Abstract
In this paper, the problem of adaptive neural output feedback control is investigated for a class of uncertain nonlinear pure feedback systems with unknown backlash-like hysteresis. In the design, RBF neural networks are used to approximate the nonlinear functions of systems, and a neural state observer is designed to estimate the unmeasured states. By utilizing the neural state observer, and combining the backstepping technique with adaptive control design, an observer-based adaptive neural output feedback control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SUUB), and both observer error and tracking error can converge to a small neighborhood of the origin.
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Li, Y., Tong, S., Li, T. (2013). Observer-Based Adaptive Neural Networks Control of Nonlinear Pure Feedback Systems with Hysteresis. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_32
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DOI: https://doi.org/10.1007/978-3-642-39068-5_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39067-8
Online ISBN: 978-3-642-39068-5
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