FPGA Implementation of Predictive Cascaded Speed and Current Control of PMSM Drives With Two-Time-Scale Optimization

Tu, Wencong; Luo, Guangzhao; Chen, Zhe; Liu, Chunqiang; Cui, Longran

doi:10.1109/tii.2019.2897074

Cited by 29 publications

(7 citation statements)

References 17 publications

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“…As no additional delay is introduced, the FPGA-based controller increases the bandwidth of the designed control loops, thus being ideal for the direct control of power converters [3], including power electronics using the recently introduced wide band-gap power switches that are commonly driven with a switching frequency above 100kHz [5]. Computational demanding algorithms like Model Predictive Control (MPC) are also good candidates for FPGA-based implementations because of their parallelized and highly pipelined architecture [6]. The main drawbacks of FPGAs are the lack of performing internal ADCs and limited size, making floating point arithmetic architecture design problematic.…”

Section: Embedded Digital Controllers and System-on-chip -Evolution And Trendsmentioning

confidence: 99%

System-on-Chip FPGA Devices for Complex Electrical Energy Systems Control

Hilairet¹,

Spagnuolo

Cirstea

2022

EEE Ind. Electron. Mag.

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Section: Embedded Digital Controllers and System-on-chip -Evolution And Trendsmentioning

confidence: 99%

System-on-Chip FPGA Devices for Complex Electrical Energy Systems Control

Hilairet¹,

Spagnuolo

Cirstea

2022

EEE Ind. Electron. Mag.

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“…Model Predictive Control (MPC) is an advanced optimization-based control strategy that is gaining more and more popularity in the power electronics framework [1], [2]. Increasing availability of computational power [3] and enhancement of optimization strategies have made advanced control schemes, such as MPC and Digital Twins [4], [5], suitable for fast dynamic systems, as electric drives.…”

Section: Introductionmentioning

confidence: 99%

Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives

et al. 2022

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This paper proposes a fast and accurate solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related control problem requires an iterative solver to find the optimal solution. The real-time certification of the algorithm is of paramount importance to move the technology toward industrial-scale applications. A relevant feature of the proposed solver is that the total number of operations can be computed in the worst-case scenario. Thus, the maximum computational time is known a priori. The solver is deeply illustrated, showing its feasibility for real-time applications in the microseconds range by means of experimental tests. The proposed method outperforms general-purpose algorithms in terms of computation time, while keeping the same accuracy.

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“…Linear control based on modulation schemes and non-linear control based on hysteresis bounds have become the most frequently used control strategies in industrial applications. Because of the continuously increasing computation ability of digital hardware, new and usually more complex control schemes can be implemented, for example, sliding mode, fuzzy, adaptive, and model predictive control [6][7][8][9]. Usually, predictive control techniques use a system model to predict the future behaviour of the plant and select the control action based on an optimisation criterion.…”

Section: Introductionmentioning

confidence: 99%

Model predictive current control with lower switching frequency for permanent magnet synchronous motor drives

Wang

Zhang

et al. 2021

IET Electric Power Appl

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The model predictive current control (MPCC) technique has been an effective alternative for motor drive applications requiring fast dynamic response. However, the conventional MPCC suffers from high switching losses due to high switching frequency of the inverter. This study proposes a low average switching frequency MPCC method based on boundary‐constrained quadratically extrapolated current trajectory for a permanent magnet synchronous motor drive. First, a quadratic function‐based current prediction model is constructed to obtain the current trajectory in the long‐term future. Second, the boundary constraint is designed to obtain the prediction horizon which is changing in real‐time in order to ensure the validity of the predicted current trajectory. Finally, the cost function established according to the value of the prediction horizon is used to reduce the average switching frequency. In comparison with the conventional MPCC method, the proposed method can significantly reduce the switching frequency of the inverter. In the meantime, fast dynamic response and high steady‐state performance of the conventional MPCC method are preserved. Both simulation and experimental results of the proposed method and the conventional MPCC method are presented to verify the feasibility and advantages of the proposed method.

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FPGA Implementation of Predictive Cascaded Speed and Current Control of PMSM Drives With Two-Time-Scale Optimization

Cited by 29 publications

References 17 publications

System-on-Chip FPGA Devices for Complex Electrical Energy Systems Control

System-on-Chip FPGA Devices for Complex Electrical Energy Systems Control

Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives

Model predictive current control with lower switching frequency for permanent magnet synchronous motor drives

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