COMPARATIVE ANALYSIS OF SPEED TRACKING PERFORMANCES IN SIGNLE-LOOP MODEL PREDICTIVE CONTROL SYSTEM FOR DC MOTOR
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Keywords

model predictive control
DC motor
speed trajectory tracking
PI-controller

How to Cite

Kovbasa, S.M., and Ye.V. Kolomiichuk. “COMPARATIVE ANALYSIS OF SPEED TRACKING PERFORMANCES IN SIGNLE-LOOP MODEL PREDICTIVE CONTROL SYSTEM FOR DC MOTOR”. Proceedings of the Institute of Electrodynamics of the National Academy of Sciences of Ukraine, no. 66, Dec. 2023, p. 124, doi:10.15407/publishing2023.66.124.

Abstract

The results of a comparative study of two speed control systems for a DC motor with permanent magnets are presented: a cascaded system based on PI controllers, and a single-loop system developed on the basis of model predictive control methods. The research was carried out by the simulations for the case of speed trajectory tracking task. It is shown that, under the conditions of known parameters, the controller based on predictive control, designed as a system with one input and one output, unlike the system with PI controllers and compensations of derivatives of the reference signal, does not provide asymptotic speed trajectory tracking. The transients during constant load torque compensation are similar, both control schemes provide asymptotical speed regulation with similar performances. In the case of introducing variations of DC motor moment of inertia, the levels of dynamic trajectory tracking errors become commensurate for both control systems. Unlike the system based on PI regulators, the controller based on model predictive control does not need to measure the armature current and provides improved dynamics in voltage limiting modes.

https://doi.org/10.15407/publishing2023.66.124
Article_19 PDF (Українська)

References

E. F. Camacho, C. Bordons. Model Predictive Control. Springer, 1998. DOI: https:// doi.org/10.1007/978-1-4471-3398-8

Yongsoo Cho, Yeongsu Bak, and Kyo-Beum Lee. Torque-Ripple Reduction and Fast Torque Response Strategy for Predictive Torque Control of Induction Motors. IEEE Transactions on Power Electronics, 2017.

V. Wisniewski, E. Maddalena, and R. Godoy. Discrete-time regional poleplacement using convex approximations: Theory and application to a boost converter. Control Engineering Practice, Vol. 91, P. 104102, 2019.

DOI: https://doi.org/10.1016/j.conengprac.2019.07.020

Sergio Vazquez, Abraham Marquez, Ricardo Aguilera, Daniel Quevedo, Jose I. Leon, and Leopoldo G. Franquelo. Predictive Optimal Switching Sequence Direct Power Control for Grid Connected Power Converters. IEEE Transactions on control Systems Technology, IEEE, 2015. DOI: https:// doi.org/10.1109/TIE.2014.3251378

Sergio Vazquez, Abraham Marquez, Ricardo Aguilera, Daniel Quevedo, Jose I. Leon, and Leopoldo G. Franquelo. Predictive Optimal Switching Sequence Direct Power Control for Grid Connected Power Converters. IEEE Transactions on control Systems Technology, IEEE, 2015. DOI: https:// doi.org/10.1109/TIE.2014.3251378

Guoqiang Li and Daniel Gorges. Hybrid Modeling and Predictive Control of the Power Split and Gear Shift in Hybrid Electric Vehicles. IEEE Transactions on control Systems Technology, Pp. 978–983. IEEE, 2019.

Dehua Shi, Shaohu Wang, Yingfen Cai, and Long Chen. Stochastic Predictive Energy Management of Power Split Hybrid Electric Bus for Real-World Driving Cycles. Automotive Engineering Research Institute, Pp. 61700–61713, 2018. DOI: https://doi.org/10.1109/ACCESS.2018.2876147

Satyabrata Sahoo, Bidyadhar Subudhi, Gayadhar Panda. Optimal Speed Control of DC Motor using Linear Quadratic Regulator and Model Predictive Control. IEEE, 2015. DOI: https://doi.org/10.1109/EPETSG.2015.7510130

Lafta E. J. Alkurawy, Nisreen Khamas. Model Predictive Control for DC Motors. International Scientific Con-ference of Engineering Sciences, 2018. DOI: https://doi.org/10.1109/ISCES.2018.8340528

Siddhesh Dani, Dayaram Sonawane, Deepak Ingole, Sanjaykumar Patil. Performance Evaluation of PID, LQR and MPC for Motor Speed Control. 2nd International Conference for Convergence in Technology (I2CT), 2017.

DOI: https://doi.org/10.1109/I2CT.2017.8226149

Yongchang Zhang, Haitao Yang and Bo Xia. Model Predictive Control of Induction Motor Drives: Torque Control versus Flux Control. IEEE Transactions on Industry Application, 2016. DOI: https://doi.org/10.1109/IPEMC.2016.7512314

Yongchang Zhang and Haitao Yang. Model-Predictive Flux Control of Induction Motor Drives With Switching Instant Optimization. IEEE Transactions on Energy Conversion, 2015.

DOI: https://doi.org/10.1109/TEC.2015.2423692

Ahmed G. Mahmoud A. Aziz, Hegazy Rez and Ahmed A. Zaki Diab. Robust Sensorless Model-Predictive Torque Flux Control for High-Performance Induction Motor Drives. MDPI Mathematics, 2021.

S. Almér, D. Frick, G. Torrisi, and S. Mariéthoz. Predictive converter control: Hidden convexity and real-time quadratically constrained optimization. IEEE Transactions on Control Systems Technology, 2020. DOI: https://doi.org/10.1109/TCST.2019.2963253

P. Zometa, M. Kögel, T. Faulwasser, and R. Findeisen. Implementation aspects of model predictive control for embedded systems. American Control Conference (ACC), Pp. 1205–1210. IEEE, 2012. DOI: https://doi.org/10.1109/ACC.2012.6315076

Popovych M.H., Peresada S.M., Kolomiyets D.N. Control of a direct current tracking electric drive based on indirect estimation of angular velocity. Visnyk Kharkivskoho derzhavnoho polytekhnichnoho universytetu. 1999. Vol. 61. Pp. 43–48.

S. Richter, S. Mariethoz and M. Morari. High-speed online mpc based on a fast gradient method applied to power converter control. Proceedings of the 2010 American Control Conference. Pp. 4737–4743. IEEE, 2010. DOI: https://doi.org/10.1109/ACC.2010.5531095

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Copyright (c) 2023 S. Kovbasa, Ye. Kolomiichuk

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