# DSP-HIL Comparison between IM Drive Control Strategies

^{1}

^{2}

^{3}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

- HIL Typhoon 402 was used to integrate the fidelity of the physical simulation and the flexibility of numerical simulations. It emulated the induction machine, inverter and sensors. Performance of the overall *.dll strategies were compared with the MATLAB SIMULINK and a good agreement among them was achieved.
- The six controllers were fully implemented into a DSP TMS320F28035 considering practical implementation issues. It was found that sampling time, controller gain discretization, variable type selection and memory allocation are the parameters that must be solved to achieve a high-performance variable speed drive. The DSP implementations on the HIL board was in consonance with the MATLAB simulation.
- It was concluded that predictive current control is computationally simple, it has no practical complexity and it achieves a higher performance compared with the other approaches. Indeed, in the author’s experience, the use of real time controller limits the conclusion previously reported, because real life control challenges are completely removed.

## 2. Control Techniques

#### 2.1. Field Oriented Control (FOC)

#### 2.2. Fuzzy FOC (DIFOC)

#### 2.3. Predictive Current Control (PCC)

#### 2.4. Direct Torque Control (DTC)

#### 2.5. Fuzzy DTC (FTC)

#### 2.6. Predictive Torque Control (PTC)

## 3. Experimental Results

#### 3.1. Software Tuning Process

#### 3.2. HIL-DSP Test Bed Considerations

#### 3.3. Practical Validation

## 4. Discussion

#### 4.1. Main Achievements Traditional Controllers

#### 4.2. Main Achievements Advance Controllers

#### 4.3. Other Strategies

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

${R}_{s}$ | Stator resistance | ${i}_{rdq}$ | Rotor current |

${i}_{s}$ | Stator current | ${L}_{m}$ | Magnetizing inductance three-phase |

${V}_{s}$ | Stator voltage | ${L}_{s}$ | Self-induction of stator windings |

${\Psi}_{s}$ | Stator flux | ${\overline{L}}_{r}$ | Total inductance of the three-phase rotor |

${R}_{r}$ | Rotor resistance | ${i}_{s\alpha}^{*}$ | Real reference stator current component |

${i}_{r}$ | Rotor current | ${R}_{\sigma}$ | Resistance of linkage factor |

${\Psi}_{r}$ | Rotor flux | ${i}_{s\beta}$ | Imaginary stator current component |

${i}_{\alpha}$ | Real current component | ${i}_{s\beta}^{*}$ | Imaginary reference stator current |

${i}_{\beta}$ | Imaginary current component | ${\Psi}_{s\beta}$ | Imaginary stator flux |

${\Psi}_{rdq}$ | Rotor flux | ${\Psi}_{s}^{*}$ | Flux stator reference |

${\tau}_{r}$ | Rotor constant time | ${\tau}_{e}^{*}$ | Electromagnetic torque reference |

${P}_{p}$ | Pair of poles | ${i}_{\beta}^{*}$ | Imaginary reference current component |

$\lambda $ | Weighting factor | ${i}_{\alpha}^{*}$ | Real reference current component |

k | Number of sample | ${k}_{s}$ | Relation of mutual-inductance with stator inductance |

${I}_{s}$ | Stator current | ${i}_{s\alpha}$ | Real stator current |

${T}_{s}$ | Sampling time | ${\tau}_{\sigma}$ | Time constant of the linkage factor |

$\sigma $ | Linkage factor | ${k}_{r}$ | Relation of mutual-inductance with rotor inductance |

