✦ LIBER ✦

Robust adaptive neural network control of a class of uncertain strict-feedback nonlinear systems with unknown dead-zone and disturbances

✍ Scribed by Sun, Gang; Wang, Dan; Wang, Mingxin

Book ID: 124140010
Publisher: Elsevier Science
Year: 2014
Tongue: English
Weight: 609 KB
Volume: 145
Category: Article
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2014.05.039

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Robust adaptive control of nonlinear unc

Robust adaptive control of nonlinear uncertain plants with unknown dead-zone

✍ Zhou, J.; Shen, X.Z. 📂 Article 📅 2007 🏛 The Institution of Engineering and Technology 🌐 English ⚖ 141 KB

Single neural network approximation base

Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems

✍ Gang Sun, Dan Wang, Tieshan Li, Zhouhua Peng, Hao Wang 📂 Article 📅 2012 🏛 Springer Netherlands 🌐 English ⚖ 541 KB

Robust adaptive neural control for a cla

Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems

✍ Ge, S.S.; Jing Wang, 📂 Article 📅 2002 🏛 IEEE 🌐 English ⚖ 899 KB

Adaptive robust control based on single

Adaptive robust control based on single neural network approximation for a class of uncertain strict-feedback discrete-time nonlinear systems

✍ Wang, Xin; Li, Tieshan; Chen, C.L. Philip; Lin, Bin 📂 Article 📅 2014 🏛 Elsevier Science 🌐 English ⚖ 610 KB

Adaptive Fuzzy Output Feedback Tracking

Adaptive Fuzzy Output Feedback Tracking Backstepping Control of Strict-Feedback Nonlinear Systems With Unknown Dead Zones

✍ Shaocheng Tong; Yongming Li 📂 Article 📅 2012 🏛 IEEE 🌐 English ⚖ 664 KB

Observer-based adaptive fuzzy-neural con

Observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems with unknown dead-zone input

✍ Yan-Jun Liu; Ning Zhou 📂 Article 📅 2010 🏛 Elsevier Science 🌐 English ⚖ 569 KB

Based on the universal approximation property of the fuzzy-neural networks, an adaptive fuzzy-neural observer design algorithm is studied for a class of nonlinear SISO systems with both a completely unknown function and an unknown dead-zone input. The fuzzy-neural networks are used to approximate th