Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-23T03:03:12.314Z Has data issue: false hasContentIssue false
  • English
  • Français

Dual Adaptive Neural Network Controller for Underactuated Systems

Published online by Cambridge University Press:  05 February 2021

Seyed Hassan Zabihifar Open the ORCID record for Seyed Hassan Zabihifar [Opens in a new window] ,
Hamed Navvabi  and
Arkady Semenovich Yushchenko
Seyed Hassan Zabihifar*
Affiliation:
Faculty of Mechatronics and Robotics, Bauman Moscow State technical university (BMSTU), Moscow, Russia. E-mail: [email protected]
Hamed Navvabi
Affiliation:
Faculty of Mechanic Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. E-mail: [email protected]
Arkady Semenovich Yushchenko
Affiliation:
Faculty of Mechatronics and Robotics, Bauman Moscow State technical university (BMSTU), Moscow, Russia. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

A new stable adaptive controller based on a neural network for underactuated systems is proposed in this paper. The control scheme has been developed for two underactuated systems as examples. The Furuta pendulum and the Inertia Wheel Pendulum (IWP) have been examined in this paper. The presented approach aims to address the control problem of the given system in swing up, stabilization, and disturbance rejection. To avoid oscillations, two adaptive neural networks (ANNs) are implemented. The first one is used to approximate the equivalent control online and the second one to minimize the oscillations.

