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Machine Intelligence Research

Erschienen in:

01.06.2013

Adaptive Tracking Control of Uncertain MIMO Nonlinear Systems with Time- varying Delays and Unmodeled Dynamics

verfasst von: Xiao-Cheng Shi, Tian-Ping Zhang

Erschienen in: Machine Intelligence Research | Ausgabe 3/2013

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Abstract

In this paper, adaptive neural tracking control is proposed based on radial basis function neural networks (RBFNNs) for a class of multi-input multi-output (MIMO) nonlinear systems with completely unknown control directions, unknown dynamic disturbances, unmodeled dynamics, and uncertainties with time-varying delay. Using the Nussbaum function properties, the unknown control directions are dealt with. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown upper bound functions of the time-varying delay uncertainties are compensated. The proposed control scheme does not need to calculate the integral of the delayed state functions. Using Young’s inequality and RBFNNs, the assumption of unmodeled dynamics is relaxed. By theoretical analysis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded.

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Titel: Adaptive Tracking Control of Uncertain MIMO Nonlinear Systems with Time- varying Delays and Unmodeled Dynamics
verfasst von: Xiao-Cheng Shi
Tian-Ping Zhang
Publikationsdatum: 01.06.2013
Verlag: Springer-Verlag
Erschienen in: Machine Intelligence Research / Ausgabe 3/2013
Print ISSN: 2731-538X
Elektronische ISSN: 2731-5398
DOI: https://doi.org/10.1007/s11633-013-0712-2

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