Android-Based Smart Centrifuge with PID Speed Control

Authors

  • Roichatun Nashichah Department of Medical Electronics Technology, Poltekkes Kemenkes Surabaya
  • Deenda Putri Duta Natalia Department of Medical Electronics Technology, Poltekkes Kemenkes Surabaya
  • Muhammad Kashif Department of Biomedicine and Prevention, University of Rome Tor Vergata

DOI:

https://doi.org/10.33019/

Keywords:

Android Monitoring, Centrifuge System, PID Controller, Speed Regulation, Ziegler–Nichols Tuning

Abstract

This study aimed to design and implement an Android-based centrifuge monitoring system with Proportional Integral Derivative (PID) speed control to improve rotational speed stability and monitoring flexibility in laboratory applications. The system utilized a DC motor as the driving unit, a speed sensor for feedback acquisition, and an Android application developed using MIT App Inventor for real-time monitoring. PID parameters were determined using the Ziegler–Nichols open-loop tuning method based on the process reaction curve. From the experimental response, a dead time of 0.5 s and a time constant of 3.65 s were obtained, resulting in PID parameters of Kp = 8.76, Ki = 8.76, and Kd = 2.19. Experimental testing was performed using a tachometer to evaluate rotational speed performance. The results showed that the implemented PID controller provided stable speed regulation with improved response characteristics and low steady-state error. In addition, the Android application successfully displayed real-time operational data and maintained reliable communication with the centrifuge system. The proposed system demonstrated that the integration of PID control and Android-based monitoring can enhance both control accuracy and operational convenience for laboratory centrifuge applications.

Downloads

Download data is not yet available.

References

1] M. Sharifian Gh and F. Norouzi, “Guidelines for an optimized differential centrifugation of cells.,” Biochem. Biophys. reports, vol. 36, p. 101585, Dec. 2023, doi: 10.1016/j.bbrep.2023.101585.

[2] M. K. Brakke, “Density Gradient Centrifugation: A New Separation Technique1,” J. Am. Chem. Soc., vol. 73, no. 4, pp. 1847–1848, Apr. 1951, doi: 10.1021/ja01148a508.

[3] K. Oshima, K. Nakamura, H. Guo, and R. L. Smith, “Mini-review on application of analytical centrifugation, ultracentrifugation and centrifugal devices to phase equilibria and separation processes,” Fluid Phase Equilib., vol. 558, p. 113457, 2022, doi: https://doi.org/10.1016/j.fluid.2022.113457.

[4] N. S. Nise, Control Systems Engineering, 8th ed. Hoboken, NJ, 2019.

[5] G. E. Company, Fundamentals of Engineering Analysis. in Fundamentals of Engineering Analysis. 1953. [Online]. Available: https://books.google.co.id/books?id=Wu5SAAAAMAAJ

[6] P. Gahinet, LMI Control Toolbox: For Use with MATLAB ; User’s Guide ; Version 1. in Computation, visualization, programming. MathWorks, 1995. [Online]. Available: https://books.google.co.id/books?id=GdWlXwAACAAJ

[7] A. Abdulameer, M. Sulaiman, M. S. M. Aras, and D. Saleem, “Tuning Methods of PID Controller for DC Motor Speed Control,” vol. 3, no. 2, pp. 343–349, 2016, doi: 10.11591/ijeecs.v3.i2.pp343-349.

[8] Ł. Lemieszewski and P. Puzio, “Optimization of PID Controller Settings Using the Ziegler–Nichols Method in Pneumatic Pressure Control Systems,” Procedia Comput. Sci., vol. 270, pp. 5177–5186, 2025, doi: https://doi.org/10.1016/j.procs.2025.09.645.

[9] R. Islam, S. Sarker, S. Mazumder, and M. Rahman, “An IoT based Real-time Low Cost Smart Energy Meter Monitoring System using Android Application Abstract :,” vol. 4, pp. 1–7, 2017.

[10] C. Alves, J. Carlo, and R. Hauck, “Teaching Programming to Novices : A Large-scale Analysis of App Inventor Projects,” 2021.

[11] K. J. Åström and T. Hägglund, “Revisiting the Ziegler–Nichols step response method for PID control,” J. Process Control, vol. 14, no. 6, pp. 635–650, 2004, doi: https://doi.org/10.1016/j.jprocont.2004.01.002.

[12] J.-H. An, H.-S. Kim, and K.-J. Paik, “Development of a Systematic Method for Tuning PID Control Gains in Free-Running Ship Simulations,” J. Mar. Sci. Eng., vol. 13, no. 9, 2025, doi: 10.3390/jmse13091813.

