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

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 8; pp. 3478 - 3489
• Main Authors: Zhao, Kai, Song, Yongduan, Ma, Tiedong, He, Liu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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

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...