Prescribed Performance Control of Uncertain Euler-Lagrange Systems Subject to Full-State Constraints

This paper studies the zero-error tracking control problem of Euler-Lagrange systems subject to full-state constraints and nonparametric uncertainties. By blending an error transformation with barrier Lyapunov function, a neural adaptive tracking control scheme is developed, resulting in a solution...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 29; číslo 8; s. 3478 - 3489
Hlavní autori:	Zhao, Kai, Song, Yongduan, Ma, Tiedong, He, Liu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptive control Artificial neural networks Barrier Lyapunov function (BLF) Computer simulation Convergence Error analysis error transformation Hazards Liapunov functions Lyapunov methods Neural networks Nussbaum gain technique prescribed tracking performance robust adaptive neural control Robustness Stability System effectiveness Tracking control Uncertainty
ISSN:	2162-237X, 2162-2388, 2162-2388
On-line prístup:	Získať plný text
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