Keywords

Adaptive neural networkOscillation compensationRotary inverted pendulumFurutaInertia wheel pendulum
Type
Article
Information
Robotica , Volume 39 , Issue 7 , July 2021 , pp. 1281 - 1298
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Pourboghrat, F. and Karlsson, M., “Adaptive control of dynamic mobile robots with nonholonomic constraints,” Comput. Electr. Eng. 28(4), 241–253 (2002).CrossRefGoogle Scholar
Labiod, S. and Boucherit, M. S., “Adaptive fuzzy control of a class of MIMO nonlinear systems,” Fuzzy Sets. Syst. 151(1), 59–77 (2005).CrossRefGoogle Scholar
Lee, K. W. and Hassan, K., “Adaptive output feedback control of robot manipulators using high-gain observer,” Int. J. Control. 67(6), 869–886 (1997).CrossRefGoogle Scholar
DeCarlo, R. A., Zak, S. H. and Matthews, G. P., “Variable structure control of nonlinear multivariable systems: A tutorial,” Proc. IEEE. 76(3), 212–232 (1988).CrossRefGoogle Scholar
Stepanenko, Y., Cao, Y. and Su, C.-Y., “Variable structure control of robotic manipulator with PID sliding surfaces,” Int. J. Robust Nonlinear Control 8(1), 79–90 (1998).3.0.CO;2-V>CrossRefGoogle Scholar
Elshafei, A. L. and Karray, F., “Variable-structure-based fuzzy-logic identification of a class of nonlinear systems,” IEEE Trans. Control Syst. Technol. 13(4), 646–653 (2005).CrossRefGoogle Scholar
Kwan, C. and Lewis, F. L., “Robust backstepping control of nonlinear systems using neural networks,” IEEE Trans. Syst. Man. Cybern Part A Syst. Hum. 30(6), 753–766 (2000).CrossRefGoogle Scholar
Lee, G. W. and Cheng, F. T., “Robust control of manipulators using the computed torque plus H8 compensation method,” IEE Proc. Control Theory Appl. 143(1), 64–72 (1996).CrossRefGoogle Scholar
Zabihifar, S. and Yushchenko, A., , 105(6), 30–45 (2015).Google Scholar
Zabihifar, S. and Yushchenko, A., “Hybrid force/position control of a collaborative parallel robot using adaptive neural network,” International Conference on Interactive Collaborative Robotics (Leipzig, Germany, 2018) pp. 280–290.CrossRefGoogle Scholar
Nelson, J. and Kraft, L. G., “Real-time control of an inverted pendulum system using complementary neural network and optimal techniques,” American Control Conference, (Baltimore, 1994) pp. 2553–2554.Google Scholar
Cong, S. and Liang, Y., “PID-like neural network nonlinear adaptive control for uncertain multivariable motion control systems,” IEEE Trans. Ind. Electron. 56(10), 3872–3879 (2009).CrossRefGoogle Scholar
Hsu, C.-F., “Adaptive backstepping Elman-based neural control for unknown nonlinear systems,” Neurocomputing 136, 170–179 (2014).CrossRefGoogle Scholar
Hsu, C.-F., Lin, C.-M. and Yeh, R.-G., “Supervisory adaptive dynamic RBF-based neural-fuzzy control system design for unknown nonlinear systems,” Appl. Soft. Comput. 13, 1620–1626 (2013).CrossRefGoogle Scholar
Ping, Z., “Tracking problems of a spherical inverted pendulum via neural network enhanced design,” Neurocomputing 106, 137–147 (2013).CrossRefGoogle Scholar
Spong, M. W., Corke, P. and Lozano, R., “Nonlinear control of the reaction wheel pendulum,” Automatica 37(11), 1845–1851 (2001).Google Scholar
Kurode, S. and Asif, C., “Swing-up and stabilization of rotary inverted pendulum using sliding modes,” IFAC Proc. 44(1), 10685–10690 (2011).CrossRefGoogle Scholar
Shubhobrata, R. and Ranjit Kumar, B., “Stabilization of furuta pendulum: A backstepping based hierarchical sliding mode approach with disturbance estimation,” 7th International Conference on Intelligent Systems and Control (ISCO) (Coimbatore, India, 2013) pp. 99–105.Google Scholar
Moreno-Valenzuela, J., “Adaptive Neural Network Control for the Trajectory Tracking of the Furuta Pendulum,” IEEE Trans Cybern., 3439–3452 (2016).CrossRefGoogle Scholar
Furuta, K., Yamakita, M. and Kobayashi, S., “Swing-up Control of Inverted Pendulum,” International Conference on Industrial Electronics, Control and Instrumentation (Kobe, Japan, 1991) pp. 2193–2198.Google Scholar
Huang, Y.-F. and Chen, A.-C., “Adaptive control of rotary inverted pendulum system with time-varying uncertainties,” Springer 76(1), 1112–1114 (2013).Google Scholar
Acosta, J., “Furuta’s pendulum: A conservative nonlinear model for theory and practice,” Math. Probl. Eng. 2010, 1–29 (2010).Google Scholar
