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Cited by 685

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems

Zhongsheng Hou, Shangtai Jin·2011·IEEE Transactions on Neural Networks

Abstract

In this paper, a data-driven model-free adaptive control (MFAC) approach is proposed based on a new dynamic linearization technique (DLT) with a novel concept called pseudo-partial derivative for a class of general multiple-input and multiple-output nonlinear discrete-time systems. The DLT includes compact form dynamic linearization, partial form dynamic linearization, and full form dynamic linearization. The main feature of the approach is that the controller design depends only on the measured input/output data of the controlled plant. Analysis and extensive simulations have shown that MFAC guarantees the bounded-input bounded-output stability and the tracking error convergence.

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Crossref · 10.1109/tnn.2011.2176141

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Metadata

Authors
Hou, Z., & Jin, S.
Venue
IEEE Transactions on Neural Networks
Published
2011-12
Volume
22
Issue
12
Pages
2173-2188
ISSN
  • 1045-9227 (ISSN-L)
  • 1941-0093
OpenAlex
W2068202815
Cited by
685
References
32
FWCI
18.89
Type
journal article
Language
en
Open access
closed

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Hou, Z., & Jin, S. (2011). Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems. IEEE Transactions on Neural Networks. https://doi.org/10.1109/tnn.2011.2176141

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