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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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 29; číslo 8; s. 3478 - 3489
Hlavní autoři: Zhao, Kai, Song, Yongduan, Ma, Tiedong, He, Liu
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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 with several salient features: 1) the control action is continuous and <inline-formula> <tex-math notation="LaTeX">\mathscr C^{1} </tex-math></inline-formula> smooth; 2) the full-state tracking error converges to a prescribed compact set around origin within a given finite time at a controllable rate of convergence that can be uniformly prespecified; 3) with Nussbaum gain in the loop, the tracking error further shrinks to zero as <inline-formula> <tex-math notation="LaTeX">t\to \infty </tex-math></inline-formula>; and 4) the neural network (NN) unit can be safely included in the loop during the entire system operational envelope without the danger of violating the compact set precondition imposed on the NN training inputs. Furthermore, by using the Lyapunov analysis, it is proven that all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded. The effectiveness and benefits of the proposed control method are validated via computer simulation.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2017.2727223