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Cited by 330

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Adaptive Neural Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis

Huanqing Wang, Bing Chen, Kefu Liu, Xiaoping Liu, Chong Lin·2014·IEEE Transactions on Neural Networks and Learning Systems

Abstract

This paper considers the problem of adaptive neural control of stochastic nonlinear systems in nonstrict-feedback form with unknown backlash-like hysteresis nonlinearities. To overcome the design difficulty of nonstrict-feedback structure, variable separation technique is used to decompose the unknown functions of all state variables into a sum of smooth functions of each error dynamic. By combining radial basis function neural networks' universal approximation capability with an adaptive backstepping technique, an adaptive neural control algorithm is proposed. It is shown that the proposed controller guarantees that all the signals in the closed-loop system are four-moment semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in the sense of mean quartic value. Simulation results further show the effectiveness of the presented control scheme.

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Metadata

Authors
Wang, H., Chen, B., Liu, K., Liu, X., & Lin, C.
Venue
IEEE Transactions on Neural Networks and Learning Systems
Published
2014-05
Volume
25
Issue
5
Pages
947-958
ISSN
  • 2162-237X (ISSN-L)
  • 2162-2388
OpenAlex
W1983294924
Cited by
330
References
25
FWCI
29.81
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Adaptive Neural Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis. https://app.cochat.ai/papers/10.1109/tnnls.2013.2283879
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Wang, H., Chen, B., Liu, K., Liu, X., & Lin, C. (2014). Adaptive Neural Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2013.2283879

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