${\omega}_{s}$ | Stator speed | ${\Psi}_{rd}$ | Real rotor flux component |

${\omega}_{r}$ | Rotor speed | ${V}_{rdq}$ | Rotor voltage |

${\tau}_{e}$ | Electromagnetic torque | ${\Psi}_{s\alpha}$ | Real stator flux |

${\Psi}_{s\beta}$ | Real rotor flux | ${\tau}_{r}$ | Rotor constant time |

${R}_{\sigma}$ | Resistance of linkage factor |

## Appendix A

Parameter | Magnitude |
---|---|

Stator resistance | 1.115 $\Omega $ |

Stator inductance | 5.974 mH |

Rotor resistance | 1.083 $\Omega $ |

Rotor inductance | 5.974 mH |

Magnetizing inductance | 203.7 mH |

Friction | 0.005752 Nms |

Pole number | 4 |

Nominal power | 5 HP |

Nominal frequency | 60 Hz |

Nominal voltage | 460 V |

Moment of inertia | 0.02 kgm${}^{2}$ |

## References

- Trzynadlowski, A.M. Control of Induction Motors; Academic Press: Cambridge, MA, USA, 2011. [Google Scholar] [CrossRef]
- Bose, B.K. Modern Power Electronics and AC Drives, 1st ed.; Pearson: London, UK, 2001; p. 711. [Google Scholar]
- Liu, H.; Gao, D. A novel flux oriented V/f control method of induction motor based industrial adjustable speed drives. In Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), Wuhan, China, 31 May–2 June 2018; pp. 1739–1744. [Google Scholar] [CrossRef]
- Talla, J.; Leu, V.Q.; Šmídl, V.; Peroutka, Z. Adaptive Speed Control of Induction Motor Drive With Inaccurate Model. IEEE Trans. Ind. Electron.
**2018**, 65, 8532–8542. [Google Scholar] [CrossRef] - Akin, Ö.; Alan, I. The use of FPGA in field-oriented control of an induction machine. Turk. J. Elec. Eng. Comp. Sci.
**2010**, 18, 943–962. [Google Scholar] [CrossRef] - Naganathan, P.; Srinivas, S.; Ittamveettil, H. Five-level torque controller-based DTC method for a cascaded three-level inverter fed induction motor drive. IET Power Electron.
**2017**, 10, 1223–1230. [Google Scholar] [CrossRef] - Ponce Cruz, P.; Herrera, A. Inteligencia Artificial con Aplicaciones a la Ingeniería; Alfaomega: Kissimmee, FL, USA, 2010; p. 348. [Google Scholar]
- Ross, T.J. Fuzzy Logic with Engineering Applications; Wiley: Hoboken, NJ, USA, 2016; p. 583. [Google Scholar]
- Zhang, Y.; Xia, B.; Yang, H.; Rodriguez, J. Overview of model predictive control for induction motor drives. Chin. J. Electr. Eng.
**2016**, 2, 62–76. [Google Scholar] [CrossRef] - Raviteja, B.; Madhuri, N. Predictive torque control of induction motor. Int. J. Adv. Res. Sci. Eng. Technol.
**2014**, 1, 318–329. [Google Scholar] - Holkar, K.S.; Waghmare, L.M. An overview of model predictive control. Int. J. Control. Autom.
**2010**, 3, 47–64. [Google Scholar] - Wróbel, K. Finite Set Model Predictive Speed Control of Induction Motor with Long Horizon. Power Electron. Drives
**2016**, 1, 117–126. [Google Scholar] - Eswar, K.M.R.; Kumar, K.V.P.; Kumar, T.V. Enhanced Predictive Torque Control with Auto-Tuning Feature for Induction Motor Drive. Electr. Power Components Syst.
**2018**, 46, 825–836. [Google Scholar] [CrossRef] - Norambuena, M.; Rodriguez, J.; Zhang, Z.; Wang, F.; Garcia, C.; Kennel, R. A Very Simple Strategy for High-Quality Performance of AC Machines Using Model Predictive Control. IEEE Trans. Power Electron.
**2019**, 34, 794–800. [Google Scholar] [CrossRef] - Hemavathy, K.; Pappa, N.; Kumar, S. Comparison of indirect vector control and direct torque control applied to Induction Motor drive. In Proceedings of the IEEE International Conference on Advanced Communications, Control and Computing Technologies, Ramanathapuram, India, 8–10 May 2014; pp. 192–197. [Google Scholar] [CrossRef]
- Wang, F.; Zhang, Z.; Mei, X.; Rodríguez, J.; Kennel, R. Advanced Control Strategies of Induction Machine: Field Oriented Control, Direct Torque Control and Model Predictive Control. Energies
