Skip to main content
Log in

Development and Implementation of a Wheeled Inverted Pendulum Vehicle Using Adaptive Neural Control with Extreme Learning Machines

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

A novel neural control on basis of extreme learning machines (ELMs) is proposed to control wheeled inverted pendulum vehicle, which is a human transportation platform mounted on two coaxial wheels. A dynamic self-balancing control scheme for such vehicle is constructed which depends on the single-hidden layer feedforward network approximation capability of combing ELMs to capture vehicle dynamics. It is superior to conventional intelligent control by using extreme learning machines since the proposed neural control adjusts the output weight parameters online on basis of the Lyapunov synthesis approach. Experimental results are provided to demonstrate that the vehicle can maintain upright posture stably with the external disturbances based on the proposed control scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Li Z, Yang C, Fan L. Advanced control of wheeled inverted pendulum systems. London: Springer; 2012.

    Google Scholar 

  2. Yang C, Li Z, Li J. Trajectory planning and optimized adaptive control for a class of wheeled iverted pendulum vehicle models. IEEE Trans Cybern. 2013;43(1):24–36.

    Article  Google Scholar 

  3. Yue M, Wei X, Li Z. Adaptive sliding mode control for two-wheeled inverted pendulum vehicle based on zero dynamics theory. Nonlinear Dyn. 2014;76(1):459–71.

    Article  Google Scholar 

  4. Jung S, Kim SS. Control experiment of a wheel-driven mobile inverted pendulum using neural network. IEEE Trans Control Syst Technol. 2008;16(2):297–303.

    Article  Google Scholar 

  5. Nasrallah DS, Michalska H, Angeles J. Controllability and posture control of a wheeled pendulum moving on an inclined plane. IEEE Trans Robot. 2007;23(3):564–77.

    Article  Google Scholar 

  6. Pathak K, Franch J, Agrawal SK. Velocity and position control of a wheeled inverted pendulum by partial feedback linearization. IEEE Trans Robot. 2005;21(3):505–13.

    Article  Google Scholar 

  7. Isidori A, Marconi L, Serrani A. Robust autonomous guidance: an internal model approach. New York: Springer; 2003.

    Book  Google Scholar 

  8. Grasser F, Arrigo A, Colombi S, Rufer AC. JOE: a mobile, inverted pendulum. IEEE Trans Ind Electron. 2002;49(1):107–14.

    Article  Google Scholar 

  9. Tsai C-C, Huang H-C, Lin S-C. Adaptive neural network control of a self-balancing two-wheeled scooter. IEEE Trans Ind Eletron. 2010;57(4):1420–8.

    Article  Google Scholar 

  10. Chiu C-H. The design and implementation of a wheeled inverted pendulum using an adaptive output recurrent cerebellar model articulation controller. IEEE Trans Ind Eletron. 2010;57(5):1814–22.

    Article  Google Scholar 

  11. Huang J, Ding F, Fukuda T, Matsuno T. Modeling and velocity control for a novel narrow vehicle based on mobile wheeled inverted pendulum. IEEE Trans Control Syst Technol. 2013;21(5):1607–17.

    Article  Google Scholar 

  12. Takei T, Imamura R, Yuta S. Baggage transportation and navigation by a wheeled inverted pendulum mobile robot. IEEE Trans Ind Eletron. 2009;56(10):3985–94.

    Article  Google Scholar 

  13. Yang C, Li Z, Cui R, Xu B. Neural network based motion control of under-actuated wheeled inverted pendulum models. IEEE Trans Neural Netw Learn Syst. 2014;25(11):2004–16.

    Article  PubMed  Google Scholar 

  14. Huang G, Chen L. Convex incremental extreme learning machine. Neurocomputing. 2007;70(16C18):3056–62.

    Article  Google Scholar 

  15. Feng G, Huang G, Lin Q, Gay R. Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans Neural Netw. 2009;20(8):1352–7.

    Article  PubMed  Google Scholar 

  16. Huang G, Ding X, Zhou H. Optimization method based extreme learning machine for classification. Neurocomputing. 2010;74(1):155–63.

    Article  Google Scholar 

  17. Huang G-B. An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput. 2014;6:376–90.

    Article  Google Scholar 

  18. Huang G-B, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern. 2012;42(2):513–29.

    Article  Google Scholar 

  19. Li Z, Zhang Y. Robust adaptive motion/force control for wheeled inverted pendulums. Automatica. 2010;46(8):1346–53.

    Article  Google Scholar 

  20. He W, Chen Y, Yin Z. Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Trans Cybern. 2015. doi:10.1109/TCYB.2015.2411285.

    Google Scholar 

  21. He W, Dong Y, Sun C. Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Trans Syst Man Cybern Syst. 2015. doi:10.1109/TSMC.2015.2429555.

    Google Scholar 

  22. Cui R, Guo J, Mao Z. Adaptive backstepping control of wheeled inverted pendulums models. Nonlinear Dyn. 2015;79(1):501–11.

    Article  Google Scholar 

  23. Liu Z, Wang F, Zhang Y, Chen X, Chen CLP. Adaptive fuzzy output-feedback controller design for nonlinear systems via backstepping and small-gain approach. IEEE Trans Cybern. 2014;44(10):1714–25.

    Article  PubMed  Google Scholar 

  24. Li Z, Xiao S, Ge SS, Su H. Constrained multi-legged robot system modeling and fuzzy control with uncertain kinematics and dynamics incorporating foot force optimization. IEEE Trans Syst Man Cybern Syst. 2015;45(11):1–13.

    Article  CAS  Google Scholar 

  25. Liu Z, Wang F, Zhang Y. Adaptive visual tracking control for manipulator with actuator fuzzy dead-zone constraint and unmodeled dynamic. IEEE Trans Syst Man Cybern Syst. 2015;45(10):1301–12.

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported in part by National Natural Science Foundation of China Grants 61174045 and 61573147, the Program for New Century Excellent Talents in University under Grant NCET-12-0195, Guangdong Science and Technology Research Collaborative Innovation Projects under Grant 2014B090901056, and the Ph.D. Programs Foundation of Ministry of Education of China under Grant 20130172110026, and Guangzhou Research Collaborative Innovation Projects (No. 2014Y2-00507), and National High-Tech Research and Development Program of China (863 Program) (Grant No. 2015AA042303).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhijun Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, J., Li, Z. Development and Implementation of a Wheeled Inverted Pendulum Vehicle Using Adaptive Neural Control with Extreme Learning Machines. Cogn Comput 7, 740–752 (2015). https://doi.org/10.1007/s12559-015-9363-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-015-9363-7

Keywords

Navigation