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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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 29; H. 8; S. 3478 - 3489
Hauptverfasser:	Zhao, Kai, Song, Yongduan, Ma, Tiedong, He, Liu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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