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Model-based designof a cone crusher adaptive control system

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Parent Category: 2025

Category: Content №6 2025

Created on 25 December 2025

Last Updated on 25 December 2025

Published on 30 November -0001

Written by O. Yu. Mykhailenko, D. V. Tsyplenkov, O. V. Ilchenko, H. V. Kolomits, V. S. Lazariev

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Authors:

O. Yu. Mykhailenko*, orcid.org/0000-0003-2898-6652, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

D. V. Tsyplenkov, orcid.org/0000-0002-0378-5400, Dnipro University of Technology, Dnipro, Ukraine;  Dnipropetrovsk Scientific Research Institute of Forensic Expertise, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O. V. Ilchenko, orcid.org/0009-0004-4489-3397, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

H. V. Kolomits, orcid.org/0000-0001-9560-9959, Kryvyi Rih National University, Kryvyi Rih, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V. S. Lazariev, orcid.org/0009-0005-4391-0877, Kryvyi Rih National University, Kryvyi Rih, Ukraine

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2025, (6): 069 - 078

https://doi.org/10.33271/nvngu/2025-6/069

Abstract:

Purpose. The purpose of the article is to develop a methodology for creating cone crusher adaptive control system based on the model-based design method for automated software generation of microprocessor controllers.

Methodology. A method based on a block-oriented predictive model was used to generate control signals for the cone crusher. The parameters and structure of this model were identified in real time using measured data from the plant. A prototype of the control system was created in MATLAB/Simulink. Then, the model-based design method was used to generate software for digital signal processors. Mathematical statistics methods were employed to analyze the experimental results.

Findings. A method of model-based design of an adaptive control system for a cone crusher has been developed. This system uses a predictive model of a block-oriented structure. This model adjusts the predictive controller’s structure and parameters directly during operation. This approach makes it possible to divide the functions of identifying the process model and generating controls between the two digital controllers. Consequently, the average computational time is reduced while ensuring the stabilization of the degree of homogeneity of ore crushing and separate output of the control size class, with respective standard deviation coefficients not exceeding 3.42 and 1.83 %, respectively.

Originality. The regularity of the effect of the closed-side setting and the eccentric speed on the particle size distribution of crushed ore has been established. This shows that high homogeneity of the crushed product is ensured by simultaneously adjusting these input coordinates. We propose a new method for synthesizing an adaptive cone crusher control system based on a model-based design approach. This method provides automated real-time generation of software for microprocessor-based controllers, allowing the system to quickly adjust to changes in rock mass characteristics and other disturbances.

Practical value. A hardware and software implementation of an adaptive control system for a cone crusher is proposed. This system is based on a block-oriented predictive model. The model ensures the stabilization of the required ore particle size distribution. This stabilization is achieved by adjusting the closed-side setting and the eccentric speed. The system is based on 16-bit, low-cost digital signal processors. A prototype of the system was tested in a crushing plant at a metallurgical enterprise.

Keywords: cone crusher, adaptive control system, model-based design, implementation

1. Cheng, J., Ren, T., Zhang, Z., Liu, D., & Jin, X. (2020). A dynamic model of inertia cone crusher using the discrete element method and multi-body dynamics coupling. Minerals, 10(10), 10. https://doi.org/10.3390/min10100862

2. Yamashita, A. S., Thivierge, A., & Euzébio, T. A. M. (2021). A review of modeling and control strategies for cone crushers in the mineral processing and quarrying industries. Minerals Engineering, 170, 107036. https://doi.org/10.1016/j.mineng.2021.107036

3. Kshirsagar, K., Shah, P., & Sekhar, R. (2022). Model based design in industrial automation. 2022 6 ^th International Conference on Computing, Communication, Control And Automation (ICCUBEA), 1-6. https://doi.org/10.1109/ICCUBEA54992.2022.10010895

4. Mykhailenko, O. (2015). Ore crushing process dynamics modeling using the Laguerre model. Eastern-European Journal of Enterprise Technologies, 4(4(76)), 30-35. https://doi.org/10.15587/1729-4061.2015.47318

5. Mykhailenko, O. (2015). Research of adaptive algorithms of Laguerre model parametrical identification at approximation of ore breaking process dynamics. Metallurgical and Mining Industry, 6, 109-117.

6. Mykhailenko, O. (2024). Modeling of cone crusher steady-state operation modes. IOP Conference Series: Earth and Environmental Science, 1415(1), 012078. https://doi.org/10.1088/1755-1315/1415/1/012078

7. Bhadani, K., Asbjörnsson, G., Schnitzer, B., Quist, J., Hansson, C., Hulthén, E., & Evertsson, M. (2021). Applied calibration and validation method of dynamic process simulation for crushing plants. Minerals, 11(9), 921. https://doi.org/10.3390/min11090921

8. Itävuo, P., & Vilkko, M. (2021). Size reduction control in cone crushers. Minerals Engineering, 173, 107202. https://doi.org/10.1016/j.mineng.2021.107202

9. Atta, K. T., Euzébio, T., Ibarra, H., Moreira, V. S., & Johansson, A. (2019). Extension, validation, and simulation of a cone crusher model. IFAC-PapersOnLine, 52(14), 1-6. https://doi.org/10.1016/j.ifacol.2019.09.154

10.      Bhadani, K., Asbjörnsson, G., Hofling, K., Hulthén, E., & Evertsson, M. (2024). Application of design of experiments (DoE) in evaluating crushing-screening performance for aggregates production. Minerals Engineering, 209, 108616. https://doi.org/10.1016/j.mineng.2024.108616

