FPGA based finite-set model predictive current control for small PMSM drives with efficient resource streaming

Wendel, Sebastian; Dietz, Armin; Kennel, Ralph

doi:10.1109/precede.2017.8071270

Cited by 27 publications

(7 citation statements)

References 7 publications

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“…Due to the fact that most direct MPC algorithms require a sampling frequency of about two orders of magnitude higher than the desired switching frequency [33], FPGAs are the most promising control platform owing to their ability to perform calculations in a highly pipelined and parallelized manner [129], [182]. For example, direct MPC with horizon N p = 2 and sampling interval T s = 10 µs (f s = 100 kHz) was solved with exhaustive search on an FPGA by pipelining all possible predictions [193].…”

Section: B Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Καραμανάκος

Liegmann

Geyer

et al. 2020

IEEE Open J. Ind. Applicat.

Self Cite

301

130

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Model predictive control (MPC) has established itself as a promising control methodology in power electronics. This survey paper highlights the most relevant MPC techniques for power electronic systems. These can be classified into two major groups, namely, MPC without modulator, referred to as direct MPC, and MPC with a subsequent modulation stage, known as indirect MPC. Design choices and parameters that affect the system performance, closed-loop stability and controller robustness are discussed. Moreover, solvers and control platforms that can be employed for the real-time implementation of MPC algorithms are presented. Finally, the MPC schemes in question are assessed, among others, in terms of design and computational complexity, along with their performance and applicability depending on the power electronic system at hand.

show abstract

Section: B Systemsmentioning

confidence: 99%

“…Following, parallelizable computations can be offloaded to the FPGA. In recent years, several research groups have built their own control platforms based on FPGA SoCs, see, e.g., [193]- [198]. Fig.…”

Section: B Systemsmentioning

confidence: 99%

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Καραμανάκος

Liegmann

Geyer

et al. 2020

IEEE Open J. Ind. Applicat.

Self Cite

301

130

View full text Add to dashboard Cite

show abstract

“…MPC schemes are classified in Finite Control Set (FCS) or Convex Control Set (CCS), depending on how the optimum voltages are generated. The former use only the base inverter space voltage set [6]. In case of a motor fed by a threephase two levels inverter, the number of voltage vectors in the set is eight.…”

Section: Introductionmentioning

confidence: 99%

An Effective Model-Free Predictive Current Control for Synchronous Reluctance Motor Drives

Carlet

Tinazzi

Bolognani

et al. 2019

IEEE Trans. on Ind. Applicat.

119

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The performances of a model predictive control (MPC) algorithm largely depend on the knowledge of the system model. A model-free predictive control approach skips all the effects of parameters variations or mismatches, as well as of model nonlinearity and uncertainties. A finite-set model-free current predictive control is proposed in this paper. The current variations predictions induced by the eight base inverter voltage vectors are estimated by means of the previous measurements stored into look-up tables. To keep the current variations information up to date, the three current measurements due to the three most recent feeding voltages are combined together to reconstruct all the others. The reconstruction is performed by taking advantage of the relationships between the three different base voltage vectors involved in the process. In particular, 210 possible combinations of three-state voltage vectors can be found, but they can be gathered together in six different groups. A light and computationally fast algorithm for the group identification is proposed in this paper. Finally, the current reconstruction for the prediction of future steps is thoroughly analysed. A compensation of the motor rotation effect on the input voltages is proposed, too. The control scheme is evaluated by means of both simulation and experimental evidences on two different synchronous reluctance motors.

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“…Moreover, the FPGA is considered the better option for controller designing and prototyping due to its fast computation ability, embedded processor and shorter design cycle [24,25]. In [26], an implementation methodology using the hardware description language (HDL) coder from MathWorks is presented with an automated workflow for implementing long-horizon FCS-MPC for a PMSM drive system. At present, a model-based design (MBD) for the FPGA implementation of complex control systems is attractive because of its time-saving capacity and great flexibility in simulation and debugging [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Step-by-Step Development and Implementation of FS-MPC for a FPGA-Based PMSM Drive System

et al. 2021

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In this paper, finite-set model-predictive control (FS-MPC) is inducted for a motor drive system. The dynamic response and multiple constraint handling nature of FS-MPC are the major factors that stand out among the controller family. However, for real-time implementation, the computational burden of FS-MPC is a primary concern. Due to the parallel processing nature and discrete nature of the hardware platform, the field-programmable gate array (FPGA) can be an alternative solution for the real-time implementation of the controller algorithm. The FPGA is capable of handling the computational requirements for FS-MPC implementation; however, the system development involves multiple steps that lead to a time-consuming debugging process. Moreover, specific hardware coding skill makes it more complex, corresponding to an increase in system complexity, which leads to a tedious task for the system development. This paper presents a FPGA-based implementation of the predictive current control of a permanent magnet synchronous motor (PMSM). FS-MPC of the PMSM drive system is designed and implemented using the digital model integration approach provided by the Xilinx system generator (XSG) and VIVADO platform. The step change in the load disturbance as well as the reference speed is considered for the analysis of the controller for the motor drive system. Moreover, the steady state error and harmonic distortion in the motor current is considered for an in-depth analysis of the system performance corresponding to different sampling frequencies.

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FPGA based finite-set model predictive current control for small PMSM drives with efficient resource streaming

Cited by 27 publications

References 7 publications

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges

An Effective Model-Free Predictive Current Control for Synchronous Reluctance Motor Drives

Step-by-Step Development and Implementation of FS-MPC for a FPGA-Based PMSM Drive System

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