**2018**, 11, 120. [Google Scholar] [CrossRef] [Green Version] - Hannan, M.; Ali, J.A.; Mohamed, A.; Hussain, A. Optimization techniques to enhance the performance of induction motor drives: A review. Renew. Sustain. Energy Rev.
**2018**, 81, 1611–1626. [Google Scholar] [CrossRef] - Karlovsky, P.; Lettl, J. Induction Motor Drive Direct Torque Control and Predictive Torque Control Comparison Based on Switching Pattern Analysis. Energies
**2018**, 11, 1793. [Google Scholar] [CrossRef] [Green Version] - Nauel, Y.; Todd, R. Digital control HIL comparison for adjustable speed drives. In Proceedings of the 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, 23–26 October 2016; pp. 2754–2759. [Google Scholar]
- Rauber, A.; den Bakker, P. A Comparison of Adjustable-Speed Drive Systems: Voltage Source Inverters and Load-Commutated Inverters for High-Power Applications. IEEE Ind. Appl. Mag.
**2020**, 26, 56–66. [Google Scholar] [CrossRef] - Lauss, G.; Strunz, K. Accurate and Stable Hardware-in-the-Loop (HIL) Real-time Simulation of Integrated Power Electronics and Power Systems. IEEE Trans. Power Electron.
**2020**. [Google Scholar] [CrossRef] - Prabakar, K.; Palmintier, B.; Pratt, A.; Hariri, A.; Mendoza, I.; Baggu, M.M. Improving the Performance of Integrated Power-Hardware-in-the-Loop and Quasi-Static Time-Series Simulations. IEEE Trans. Ind. Electron.
**2020**. [Google Scholar] [CrossRef] - Iranian, M.E.; Mohseni, M.; Aghili, S.; Parizad, A.; Baghaee, H.R.; Guerrero, J.M. Real-Time FPGA-based HIL Emulator of Power Electronics Controllers using NI PXI for DFIG Studies. IEEE J. Emerg. Sel. Top. Power Electron.
**2020**. [Google Scholar] [CrossRef] - Ahmad, J.; Pervez, I.; Sarwar, A.; Tariq, M.; Fahad, M.; Chakrabortty, R.K.; Ryan, M.J. Performance Analysis and Hardware-In-the-Loop (HIL) Validation of Single Switch High Voltage Gain DC-DC Converters for MPP Tracking in Solar PV System. IEEE Access
**2020**, 9, 48811–48830. [Google Scholar] [CrossRef] - Liang, T.; Liu, Q.; Dinavahi, V.R. Real-Time Hardware-in-the-Loop Emulation of High-Speed Rail Power System With SiC-Based Energy Conversion. IEEE Access
**2020**, 8, 122348–122359. [Google Scholar] [CrossRef] - Chakraborty, S.; Mazuela, M.; Tran, D.; Corea-Araujo, J.A.; Lan, Y.; Loiti, A.A.; Garmier, P.; Aizpuru, I.; Hegazy, O. Scalable Modeling Approach and Robust Hardware-in-the-Loop Testing of an Optimized Interleaved Bidirectional HV DC/DC Converter for Electric Vehicle Drivetrains. IEEE Access
**2020**, 8, 115515–115536. [Google Scholar] [CrossRef] - Luo, Y.; Awal, M.A.; Yu, W.; Husain, I. FPGA Based High Bandwidth Motor Emulator for Interior Permanent Magnet Machine Utilizing SiC Power Converter. IEEE J. Emerg. Sel. Top. Power Electron.
**2020**. [Google Scholar] [CrossRef] - Estrada, L.; Vázquez, N.; Vaquero, J.; de Castro, Á.; Arau, J. Real-Time Hardware in the Loop Simulation Methodology for Power Converters Using LabVIEW FPGA. Energies
**2020**, 13, 373. [Google Scholar] [CrossRef] [Green Version] - Khooban, M.; Gheisarnejad, M.; Vafamand, N.; Jafari, M.; Mobayen, S.; Dragicevic, T.; Boudjadar, J. Robust Frequency Regulation in Mobile Microgrids: HIL Implementation. IEEE Syst. J.
**2019**, 13, 4281–4291. [Google Scholar] [CrossRef] - Bai, Y.; Zhuang, H.; Wang, D. Advanced Fuzzy Logic Technologies in Industrial Applications; Springer: London, UK, 2006; p. 334. [Google Scholar] [CrossRef]
- Shaw, I.S. Fuzzy Control of Industrial Systems: Theory and Applications, 1st ed.; Springer: New York, NY, USA, 1998; p. 192. [Google Scholar] [CrossRef]
