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Neural network-based motion control of an underactuated wheeled inverted pendulum model

Yang, Chenguang; Li, Zhijun; Cui, Rongxin; Xu, Bugong

Authors

Zhijun Li

Rongxin Cui

Bugong Xu



Abstract

In this paper, automatic motion control is investigated for one of wheeled inverted pendulum (WIP) models, which have been widely applied for modeling of a large range of two wheeled modern vehicles. First, the underactuated WIP model is decomposed into a fully actuated second order subsystem Σa consisting of planar movement of vehicle forward and yaw angular motions, and a nonactuated first order subsystem Σb of pendulum motion. Due to the unknown dynamics of subsystem Σa and the universal approximation ability of neural network (NN), an adaptive NN scheme has been employed for motion control of subsystem Σa. The model reference approach has been used whereas the reference model is optimized by the finite time linear quadratic regulation technique. The pendulum motion in the passive subsystem Σb is indirectly controlled using the dynamic coupling with planar forward motion of subsystem Σa , such that satisfactory tracking of a set pendulum tilt angle can be guaranteed. Rigours theoretic analysis has been established, and simulation studies have been performed to demonstrate the developed method.

Citation

Yang, C., Li, Z., Cui, R., & Xu, B. (2014). Neural network-based motion control of an underactuated wheeled inverted pendulum model. IEEE Transactions on Neural Networks and Learning Systems, 25(11), 2004-2016. https://doi.org/10.1109/TNNLS.2014.2302475

Journal Article Type Article
Acceptance Date Jan 12, 2014
Online Publication Date Mar 11, 2014
Publication Date Nov 1, 2014
Deposit Date Oct 8, 2019
Journal IEEE Transactions on Neural Networks and Learning Systems
Print ISSN 2162-237X
Electronic ISSN 2162-2388
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 25
Issue 11
Pages 2004-2016
DOI https://doi.org/10.1109/TNNLS.2014.2302475
Keywords Vehicle dynamics , Vehicles , Dynamics , Artificial neural networks , Vectors , Wheels , Robots
Public URL https://uwe-repository.worktribe.com/output/3596879
Publisher URL https://ieeexplore.ieee.org/document/6762995