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Neural Computing and Applications

Erschienen in:

01.11.2010 | Original Article

Self-tuning output recurrent cerebellar model articulation controller for a wheeled inverted pendulum control

verfasst von: Chih-Hui Chiu

Erschienen in: Neural Computing and Applications | Ausgabe 8/2010

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Abstract

In this study, a model-free self-tuning output recurrent cerebellar model articulation controller (SORCMAC) is investigated to control a wheeled inverted pendulum (WIP). Since the proposed SORCMAC captures the system dynamics, it has superior capability compared to the conventional cerebellar model articulation controller in terms of an efficient learning mechanism and dynamic response. The dynamic gradient descent method is also adopted to adjust the SORCMAC parameters online. Moreover, an analytical method based on a Lyapunov function is proposed to determine the learning rates of the SORCMAC so that the convergence of the system can be guaranteed. Finally, the effectiveness of the proposed control system is verified by simulations of the WIP control. Simulation results show that the WIP can move forward and backward stably with uncertainty disturbance by using the proposed SORCMAC.

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Titel: Self-tuning output recurrent cerebellar model articulation controller for a wheeled inverted pendulum control
verfasst von: Chih-Hui Chiu
Publikationsdatum: 01.11.2010
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 8/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0335-2

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