- Castillo, O.; Melin, P. Type-2 Fuzzy Logic: Theory and Applications, 1st ed.; Studies in Fuzziness and Soft Computing; Springer: Berlin/Heidelberg, Germany, 2008; Volume 223, p. 244. [Google Scholar] [CrossRef]
- Wang, F.; Mei, X.; Rodriguez, J.; Kennel, R. Model predictive control for electrical drive systems-an overview. CES Trans. Electr. Mach. Syst.
**2017**, 1, 219–230. [Google Scholar] [CrossRef] - Uddin, M.; Mekhilef, S.; Nakaoka, M.; Rivera, M. Model predictive control of induction motor with delay time compensation: An experimental assessment. In Proceedings of the IEEE Applied Power Electronics Conference and Exposition (APEC), Charlotte, NC, USA, 15–19 March 2015; pp. 543–548. [Google Scholar] [CrossRef]
- Seborg, D.E.; Thomas, F.; Edgar Mellichamp, D.A.; Doyle, F.J., III. Process Dynamics and Control; Wiley: Hoboken, NJ, USA, 2016; p. 512. [Google Scholar]
- Rossiter, J. Model-Based Predictive Control: A Practical Approach; Control Series; CRC Press: Boca Raton, FL, USA, 2003. [Google Scholar]
- Gan, L. Model Predictive Control of Induction Motor Drive with Constraints. Ph.D. Thesis, RMIT University, Melbourne, Australia, 2014. [Google Scholar]
- Sharma, F.; Kapoor, S. Recent Soft Computing Strategies for Induction Motor Analysis and Control: A Survey; Springer: Berlin/Heisenberg, Germany, 2021; pp. 67–71. [Google Scholar] [CrossRef]
- Radwan, A.G.; Emira, A.A.; AbdelAty, A.M.; Azar, A.T. Modeling and analysis of fractional order DC-DC converter. ISA Trans.
**2018**, 82, 184–199. [Google Scholar] [CrossRef] [PubMed] - Tavazoei, M. Time response analysis of fractional-order control systems: A survey on recent results. Fract. Calc. Appl. Anal.
**2014**, 17. [Google Scholar] [CrossRef] [Green Version] - Khubalkar, S.; Junghare, A.; Aware, M.; Das, S. Unique fractional calculus engineering laboratory for learning and research. Int. J. Electr. Eng. Educ.
**2020**, 57, 3–23. [Google Scholar] [CrossRef] - Chandra Sekhar, O.; Lakhimsetty, S. Direct torque control scheme for a five-level multipoint clamped inverter fed induction motor drive using fractional-order PI controller. Int. Trans. Electr. Energy Syst.
**2020**, 30, e12474. [Google Scholar] [CrossRef] - Chandra Sekhar, O.; Lakhimsetty, S.; Bhat, A.H. A comparative experimental analysis of fractional order PI controller based direct torque control scheme for induction motor drive. Int. Trans. Electr. Energy Syst.
**2021**, 31, e12705. [Google Scholar] [CrossRef] - Vinagre, B.; Podlubny, I.; Hernández, A.; Feliu, V. Some approximations of fractional order operators used in control theory. Fract. Calc. Appl. Anal.
**2000**, 3, 231–248. [Google Scholar] - Tlelo-Cuautle, E.; Pano-Azucena, A.; Guillén-Fernández, O.; Silva-Juárez, A. Analog/Digital Implementation of Fractional Order Chaotic Circuits and Applications; Springer: Berlin/Heisenberg, Germany, 2019. [Google Scholar]

