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Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model

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

IEEE transaction on neural networks and learning systems, 2014-11, Vol.25 (11), p.2004-2016

United States: IEEE

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Título:
Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model
Autor: Yang, Chenguang ; Li, Zhijun ; Cui, Rongxin ; Xu, Bugong
Assuntos: Algorithms ; Artificial neural networks ; Computer Simulation ; Dynamics ; Finite time linear quadratic regulator (LQR) ; Humans ; Models, Theoretical ; Motion ; Motion control ; neural network (NN) ; Neural networks ; Neural Networks (Computer) ; Nonlinear Dynamics ; Robots ; Time Factors ; underactuated ; Vectors ; Vehicle dynamics ; Vehicles ; wheeled inverted pendulum (WIP) ; Wheels
É parte de: IEEE transaction on neural networks and learning systems, 2014-11, Vol.25 (11), p.2004-2016
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: In this paper, automatic motion control is investigated for 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 Σ a consisting of planar movement of vehicle forward motion and yaw angular motions, and a passive (nonactuated) first-order subsystem Σ b of pendulum tilt motion. Due to the unknown dynamics of subsystem Σ a and universal approximation ability of neural network (NN), an adaptive NN scheme has been employed for motion control of subsystem Σ a . Model reference approach has been used, whereas the reference model is optimized by finite time linear quadratic regulation technique. Inspired by human control strategy of inverted pendulum, the tilt angular motion in the passive subsystem Σ b has been indirectly controlled using the dynamic coupling with planar forward motion of subsystem Σ a , such that the satisfactory tracking of set tilt angle can be guaranteed. Rigorous theoretic analysis has been established, and simulation studies have been performed to demonstrate the developed method.
Editor: United States: IEEE
Idioma: Inglês

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Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model

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

United States: IEEE

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