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Sensorless real-time implementation-based FS-MPCC and deadbeat predictive control with delay and dead-time compensation of PMSM using MRAS and T‐S fuzzy speed controller

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Abstract

In practice, inverter dead time and digital signal processing delay are influential factors in classic predictive control performance. In this paper, delay compensation in finite-set model predictive current control (FS-MPCC) and inverter dead-time compensation in deadbeat predictive control are considered to improve permanent magnet synchronous machine control steady-state error, current and torque ripples, reduce the computational burden, correct inverter dead time-induced deviation, and reduce the sensitivity of parameter uncertainties. Deadbeat control uses a space vector modulation (SWM) block and converts the controller output voltage into duty cycles imposed on the inverter ensuring a fixed inverter switching frequency as opposed to FS-MPCC, which uses a finite set of switching states with a variable switching frequency (without modulation). The second approach looks at how a Takagi–Sugeno fuzzy logic speed controller (TS-FLC) can be applied to operate an intelligent system, offering a great current reference, which is crucial for the design of the FS-MPCC cost function and the deadbeat inner-loop control. A speed estimation observer based on a model reference adaptive system (MRAS) is suggested. By eliminating the encoder or speed sensor, the observer improves system reliability, boosts control performance, and reduces costs. The chosen FS-MPCC and deadbeat controller will be built and used in the laboratory utilizing DSpace.1104. The experimental results comparison shows that both FS-MPCC and deadbeat can be well applied to the PMSM driving system with good speed tracking performance. However, the FS-MPCC can achieve less harmonics in the stator currents and shows advantages in smaller ripples in the mechanical torque.

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Abbreviations

\({\nu }_{sd}\),\({\nu }_{sq}\) :

Dq-frame stator voltages

\({v}_{sd}^{p}\),\({v}_{sq}^{p}\) :

Stator voltage predictions

\({\widehat{v}}_{sd}\),\({\widehat{v}}_{sd}\) :

Stator voltage estimates.

\({v}_{sd}^{DT}\),\({v}_{sq}^{DT}\) :

Dead-time stator voltages.

\({v}_{dc}\) :

DC-link voltage

\(\Delta V\) :

Average voltage distortion

\({I}_{sa}\), \({I}_{sb}\),\({I}_{sc}\) :

Stator currents in abc-frame

\({\iota }_{sd}\),\({\iota }_{sq}\) :

Stator currents in dq-frame

\({\iota }_{sd}^{*}\),\({\iota }_{sq}^{*}\) :

Reference stator currents in dq-frame

\({\iota }_{sd}^{p}\),\({\iota }_{sq}^{p}\) :

Predicted stator currents in dq-frame

\({\widehat{\iota }}_{sd}\),\({\widehat{\iota }}_{sq}\) :

Estimated stator currents in dq-frame

\({\varphi }_{sd}\),\({\varphi }_{sq}\) :

Stator flux linkage in dq-frame

\({\varphi}_{PM}\) :

Rotor flux linkage

\({\Omega }_{r}\),\({\Omega }_{r}^{*},\widehat{{\Omega }_{r}}\) :

Real, reference, estimated speed

\({e}_{\Omega }\) :

Speed error

\({T}_{r}\) :

Mechanical torque

\({R}_{s}\) :

Resistance

\({T}_{e}\),\({T}_{e}^{*}\) :

Real and reference electromagnetic torque

\({L}_{d}\),\({L}_{q}\) :

Inductance

\(J\) :

Moment of inertia

\(f\) :

Friction

\({s}_{sd}^{p}\),\({s}_{sq}^{p}\) :

Predicted Rectifier dq-axes switching signals

\(p\) :

Number of pole pairs

\({T}_{s}\) :

Sampling time

\(g\) :

Cost function

\(F\) :

Weighting factor

\({t}_{DT}\) :

Dead time

\({K}_{1}{, K}_{2}\) :

MRAS gains

\({G}_{1}{, G}_{2}{, G}_{3}\) :

FLC gains

FOC:

Field-oriented control

DTC:

Direct torque control

DPC:

Direct predictive control

FS-MPCC:

Finite-set model predictive current control

MRAS:

Model reference adaptive system

TS-FLC:

Takagi–Sugeno fuzzy logic controller

THD:

Total harmonic distortion

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Acknowledgements

The Electrical Engineering Laboratory (LGEB), Biskra University, is appreciative to Algeria's General Directorate of Scientific Research and Technological Development for financing this research (DGRSDT). The authors would like to thank the Director of the LGEB laboratory for providing the equipment essential to test and validate the experimental results reported in this study.

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Authors and Affiliations

  1. Electrical Engineering Laboratory of Biskra LGEB, University of Biskra, Biskra, Algeria

    Meryem Benakcha, Abdelhamid Benakcha & Salah Eddine Zouzou

  2. Electrical Engineering Laboratory LEB, University of Batna, Batna, Algeria

    Rachid Abdessemed

Authors
  1. Meryem Benakcha
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Contributions

The first author (BM) surveyed the literature, arranged it systematically and produced the draft of MS. She conducted all the simulations and experimental tests. The second author (BA) corrected and suggested some changes in the draft manuscript. He also helped in the realization of the test bench and the experimental tests. The third author (AR) and the forth author (ZS) supervised the work as researchers guide and gave some suggestions. All authors read and approved the final manuscript.

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Correspondence to Meryem Benakcha.

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Table 2 Parameters
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Benakcha, M., Benakcha, A., Abdessemed, R. et al. Sensorless real-time implementation-based FS-MPCC and deadbeat predictive control with delay and dead-time compensation of PMSM using MRAS and T‐S fuzzy speed controller. Electr Eng 105, 4139–4156 (2023). https://doi.org/10.1007/s00202-023-01939-8

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