), model-based loss minimization (LM) control, surface-mounted permanent magnet synchronous motor (SPMSM), torque ripple minimization (TRM).

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“…MPC belongs to a short prediction horizon method covering only one sampling period. The MPC in the PMSM system can be divided into model predictive current control (MPCC), model predictive speed control (MPSC), and model predictive torque control (MPTC) according to the primary control objective [8][9][10]. A voltage smoother in [11] and an integral sliding mode disturbance observer in [12] are combined with the MPSC respectively to enhance control performances of speed or angular velocity.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Angular Velocity Error Prediction Horizon Self-Tuning Nonlinear Model Predictive Speed Control Strategy for PMSM System

Wei

Sun

et al. 2021

Electronics

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In nonlinear model predictive control (NMPC), higher accuracy can be obtained with a shorter prediction horizon in steady-state, better dynamics can be obtained with a longer prediction horizon in a transient state, and calculation burden is proportional to the prediction horizon which is usually pre-selected as a constant according to dynamics of the system with NMPC. The minimum calculation and prediction accuracy are hard to ensure for all operating states. This can be improved by an online changing prediction horizon. A nonlinear model predictive speed control (NMPSC) with advanced angular velocity error (AAVE) prediction horizon self-tuning method has been proposed in which the prediction horizon is improved as a discrete-time integer variable and can be adjusted during each sampling period. A permanent magnet synchronous motor (PMSM) rotor position control system with the proposed strategy is accomplished. Tracking performances including rotor position Integral of Time-weighted Absolute value of the Error (ITAE), the maximal delay time, and static error are improved about 15.033%, 23.077%, and 10.294% respectively comparing with the conventional NMPSC strategy with a certain prediction horizon. Better disturbance resisting performance, lower weighting factor sensitivities, and higher servo stiffness are achieved. Simulation and experimental results are given to demonstrate the effectiveness and correctness.

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Predictive Current Trajectory Control for PMSM at Voltage Limit

Cited by 26 publications

References 30 publications

High‐Efficiency Drive for IPMSM Considering Copper Loss and Iron Loss

High‐Efficiency Drive for IPMSM Considering Copper Loss and Iron Loss

Improved Torque Ripple Minimization Technique With Enhanced Efficiency for Surface- Mounted PMSM Drives

An Advanced Angular Velocity Error Prediction Horizon Self-Tuning Nonlinear Model Predictive Speed Control Strategy for PMSM System

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