11.      Bhadani, K., Asbjörnsson, G., Hulthén, E., Hofling, K., & Evertsson, M. (2021). Application of optimization method for calibration and maintenance of power-based belt scale. Minerals, 11(4), 412. https://doi.org/10.3390/min11040412

12.      Nsugbe, E., Starr, A., Jennions, I., & Carcel, C. R. (2017). Online particle size distribution estimation of a mixture of similar sized particles with acoustic emissions. Journal of Physics: Conference Series, 885(1), 012009. https://doi.org/10.1088/1742-6596/885/1/012009

13.      Jia, N., Su, M., & Cai, X. (2019). Particle size distribution measurement based on ultrasonic attenuation spectra using burst superposed wave. Results in Physics, 13, 102273. https://doi.org/10.1016/j.rinp.2019.102273

14.      Guillard, F., Marks, B., & Einav, I. (2017). Dynamic X-ray radiography reveals particle size and shape orientation fields during granular flow. Scientific Reports, 7(1), 8155. https://doi.org/10.1038/s41598-017-08573-y

15.      Olivier, L. E., Maritz, M. G., & Craig, I. K. (2020). Estimating ore particle size distribution using a deep convolutional neural network. IFAC-PapersOnLine, 53(2), 12038-12043. https://doi.org/10.1016/j.ifacol.2020.12.740

16.      Fu, Y., & Aldrich, C. (2023). Online particle size analysis on conveyor belts with dense convolutional neural networks. Minerals Engineering, 193, 108019. https://doi.org/10.1016/j.mineng.2023.108019

17.      Krawczykowski, D. (2018). Application of a vision systems for assessment of particle size and shape for mineral crushing products. IOP Conference Series: Materials Science and Engineering, 427(1), 012013. https://doi.org/10.1088/1757-899X/427/1/012013

18.      Ma, X., Zhang, P., Man, X., & Ou, L. (2020). A new belt ore image segmentation method based on the convolutional neural network and the image-processing technology. Minerals, 10(12), 1115. https://doi.org/10.3390/min10121115

19.      Wang, R., Zhang, W., & Shao, L. (2018). Research of ore particle size detection based on image processing. In Y. Jia, J. Du, & W. Zhang (Eds.), Proceedings of 2017 Chinese Intelligent Systems Conference, (pp. 505-514). Springer. https://doi.org/10.1007/978-981-10-6499-9_48

20.      Li, H., Pan, C., Chen, Z., Wulamu, A., & Yang, A. (2020). Ore image segmentation method based on U-Net and watershed. Computers, Materials & Continua, 65(1), 563-578. https://doi.org/10.32604/cmc.2020.09806

21.      Sun, G., Huang, D., Cheng, L., Jia, J., Xiong, C., & Zhang, Y. (2022). Efficient and lightweight framework for real-time ore image segmentation based on deep learning. Minerals, 12(5), 5. https://doi.org/10.3390/min12050526

22.      Wang, C., Luo, H., Wang, J., & Groom, D. (2025). ReUNet: Efficient deep learning for precise ore segmentation in mineral processing. Computers & Geosciences, 195, 105773. https://doi.org/10.1016/j.cageo.2024.105773

23.      Xiao, D., Liu, X., Le, B. T., Ji, Z., & Sun, X. (2020). An ore image segmentation method based on RDU-Net model. Sensors, 20(17), 17. https://doi.org/10.3390/s20174979

24.      Schoukens, M., & Tiels, K. (2017). Identification of block-oriented nonlinear systems starting from linear approximations: A survey. Automatica, 85, 272-292. https://doi.org/10.1016/j.automatica.2017.06.044

25.      Tryputen, M., Kuznetsov, V., Kuznetsova, A., Tryputen, M., Kuznetsova, Y., & Serdiuk, T. (2021). Improving the Reliability of Simulating the Operation of an Induction Motor in Solving the Technical and Economic Problem. In Z. Hu, S. Petoukhov, I. Dychka, & M. He (Eds.). Advances in Computer Science for Engineering and Education III, (pp. 143-152). Springer International Publishing. https://doi.org/10.1007/978-3-030-55506-1_13

26.      Yevheniia, K., Vitaliy, K., Tryputen, M., Kuznetsova, A., Tryputen, M., & Mykola, B. (2019). Development and Verification of Dynamic Electromagnetic Model of Asynchronous Motor Operating in Terms of Poor-Quality Electric Power. 2019 IEEE International Conference on Modern Electrical and Energy Systems (MEES), 350-353. https://doi.org/10.1109/MEES.2019.8896598

27.      Kikovka, S., Tytiuk, V., & Ilchenko, O. (2017). Exploring the operational characteristics of a three-phase induction motor with multi-zone stator windings. 2017 International Conference on Modern Electrical and Energy Systems (MEES), 120-123. https://doi.org/10.1109/MEES.2017.8248867

28.      Kuznetsov, V., Tsyplenkov, D., & Nikolenko, A. (2024). Identification of induction motor parameters in vector control electric drives. European Science, 1(sge26-01), 58-103. https://doi.org/
10.30890/2709-2313.2024-26-00-006

29.      Grüne, L., & Pannek, J. (2017). Nonlinear Model Predictive Control. Springer International Publishing. https://doi.org/10.1007/978-3-319-46024-6

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