Adaptive Neural Network Finite-Time Output Feedback Control of Quantized Nonlinear Systems

By Fang Wang, Bing Chen, Chong Lin, Jing Zhang, Xinzhu Meng. IEEE Transactions on Cybernetics, 2018

407 citations recorded by the source corpus.

This paper addresses the finite-time tracking issue for nonlinear quantized systems with unmeasurable states. Compared with the existing researches, the finite-time quantized feedback control is considered for the first time. By proposing a new finite-time stability criterion and designing a state observer, a novel adaptive neural output-feedback control strategy is raised by backstepping technique. Under the presented control scheme, the finite-time quantized feedback control problem is coped with without limiting assumption for nonlinear functions.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/5a527be469717b0f72306a6b9fd591e99534ee36

How researchers see this paper

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In Computational Theory and Mathematics

1. Shaocheng Tong
2. Yong-ming Li
3. Kewen Li
4. Shuzhi Sam Ge
5. Mou Chen
6. Yanjun Liu
7. Tong Wang
8. Yuanxin Li

In Control and Systems Engineering

1. Shaocheng Tong
2. Yong-ming Li
3. Kewen Li
4. Yanjun Liu
5. Shuzhi Sam Ge
6. Mou Chen
7. Hongyi Li
8. Peng Shi

Citations in the corpus

• Finite-Time Adaptive Fuzzy Output Feedback Dynamic Surface Control for MIMO Nonstrict Feedback Systems (2019, background): “([43], [44]): Consider differential equation ̇̂ ς(t) = −γς̂(t) + kv(t), where γ > 0 and k > 0 are constants, and v(t) is a positive function.”