Aguilar-Ibañez, C., Suárez-Castañon, M. and Gutiérres-Frias, O., “The direct Lyapunov method for the stabilization of the Furuta pendulum,” Int. J. Control 83(11) 2285–2293 (2010).CrossRefGoogle Scholar
Turker, T., Gorgun, H. and Cansever, G., “Lyapunov’s direct method for stabilization of the Furuta pendulum,” Turk J. Electr. Eng. Comput. Sci. 120(1) 99–110 (2012).Google Scholar
de Jesús Rubio, J., “Discrete time control based in neural networks for pendulums,” Appl. Soft. Comput. J. 68, 821–832 (2018).CrossRefGoogle Scholar
Astrom, K., Aracil, J. and Gordillo, F., “A family of smooth controller for swinging up a pendulum,” Automatica 36(2), 1841–1848 (2008).Google Scholar
Gordillo, F., Acosta, J. and Aracil, J., “A new swing-up law for the Furuta pendulum,” Int. J. Control 76(8), 836–844 (2003).CrossRefGoogle Scholar
Hera, P., Freidovich, L., Shiriaev, A. and Mettin, U., “New approach for swing up the Furuta pendulum: Theory and experiments,” Mechatronics 19, 1240–1250 (2009).CrossRefGoogle Scholar
Hercus, R. and Wong, K.-y., “Control of an Inverted Pendulum using the NeuraBase,” International Conference on Neural Information Processing (Springer, Berlin, Heidelberg, 2013) pp. 605–615.CrossRefGoogle Scholar
Aguilar, L., Boiko, I., Fridman, L. and Iriarte, R., “Generating self-excited oscillations via two-relay controller,” IEEE Trans. Autom. Control 54(2), 416–420 (2009).CrossRefGoogle Scholar
Freidovich, L., Shiriaev, A., Gordillo, F., Gomez-Estern, F. and Aracil, J., “Partial-energy-shaping control for orbital stabilization of high-frequency oscillations of the Furuta pendulum,” IEEE Trans. Control Syst. Technol. 17(4), 853–858 (2009).CrossRefGoogle Scholar
Sicard, P. and Chaoui, H., “Motion and Balance Neural Control of Inverted Pendulums with Nonlinear Friction and Disturbance,” Proceedings of the 24th Canadian Conference on Electrical and Computer Engineering, CCECE 2011, Niagara Falls (Canada, May 2011) pp. 001222–001227.Google Scholar
Yu, C., Wang, F. and Lu, Y., “Robust control of a Furuta pendulum,” SICE Annual Conference (Taipei, Taiwan, 2010) pp. 2559–2563.Google Scholar
Khanesar, M., Teshnehlab, M. and Shoorehdeli, M., “Fuzzy sliding mode control of rotary inverted pendulum,” IEEE International Conference on Computational Cybernetics (Gammarth, Tunisia, 2007) pp. 57–62.Google Scholar
Van Cuong, P. and Yao Nan, W., “Adaptive trajectory tracking neural network control with robust compensator for robot manipulators,” Nat. Comput. Appl. 27, 525–536 (2016).Google Scholar
Boudjedir, H., Yacef, F., Bouhalim, O. and Rizoug, A., “Dual neural network for adaptive sliding mode control of quadrotor helicopter stabilization,” IJIST 2, 1–14 (2012).CrossRefGoogle Scholar
Lewis, F. W., Jagannathan, S. and Yesildirak, A., Neural Network Control Of Robot Manipulators And Non-Linear Systems (CRC press, Boca Raton, FL, 1998).Google Scholar
Shuzhi, S., Ge, S., Hang, C. C., Lee, T. H. and Zhang, T., Stable Adaptive Neural Network Control (Springer US, Berlin, Germany, 2002).Google Scholar
Zabihifar, S., Davaie Markazi, A. H., Madani, M. and Doostmohammadi, N., “Adaptive fuzzy sliding mode control of under-actuated nonlinear systems,” Int. J. Autom. Comput. 15, 364–376 (2018).Google Scholar
Fantoni, I., Non-linear Control for Underactuated Mechanical Systems (Springer, London, 2002).CrossRefGoogle Scholar
Olfati-Saber, R., “Fixed point controllers and stabilization of the cart-pole system and the rotating pendulum,” Proc. 38th IEEE Conf. Decis. Control 2, 1174–1181 (1999).Google Scholar
Santibañez, V., Kelly, R. and Sandoval, J., “Control of the Inertia Wheel Pendulum by Bounded Torques,” Proceedings of the 44th IEEE Conference on Decision and Control (Seville, Spain, 2005) pp. 8266–8270.Google Scholar
Kim, S. and Jung, S. S., “Control experiment of a wheel-driven mobile inverted pendulum using neural network,” IEEE Trans. Control Syst. Technol. 16(2), 297–303 (2008).Google Scholar
George, S. and Feng, L., “Adaptive neural network control by adaptive interaction,” Proceedings of the American Control Conference (Arlington, VA, 2001) pp. 1247–1252.Google Scholar
Robert, D. B. and Feng, L., “Adaptive interaction and its application to neural networks,” Inf. Sci. 121(3), 201–215 (1999).Google Scholar
Kurode, S. and Asif, C., “Swing-Up and Stabilization of Rotary Inverted Pendulum using Sliding Modes,” International Federation of Accountan (Milano, 2011) pp. 10685–10690.Google Scholar