Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances

Liu, P., Yu, H. and Cang, S. ORCID: 0000-0002-7984-0728, 2019. Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances. Nonlinear Dynamics, 98, pp. 1447-1464. ISSN 0924-090X

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This paper studies neural network-based tracking control of underactuated systems with unknown parameters and with matched and mismatched disturbances. Novel adaptive control schemes are proposed with the utilization of multi-layer neural networks, adaptive control and variable structure strategies to cope with the uncertainties containing approximation errors, unknown base parameters and time-varying matched and mismatched external disturbances. Novel auxiliary control variables are designed to establish the controllability of the non-collocated subset of the underactuated systems. The approximation errors and the matched and mismatched external disturbances are efficiently counteracted by appropriate design of robust compensators. Stability and convergence of the time-varying reference trajectory are shown in the sense of Lyapunov. The parameter updating laws for the designed control schemes are derived using the projection approach to reduce the tracking error as small as desired. Unknown dynamics of the non-collocated subset is approximated through neural networks within a local region. Finally, simulation studies on an underactuated manipulator and an underactuated vibro-driven system are conducted to verify the effectiveness of the proposed control schemes.

Item Type: Journal article
Publication Title: Nonlinear Dynamics
Creators: Liu, P., Yu, H. and Cang, S.
Publisher: Springer Science and Business Media LLC
Date: October 2019
Volume: 98
ISSN: 0924-090X
Rights: © The Author(s) 2019. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 27 Aug 2020 07:50
Last Modified: 31 May 2021 15:17

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