In:
Journal of Low Frequency Noise, Vibration and Active Control, SAGE Publications, Vol. 37, No. 1 ( 2018-03), p. 128-143
Abstract:
In this paper, the tracking control of periodic oscillations in an underactuated mechanical system is discussed. The proposed scheme is derived from the feedback linearization control technique and adaptive neural networks are used to estimate the unknown dynamics and to compensate uncertainties. The proposed neural network-based controller is applied to the Furuta pendulum, which is a nonlinear and nonminimum phase underactuated mechanical system with two degrees of freedom. The new neural network-based controller is experimentally compared with respect to its model-based version. Results indicated that the proposed neural algorithm performs better than the model-based controller, showing that the real-time adaptation of the neural network weights successfully estimates the unknown dynamics and compensates uncertainties in the experimental platform.
Type of Medium:
Online Resource
ISSN:
1461-3484
,
2048-4046
DOI:
10.1177/1461348417752988
Language:
English
Publisher:
SAGE Publications
Publication Date:
2018
detail.hit.zdb_id:
46883-6
detail.hit.zdb_id:
2025887-2