HIL Co-Simulation of Finite Set-Model Predictive Control Using FPGA for a Three-Phase VSI System

Singh, Vijay Kumar; Tripathi, Ravi Nath; Hanamoto, Tsuyoshi

doi:10.3390/en11040909

Cited by 21 publications

(16 citation statements)

References 29 publications

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“…However, the hardware implementation of digital systems using FPGA is somewhat tricky and needs the training to deal with Hardware Description Language (HDL). Therefore, a virtual environment for de-signing, implementing, and testing the digital systems using FPGA is needed [24].…”

Section: Introductionmentioning

confidence: 99%

Performance Investigation of Digital Lowpass IIR Filter Based on Different Platforms

Hassan

2021

Int. j. electr. comput. eng. syst. (Online)

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The work presented in this paper illuminates the design and simulation of a recursive or Infinite Impulse Response (IIR) filter. The proposed design algorithm employs the Genetic Algorithm to determine the filter coefficients to satisfy the required performance. The effectiveness of different platforms on filter design and performance has been studied in this paper. Three different platforms are considered to implement and verify the designed filter’s work through simulation. The first platform is the MATLAB/SIMULINK software package used to implement the Biquad form filter. This technique is the basis for the software implementation of the designed IIR filter. The HDL – Cosimulation technique is considered the second one; it inspired to take advantage of the existing tools in SIMULINK to convert the designed filter algorithm to the Very high-speed integrated circuit Hardware Description Language (VHDL) format. The System Generator is employed as the third technique, in which the designed filter is implemented as a hardware structure based on basic unit blocks provided by Xilinx System Generator. This technique facilitates the implementation of the designed filter in the FPGA target device. Simulation results show that the performance of the designed filter is remarkably reliable even with severe noise levels.

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Section: Introductionmentioning

confidence: 99%

Performance Investigation of Digital Lowpass IIR Filter Based on Different Platforms

Hassan

2021

Int. j. electr. comput. eng. syst. (Online)

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“…The performance of the FS-MPC was analyzed through the simulations in the integrated platform of XSG and MATLAB-Simulink considering steady state response and intermediate responses with the change in the sampling time. In [33], a hardware-in-the-loop (HIL) simulation methodology was adopted for the verification of the XSG-based FS-MPC in αβ-frame for a three-phase VSI system with motor load condition. The performance was tested for the step-by-step verification through simulations considering the controller in MATLAB-Simulink, XSG, and HIL co-simulation.…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based Implementation of Finite Set-MPC for a VSI System Using XSG-Based Modeling

2020

Self Cite

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Finite set-model predictive control (FS-MPC) is used for power converters and drives having unique advantages as compared to the conventional control strategies. However, the computational burden of the FS-MPC is a primary concern for real-time implementation. Field programmable gate array (FPGA) is an alternative and exciting solution for real-time implementation because of the parallel processing capability, as well as, discrete nature of the hardware platform. Nevertheless, FPGA is capable of handling the computational requirements for the FS-MPC implementation, however, the system development involves multiple steps that lead to the time-consuming debugging process. Moreover, specific hardware coding skill makes it more complex corresponding to an increase in system complexity that leads to a tedious task for system development. This paper presents an FPGA-based experimental implementation of FS-MPC using the system modeling approach. Furthermore, a comparative analysis of FS-MPC in stationary αβ and rotating dq frame is considered for simulation as well as experimental result. The FS-MPC for a three-phase voltage source inverter (VSI) system is developed in a realistic digital simulator integrated with MATLAB-Simulink. The simulated controller model is further used for experimental system implementation and validation using Xilinx FPGA: Zedboard Zynq Evaluation and Development Kit. The digital simulator termed as Xilinx system generator (XSG) provided by Xilinx is used for modeling-based FPGA design.

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“…In CCS-MPC, an optimization problem is solved online to get the desired voltage vector, which is then synthesized through a modulator [4]. FCS-MPC can take advantage of the discrete nature of power converters without any modulator [5]. Available voltage vectors that represent all switching states are enumerated to complete the prediction process, and the optimal one is finally selected for the next control period based on a designed cost function.…”

Section: Introductionmentioning

confidence: 99%

Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs

Huang

Niu

et al. 2018

Energies

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Finite-control-set model predictive current control (FCS-MPCC) has been widely investigated in the field of motor control. When the discrete motor prediction model is not obtained accurately, prediction error often occurs, which can result in improper determinations of optimal voltage vectors and can further affect the control performance of motor systems. However, papers evaluating the motor control performance employing FCS-MPCC rarely consider prediction error and its utilization to weaken the influence of inaccurate prediction model. This paper investigates in depth the prediction error caused by three influencing factors from the perspective of model accuracy-discretization method, prediction stepsize, and parameter mismatch. Firstly, the evaluation index, prediction error, is defined and its formulas considering the above three factors are derived based on interior permanent magnet synchronous motor (IPMSM). Then, the theoretical analysis of prediction error is provided. Finally, experimental results of an IPMSM drive system are presented to verify and complement the theoretical analysis. Both the theoretical analysis and experimental results fully elaborate the prediction error, which can offer practical guidelines for the evaluation and improvement of motor control performance, especially for FCS-MPCC in IPMSM applications.

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HIL Co-Simulation of Finite Set-Model Predictive Control Using FPGA for a Three-Phase VSI System

Cited by 21 publications

References 29 publications

Performance Investigation of Digital Lowpass IIR Filter Based on Different Platforms

Performance Investigation of Digital Lowpass IIR Filter Based on Different Platforms

FPGA-Based Implementation of Finite Set-MPC for a VSI System Using XSG-Based Modeling

Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs

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