Regular Papers

International Journal of Control, Automation and Systems 2018; 16(4): 2002-2010

https://doi.org/10.1007/s12555-017-0564-6

© The International Journal of Control, Automation, and Systems

Integral Barrier Lyapunov Functions-based Neural Control for Strictfeedback Nonlinear Systems with Multi-constraint

Jun Zhang

Jiangsu University

Abstract

A new robust tracking control approach is proposed for strict-feedback nonlinear systems with state and input constraints. The constraints are tackled by extending the control input as an extended state and introducing an integral barrier Lyapunov function (IBLF) to each step in a backstepping procedure. This extends current research on barrier Lyapunov functions(BLFs)-based control for nonlinear systems with state constraints to IBLF-based control for strict-feedback nonlinear systems with state and input constraints. Since the IBLF allows the original constraints to be mixed with the error terms, the use of IBLF decreases conservatism in barrier Lyapunov functionsbased control. In the backstepping procedure, neural networks (NNs) with projection modifications are applied to estimate system uncertainties, due to their ability in guaranteeing estimators in a given bounded area. To facilitate the use of the once-differentiable NNs estimators in the backstepping procedure, the virtual controllers are passed through command filters. Finally, simulation results are presented to illustrate the feasibility and effectiveness of the proposed control."

Keywords Barrier Lyapunov function, dynamic surface control, input saturation, neural networks, state constraints, strict-feedback nonlinear system.

Article

Regular Papers

International Journal of Control, Automation and Systems 2018; 16(4): 2002-2010

Published online August 1, 2018 https://doi.org/10.1007/s12555-017-0564-6

Copyright © The International Journal of Control, Automation, and Systems.

Integral Barrier Lyapunov Functions-based Neural Control for Strictfeedback Nonlinear Systems with Multi-constraint

Jun Zhang

Jiangsu University

Abstract

A new robust tracking control approach is proposed for strict-feedback nonlinear systems with state and input constraints. The constraints are tackled by extending the control input as an extended state and introducing an integral barrier Lyapunov function (IBLF) to each step in a backstepping procedure. This extends current research on barrier Lyapunov functions(BLFs)-based control for nonlinear systems with state constraints to IBLF-based control for strict-feedback nonlinear systems with state and input constraints. Since the IBLF allows the original constraints to be mixed with the error terms, the use of IBLF decreases conservatism in barrier Lyapunov functionsbased control. In the backstepping procedure, neural networks (NNs) with projection modifications are applied to estimate system uncertainties, due to their ability in guaranteeing estimators in a given bounded area. To facilitate the use of the once-differentiable NNs estimators in the backstepping procedure, the virtual controllers are passed through command filters. Finally, simulation results are presented to illustrate the feasibility and effectiveness of the proposed control."

Keywords: Barrier Lyapunov function, dynamic surface control, input saturation, neural networks, state constraints, strict-feedback nonlinear system.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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