**Figure 8.**Numerical and practical results of the IFOC variable speed controller: (

**a**) speed; (

**b**) torque; (

**c**) stator current; and (

**d**) stator flux.

**Figure 9.**Numerical and practical results of the DIFOC variable speed controller: (

**a**) speed; (

**b**) torque; (

**c**) stator current; and (

**d**) stator flux.

**Figure 10.**Numerical and practical results of the PCC variable speed controller: (

**a**) speed; (

**b**) torque; (

**c**) stator current; and (

**d**) stator flux.

**Figure 11.**Numerical and practical results of the DTC variable speed controller: (

**a**) speed; (

**b**) torque; (

**c**) stator current; and (

**d**) stator flux.

**Figure 12.**Numerical and practical results of the FTC variable speed controller: (

**a**) speed; (

**b**) torque; (

**c**) stator current; and (

**d**) stator flux.

**Figure 13.**Numerical and practical results of the PTC variable speed controller: (

**a**) speed; (

**b**) torque; (

**c**) stator current; and (

**d**) stator flux.

**Table 1.**Comparative table of developed controllers at ${T}_{l}$ = 10 Nm and speed of 1000 RPM. The dynamic performance, hardware resources and general characteristics are indicated with the colors green, light yellow and light orange, respectively.

Characteristics | IFOC | DIFOC | PCC | DTC | FTC | PTC |
---|---|---|---|---|---|---|

Settling time | ∼200 ms | ∼50 ms | ∼400 ms | ∼250 ms | ∼250 ms | ∼250 ms |

Overshoot speed (%) | 6.9 | No overshoot | 12.7 | 5.8 | 8.9 | 8.9 |

Speed variation [minimum | [98.43%,100.08%] | [97.53%,101.47%] | [99.86%,100.23%] | [99.76%,100.23%] | [99.71%,100.23%] | [99.74%,100.23%] |

peak, maximum peak] | ||||||

Torque response time | ∼200 ms | ∼50 ms | ∼400 ms | ∼250 ms | ∼250 ms | ∼250 ms |

Electromagnetic torque | 4.01 | 24.62 | 7.81 | 4.49 | 11.12 | 14.44 |

ripple in steady state (%) | ||||||

Total harmonic distortion | 0.2393 | 0.2843 | 0.3142 | 0.6994 | 0.9737 | 0.9698 |

of flux (%) | ||||||

Total harmonic distortion | 0.5607 | 1.1675 | 1.5693 | 7.355 | 7.2173 | 9.344 |

of current (%) | ||||||

Switching | Constant | Constant | Variable | Variable | Variable | Variable |

frequency | 40 kHz | 40 kHz | ||||

Low speed behavior | Excellent | Excellent | Excellent | Poor | Poor | Poor |

Microcontroller | GPIO, Timers | GPIO, Timers | GPIO, Timers | GPIO, Timers | GPIO, Timers | GPIO, Timers |

resources | PWM, ADC | PWM, ADC | ADC | ADC | ADC | ADC |

Microcontroller | 37.79 | 52.47 | 37.79 | 36.47 | 46.87 | 36.77 |

memory used (%) | ||||||

Execution time | ∼16 $\mathsf{\mu}$S | ∼28 $\mathsf{\mu}$S | ∼25 $\mathsf{\mu}$S | ∼15 $\mathsf{\mu}$S | ∼31 $\mathsf{\mu}$S | ∼42 $\mathsf{\mu}$S |

(F${}_{clk}$ = 60 MHz) | ||||||

Clock cycles | 911 | 1700 | 1497 | 879 | 1855 | 2494 |

Modulation | Necessary | Necessary | Unnecessary | Unnecessary | Unnecessary | Unnecessary |

Controllers | 3 PI | 2 PI and | 1 PI | 1 PI and | 1 PI | 1 PI |

1 PD Fuzzy | 2 hysteresis | |||||

Parameters sensitivity | ${R}_{r}$ and | ${R}_{r}$ and | All motor | ${R}_{s}$ | ${R}_{s}$ | All motor |

${L}_{r}$ | ${L}_{r}$ | parameters | parameters | |||

Controlled variables | Currents | Currents | Currents | Torque and flux | Torque and flux | Torque and flux |

Transformations | abc$\to \alpha \beta \to $dq | abc$\to \alpha \beta $dq | abc$\to \alpha \beta $ | abc$\to \alpha \beta $ | abc$\to \alpha \beta $ | abc$\to \alpha \beta $ |

dq$\to \alpha \beta \to $abc | dq$\to \alpha \beta $abc | dq$\to \alpha \beta $ |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Ortega-García, L.E.; Rodriguez-Sotelo, D.; Nuñez-Perez, J.C.; Sandoval-Ibarra, Y.; Perez-Pinal, F.J.
DSP-HIL Comparison between IM Drive Control Strategies. *Electronics* **2021**, *10*, 921.
https://doi.org/10.3390/electronics10080921

**AMA Style**

Ortega-García LE, Rodriguez-Sotelo D, Nuñez-Perez JC, Sandoval-Ibarra Y, Perez-Pinal FJ.
DSP-HIL Comparison between IM Drive Control Strategies. *Electronics*. 2021; 10(8):921.
https://doi.org/10.3390/electronics10080921

**Chicago/Turabian Style**

Ortega-García, Luis E., Daniela Rodriguez-Sotelo, Jose C. Nuñez-Perez, Yuma Sandoval-Ibarra, and Francisco J. Perez-Pinal.
2021. "DSP-HIL Comparison between IM Drive Control Strategies" *Electronics* 10, no. 8: 921.
https://doi.org/10.3390/electronics10080921