Investigation of the Process Parameters Influence on the Energy Efficiency of an Induction Motor under Model Predictive Control GRAMPC

<- Back to III. Electrical Complexes and Systems Vol. 12

Read full-text

Cite the paper

G.G. Diachenko, ; O.O. Aziukovskyi,

Investigation of the Process Parameters Influence on the Energy Efficiency of an Induction Motor under Model Predictive Control GRAMPC Journal Article

Mechanics, Materials Science & Engineering, 12 , 2017, ISSN: 2412-5954.

Abstract | Links | BibTeX

Authors: G.G. Diachenko, O.O. Aziukovskyi

ABSTRACT. This paper presents the implementation of the nonlinear gradient based model predictive control (MPC) software GRAMPC (GRAdient based MPC) for the energy efficient control of three-phase induction motor drives. GRAMPC is appropriate for controlling nonlinear systems with input constraints in the (sub)millisecond range and is based on real-time solution strategy. The effect of the model algorithmic parameters: prediction horizon, the maximum number of iterations and number of data points is considered and default values in terms of real-time demands are determined. Additionally, some comparison results with conventional methods are provided, which demonstrate the advantages and performance of GRAMPC. The analysis for appropriate choice of the algorithmic parameters is based on simulation results for three different induction motors with different rated powers.

Keywords: induction motor drives, model predictive control, field oriented control, energy efficiency

DOI 10.2412/mmse.5.86.76


[1] Bazzi, A. M., Krein, P. T. (2010). Review of Methods for Real-Time Loss Minimization in Induction Machines. IEEE Transactions on Industry Applications, 46(6), 2319-2328. DOI 10.1109/tia.2010.2070475

[2] Klenke, F., Hofmann, W. (2011). Energy–Efficient Control of Induction Motor Servo Drives With Optimized Motion and Flux Trajectories. Proceedings of the 14th European Conference on Power Electronics and Applications (pp. 1-7).

[3] Stumper, J., Dotlinger, A., Kennel, R. (2013). Loss Minimization of Induction Machines in Dynamic Operation. IEEE Transactions on Energy Conversion,28(3), 726-735. DOI 10.1109/tec.2013.2262048

[4] Qu, Z., Ranta, M., Hinkkanen, M., Luomi, J. (2011). Loss-minimizing flux level control of induction motor drives. 2011 IEEE International Electric Machines & Drives Conference (IEMDC). DOI 10.1109/iemdc.2011.5994597

[5] Diachenko, G., Schullerus, G. (2015). Simple dynamic energy efficient field oriented control in induction motors. In Proceeding of the 18th International Symposium on Power Electronics. Novi Sad.

[6] Kapernick, B., Graichen, K. (2014). The gradient based nonlinear model predictive control software GRAMPC. 2014 European Control Conference (ECC), 1170-1175. DOI 10.1109/ecc.2014.6862353

[7] Graichen, K., Kapernick, B. (2012). A Real-Time Gradient Method for Nonlinear Model Predictive Control. Frontiers of Model Predictive Control, 9-28. DOI 10.5772/37638

Creative Commons Licence
Mechanics, Materials Science & Engineering Journal by Magnolithe GmbH is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at