[13] D. M. Umurzakova, I. X. Siddikov, O. V Porubay, T. M. Abdullayev, and C. Author, “Impact of Steady-State Error Minimization on the Performance of Numerical Optimization Techniques in Linear Automatic Control Systems,” vol. 6, no. 6, 2025, doi: 10.18196/jrc.v6i6.26790.

[14] C. I. Muresan and C. M. Ionescu, “Generalization of the FOPDT Model for Identification and Control Purposes,” Processes, vol. 8, no. 6, 2020, doi: 10.3390/pr8060682.

[15] T. Sato, I. Hayashi, Y. Horibe, R. Vilanova, and Y. Konishi, “Optimal Robust PID Control for First- and Second-Order Plus Dead-Time Processes,” Appl. Sci., vol. 9, no. 9, 2019, doi: 10.3390/app9091934.

[16] A. Roy and K. Iqbal, “PID controller tuning for the first-order-plus-dead-time process model via Hermite-Biehler theorem,” ISA Trans., vol. 44, no. 3, pp. 363–378, 2005, doi: https://doi.org/10.1016/S0019-0578(07)60210-9.

[17] M. Tavazoei, “A Novel Approach to Model-Reference Adaptive PID Controllers,” Iran. J. Sci. Technol. Trans. Electr. Eng., vol. 49, no. 1, pp. 371–381, 2025, doi: 10.1007/s40998-024-00777-z.

[18] L. Wang and A. Visioli, “Introduction to the special issue on PID control system design, automatic tuning and applications,” Int. J. Control, vol. 97, no. 1, p. 1, Jan. 2024, doi: 10.1080/00207179.2024.2284449.

[19] V. M. Alfaro and R. Vilanova, “PID control: Resilience with respect to controller implementation,” Front. Control Eng., vol. Volume 3-2022, 2022, doi: 10.3389/fcteg.2022.1061830.

[20] J. G. Ziegler and N. B. Nichols, “Optimum Settings for Automatic Controllers,” J. Dyn. Syst. Meas. Control, vol. 115, no. 2B, pp. 220–222, 1993, doi: 10.1115/1.2899060.

[21] C. Series, “Review of PID control design and tuning methods,” 2023, doi: 10.1088/1742-6596/2649/1/012009.

[22] K. J. Åström and T. Hägglund, “The future of PID control,” Control Eng. Pract., vol. 9, no. 11, pp. 1163–1175, 2001, doi: https://doi.org/10.1016/S0967-0661(01)00062-4.

[23] L. Bernardini, M. O. Mclinden, X. Yang, and M. Richter, How Accurate are Your Experimental Data ? A More Accessible GUM ‑ Based Methodology for Uncertainty Evaluation, vol. 45, no. 11. Springer US, 2024. doi: 10.1007/s10765-024-03446-9.

[24] R. Willink, “An accurate and consistent procedure for the evaluation of measurement uncertainty,” Meas. Sensors, vol. 18, p. 100334, 2021, doi: https://doi.org/10.1016/j.measen.2021.100334.

[25] M. Cox and A. O. Hagan, “Meaningful expression of uncertainty in measurement,” Accredit. Qual. Assur., vol. 27, no. 1, pp. 19–37, 2022, doi: 10.1007/s00769-021-01485-5.

[26] P. Horvath, M. Yampolskiy, and X. Koutsoukos, “Efficient evaluation of wireless real-time control networks.,” Sensors (Basel)., vol. 15, no. 2, pp. 4134–4153, Feb. 2015, doi: 10.3390/s150204134.

[27] I. Calvo, E. Villar, C. Napole, A. Fernández, O. Barambones, and J. M. Gil-García, “Reliable Control Applications with Wireless Communication Technologies: Application to Robotic Systems,” 2021. doi: 10.3390/s21217107.

[28] Z. Wang and S. Fukushima, “Control strategy for networked control systems with time delay and packet dropout using linear matrix inequalities,” 2020.

[29] W. P. Malcolm and R. J. Elliott, “New finite dimensional filters for mixed time-scale dynamics,” in Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002., 2002, pp. 828–832 vol.1. doi: 10.1109/ACSSC.2002.1197294.

Downloads

Published

18.10.2026

How to Cite

[1]
R. Nashichah, D. P. D. Natalia, and Muhammad Kashif, “Android-Based Smart Centrifuge with PID Speed Control”, JurnalEcotipe, vol. 13, no. 2, pp. 169–179, Oct. 2026, doi: 10.33019/.

Most read articles by the same author(s)