Balance prediction of the inertia wheel pendulum by using swing up and PID controller

  • 1 Trakya University, Mechanical Engineering Department, Edirne, Turkey
  • 2 Kırklareli University, Technical Sciences Vocational School, Kırklareli, Turkey


In this paper, inertia wheel pendulum balance control is performed by using swing up and PID controller. Paper provides predictions on real time design balance system. Predictions were performed through data that were classified and tested by machine learning via MATLAB. Data obtained a result of the analyze of balance positions and swinging times of the wheel different diameters and weights in real-time. Through to this work will be able to predictable which wheel characteristics can be controlled and balanced



  1. C. Aguilar-Avelar, R. Rodríguez-Calderón, S. Puga- Guzmán, and J. Moreno-Valenzuela, “Effects of nonlinear friction compensation in the inertia wheel pendulum,” J. Mech. Sci. Technol., vol. 31, no. 9, pp. 4425–4433, 2017, doi: 10.1007/s12206-017-0843-4.
  2. B. Leo, “Bagging predictors,” Mach. Learn., vol. 24, no. 2, pp. 123–140, 1996.
  3. M. W. Spong, P. Corke, and R. Lozano, “Nonlinear control of the Reaction Wheel Pendulum,” Automatica, 2001, doi: 10.1016/S0005-1098(01)00145-5.
  4. O. D. Montoya, V. M. Garrido, W. Gil-gonzález, and C. Orozco-henao, “Passivity-Based Control Applied of a Reaction Wheel Pendulum : an IDA-PBC Approach Passivity-Based Control Applied of a Reaction Wheel Pendulum : an IDA-PBC Approach,” 2019 IEEE Int. Autumn Meet. Power, Electron. Comput., pp. 1–6, 2019.
  5. I. Siradjuddin, E. R. K. Pradani, E. Rohadi, S. Adhisuwignjo, M. Kusumawardani, and I. M. Fitriani, “Designing, implementing and analysing optimal controllers on a non-linear reaction wheel pendulum,” J. Phys. Conf. Ser., vol. 1402, no. 4, 2019, doi: 10.1088/1742- 6596/1402/4/044025.
  6. V. M. Hernández and H. Sira-Ramírez, “Generalized PI Control for Swinging up and Balancing the Inertia Wheel Pendulum,” Proc. Am. Control Conf., vol. 4, no. 2508, pp. 2809–2814, 2003, doi: 10.1109/acc.2003.1243748.
  7. V. Santibañez, R. Kelly, and J. Sandoval, “Control of the inertia wheel pendulum by bounded torques,” Proc. 44th IEEE Conf. Decis. Control. Eur. Control Conf. CDC-ECC ’05, vol. 2005, no. 3, pp. 8266–8270, 2005, doi: 10.1109/CDC.2005.1583500.
  8. N. Kant and R. Mukherjee, “Impulsive Dynamics and Control of the Inertia-Wheel Pendulum,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 3208–3215, 2018, doi: 10.1109/LRA.2018.2851029.
  9. H. I. Jaafar et al., “Efficient control of a nonlinear double-pendulum overhead crane with sensorless payload motion using an improved PSO-tuned PID controller,” JVC/Journal Vib. Control, vol. 25, no. 4, pp. 907–921, 2019, doi: 10.1177/1077546318804319.
  10. R. Ortega and P. J. Nicklasson, Passivity-based Control of Euler-Lagrange Systems : .
  11. A. Türkmen, M. Y. Korkut, M. Erdem, Ö. Gönül, and V. Sezer, “Design, implementation and control of dual axis self balancing inverted pendulum using reaction wheels,” 2017 10th Int. Conf. Electr. Electron. Eng. ELECO 2017, vol. 2018-Janua, no. 1, pp. 717–721, 2018.
  12. M. W. Spong, “The Swing Up Control of the Acrobot,” IEEE Control Syst. Mag., vol. 15, no. May 1994, pp. 49– 55, 1995.
  13. C. Sánchez-López, V. H. Carbajal-Gómez, M. A. Carrasco- Aguilar, and F. E. Morales-López, “PID controller design based on memductor,” AEU - Int. J. Electron. Commun., 2019, doi: 10.1016/j.aeue.2019.01.019.
  14. M. Tuna, C. B. Fidan, S. Kocabey, and S. Görgülü, “Effective and reliable speed control of permanent magnet DC (PMDC) motor under variable loads,” J. Electr. Eng. Technol., vol. 10, no. 5, pp. 2170–2178, 2015, doi: 10.5370/JEET.2015.10.5.2170.
  15. K. Shi, L. Li, H. Liu, J. He, N. Zhang, and W. Song, “An improved KNN text classification algorithm based on density,” CCIS2011 - Proc. 2011 IEEE Int. Conf. Cloud Comput. Intell. Syst., vol. 4, no. 3, pp. 113–117, 2011, doi: 10.1109/CCIS.2011.6045043.
  16. L. K. S. Kilian Q. Weinberger, “Distance Metric Learning for Large Margin Nearest Neighbor Classification,” J. Mach. Learn. Res., vol. 10, pp. 207–244, 2009.
  17. S. Kocaoğlu and E. Akdoğan, “Design and development of an intelligent biomechatronic tumor prosthesis,” Biocybern. Biomed. Eng., 2019, doi: 10.1016/j.bbe.2019.05.004.

Article full text

Download PDF