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.
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Li Z, Yang C, Fan L. Advanced control of wheeled inverted pendulum systems. London: Springer; 2012.
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.
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.
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.
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.
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.
Isidori A, Marconi L, Serrani A. Robust autonomous guidance: an internal model approach. New York: Springer; 2003.
Grasser F, Arrigo A, Colombi S, Rufer AC. JOE: a mobile, inverted pendulum. IEEE Trans Ind Electron. 2002;49(1):107–14.
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.
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.
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.
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.
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.
Huang G, Chen L. Convex incremental extreme learning machine. Neurocomputing. 2007;70(16C18):3056–62.
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.
Huang G, Ding X, Zhou H. Optimization method based extreme learning machine for classification. Neurocomputing. 2010;74(1):155–63.
Huang G-B. An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput. 2014;6:376–90.
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.
Li Z, Zhang Y. Robust adaptive motion/force control for wheeled inverted pendulums. Automatica. 2010;46(8):1346–53.
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.
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.
Cui R, Guo J, Mao Z. Adaptive backstepping control of wheeled inverted pendulums models. Nonlinear Dyn. 2015;79(1):501–11.
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.
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.
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.
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).
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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
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DOI: https://doi.org/10.1007/s12559-015-9363-7